From d84470f7f77fa24bc70f46fc0ac3c4c659172683 Mon Sep 17 00:00:00 2001
From: Mohsen <marzbanm7@gmail.com>
Date: Tue, 27 Oct 2026 14:13:32 +0100
Subject: [PATCH] Initial Commit

---
 .gitignore                                    |  24 +
 .vscode/extensions.json                       |   9 +
 .vscode/launch.json                           | 334 ++++++
 .vscode/settings.json                         |  11 +
 API/3a_stations_sensors.json                  | 965 ++++++++++++++++++
 API/fetch_3a.py                               | 493 +++++++++
 CLAUDE.md                                     |  11 +
 Diagram.png                                   | Bin 0 -> 576805 bytes
 docs/ImplementazioneAlgoritmiSensori.pdf      | Bin 0 -> 444470 bytes
 pyproject.toml                                |  27 +
 src/damage_engine/CV_models/__init__.py       |   0
 .../CV_models/brina_gelo_snow.py              |   0
 src/damage_engine/CV_models/drought.py        |   0
 src/damage_engine/CV_models/flood.py          |   0
 src/damage_engine/CV_models/hail.py           |   0
 src/damage_engine/CV_models/strong_wind.py    |   0
 src/damage_engine/__init__.py                 |   0
 src/damage_engine/detectors/__init__.py       |   0
 src/damage_engine/detectors/brina.py          | 378 +++++++
 src/damage_engine/detectors/drought.py        | 395 +++++++
 src/damage_engine/detectors/flood.py          | 365 +++++++
 src/damage_engine/detectors/freezing.py       | 404 ++++++++
 src/damage_engine/detectors/hail.py           | 587 +++++++++++
 .../detectors/high_temperature.py             | 407 ++++++++
 src/damage_engine/detectors/hot_wind.py       | 359 +++++++
 src/damage_engine/detectors/snow.py           | 424 ++++++++
 src/damage_engine/detectors/strong_wind.py    | 310 ++++++
 src/damage_engine/event_merge.py              |   0
 src/damage_engine/hail-snow/__init__.py       |   0
 src/damage_engine/hail-snow/hail_snow.py      |   0
 src/damage_engine/main.py                     | 258 +++++
 src/damage_engine/report/__init__.py          |   0
 src/damage_engine/report/assets/3a_logo.png   | Bin 0 -> 12385 bytes
 .../report/assets/fonts/DejaVu-LICENSE.txt    | 187 ++++
 .../report/assets/fonts/DejaVuSans.ttf        | Bin 0 -> 757076 bytes
 .../report/assets/fonts/NotoSans-Bold.ttf     | Bin 0 -> 631484 bytes
 .../report/assets/fonts/NotoSans-Italic.ttf   | Bin 0 -> 639124 bytes
 .../report/assets/fonts/NotoSans-OFL.txt      |  93 ++
 .../report/assets/fonts/NotoSans-Regular.ttf  | Bin 0 -> 621572 bytes
 src/damage_engine/report/report.py            | 301 ++++++
 src/damage_engine/report/station_pdf.py       | 478 +++++++++
 src/damage_engine/report/station_report.py    | 900 ++++++++++++++++
 src/damage_engine/schemas.py                  | 107 ++
 src/damage_engine/severity.py                 |  43 +
 src/damage_engine/sources/__init__.py         |   0
 src/damage_engine/sources/derived.py          | 108 ++
 src/damage_engine/sources/fields.py           |  53 +
 src/damage_engine/sources/stations.py         |  62 ++
 src/damage_engine/sources/threea.py           | 242 +++++
 src/damage_engine/thresholds.py               | 574 +++++++++++
 tests/__init__.py                             |   0
 tests/test_brina.py                           | 149 +++
 tests/test_derived.py                         |  59 ++
 tests/test_drought.py                         | 141 +++
 tests/test_flood.py                           | 189 ++++
 tests/test_freezing.py                        | 154 +++
 tests/test_hail.py                            | 290 ++++++
 tests/test_high_temperature.py                | 250 +++++
 tests/test_hot_wind.py                        | 194 ++++
 tests/test_pipeline.py                        | 182 ++++
 tests/test_report.py                          | 106 ++
 tests/test_snow.py                            | 155 +++
 tests/test_station_report.py                  | 507 +++++++++
 tests/test_strong_wind.py                     | 160 +++
 tests/test_threea.py                          |  38 +
 65 files changed, 11483 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 .vscode/extensions.json
 create mode 100644 .vscode/launch.json
 create mode 100644 .vscode/settings.json
 create mode 100644 API/3a_stations_sensors.json
 create mode 100644 API/fetch_3a.py
 create mode 100644 CLAUDE.md
 create mode 100644 Diagram.png
 create mode 100644 docs/ImplementazioneAlgoritmiSensori.pdf
 create mode 100644 pyproject.toml
 create mode 100644 src/damage_engine/CV_models/__init__.py
 create mode 100644 src/damage_engine/CV_models/brina_gelo_snow.py
 create mode 100644 src/damage_engine/CV_models/drought.py
 create mode 100644 src/damage_engine/CV_models/flood.py
 create mode 100644 src/damage_engine/CV_models/hail.py
 create mode 100644 src/damage_engine/CV_models/strong_wind.py
 create mode 100644 src/damage_engine/__init__.py
 create mode 100644 src/damage_engine/detectors/__init__.py
 create mode 100644 src/damage_engine/detectors/brina.py
 create mode 100644 src/damage_engine/detectors/drought.py
 create mode 100644 src/damage_engine/detectors/flood.py
 create mode 100644 src/damage_engine/detectors/freezing.py
 create mode 100644 src/damage_engine/detectors/hail.py
 create mode 100644 src/damage_engine/detectors/high_temperature.py
 create mode 100644 src/damage_engine/detectors/hot_wind.py
 create mode 100644 src/damage_engine/detectors/snow.py
 create mode 100644 src/damage_engine/detectors/strong_wind.py
 create mode 100644 src/damage_engine/event_merge.py
 create mode 100644 src/damage_engine/hail-snow/__init__.py
 create mode 100644 src/damage_engine/hail-snow/hail_snow.py
 create mode 100644 src/damage_engine/main.py
 create mode 100644 src/damage_engine/report/__init__.py
 create mode 100644 src/damage_engine/report/assets/3a_logo.png
 create mode 100644 src/damage_engine/report/assets/fonts/DejaVu-LICENSE.txt
 create mode 100644 src/damage_engine/report/assets/fonts/DejaVuSans.ttf
 create mode 100644 src/damage_engine/report/assets/fonts/NotoSans-Bold.ttf
 create mode 100644 src/damage_engine/report/assets/fonts/NotoSans-Italic.ttf
 create mode 100644 src/damage_engine/report/assets/fonts/NotoSans-OFL.txt
 create mode 100644 src/damage_engine/report/assets/fonts/NotoSans-Regular.ttf
 create mode 100644 src/damage_engine/report/report.py
 create mode 100644 src/damage_engine/report/station_pdf.py
 create mode 100644 src/damage_engine/report/station_report.py
 create mode 100644 src/damage_engine/schemas.py
 create mode 100644 src/damage_engine/severity.py
 create mode 100644 src/damage_engine/sources/__init__.py
 create mode 100644 src/damage_engine/sources/derived.py
 create mode 100644 src/damage_engine/sources/fields.py
 create mode 100644 src/damage_engine/sources/stations.py
 create mode 100644 src/damage_engine/sources/threea.py
 create mode 100644 src/damage_engine/thresholds.py
 create mode 100644 tests/__init__.py
 create mode 100644 tests/test_brina.py
 create mode 100644 tests/test_derived.py
 create mode 100644 tests/test_drought.py
 create mode 100644 tests/test_flood.py
 create mode 100644 tests/test_freezing.py
 create mode 100644 tests/test_hail.py
 create mode 100644 tests/test_high_temperature.py
 create mode 100644 tests/test_hot_wind.py
 create mode 100644 tests/test_pipeline.py
 create mode 100644 tests/test_report.py
 create mode 100644 tests/test_snow.py
 create mode 100644 tests/test_station_report.py
 create mode 100644 tests/test_strong_wind.py
 create mode 100644 tests/test_threea.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..20a4a40
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,24 @@
+# --- PRIVATE: 3A credentials, never commit ---
+API/3a_credentials.json
+API/mohsen_new_api.html
+.env
+*.env
+
+# --- cached 3A login token + local farmer fields ---
+data/
+
+# --- generated reports (station data, rebuilt by each run) ---
+outputs/
+
+# --- Python / tools ---
+.venv/
+__pycache__/
+*.py[cod]
+*.egg-info/
+.pytest_cache/
+build/
+dist/
+
+# --- kept local only (your choice) ---
+README.md
+how to RUN.txt
diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 0000000..c707295
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,9 @@
+{
+  "recommendations": [
+    "ms-python.python",
+    "ms-python.vscode-pylance",
+    "ms-python.debugpy",
+    "ms-toolsai.jupyter",
+    "redhat.vscode-yaml"
+  ]
+}
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..4116d43
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,334 @@
+{
+  // Run and Debug (F5): pick a configuration in the Run and Debug panel.
+  // Every entry runs with -m (module), so the damage_engine imports work.
+  // Live entries use the 3A API (5 requests / 15 min): don't repeat them quickly.
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Pipeline: run every block + client reports (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Pipeline: run every block + client reports for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Brina: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "brina"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Brina: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "brina", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Brina: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.brina",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Flood: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "flood"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Flood: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "flood", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Flood: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.flood",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Freezing: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "freezing"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Freezing: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "freezing", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Freezing: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.freezing",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Strong wind: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "strong_wind"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Strong wind: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "strong_wind", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Strong wind: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.strong_wind",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hot wind: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "hot_wind"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hot wind: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "hot_wind", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hot wind: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.hot_wind",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "High temperature: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "high_temperature"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "High temperature: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "high_temperature", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "High temperature: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.high_temperature",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Snow: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "snow"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Snow: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "snow", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Snow: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.snow",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hail: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "hail"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hail: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "hail", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hail: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.hail",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hail: daytime check 13:00 today + save (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--hail-daytime", "13"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Hail: daytime check 19:00 today + save (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--hail-daytime", "19"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Drought: run + save report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "drought"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Drought: run + save report for a date (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--once", "drought", "--date", "${input:runDay}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Drought: print only, no report (live API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.detectors.drought",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Scheduler: keep running (whole pipeline every day at 07:05)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.main",
+      "args": ["--serve"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Report: choose a field (no API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.report.report",
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Report: all fields, block by block (no API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.report.report",
+      "args": ["--all"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Station report: JSON + PDF for one station (no API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "damage_engine.report.station_report",
+      "args": ["--station", "${input:stationId}"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    },
+    {
+      "name": "Unit tests (no API)",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "pytest",
+      "args": ["tests", "-q"],
+      "cwd": "${workspaceFolder}",
+      "console": "integratedTerminal"
+    }
+  ],
+  "inputs": [
+    {
+      "id": "runDay",
+      "type": "promptString",
+      "description": "Run day (YYYY-MM-DD, within the last ~180 days)"
+    },
+    {
+      "id": "stationId",
+      "type": "promptString",
+      "description": "3A station id (e.g. 566)",
+      "default": "566"
+    }
+  ]
+}
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..8741395
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,11 @@
+{
+  "python.defaultInterpreterPath": "${workspaceFolder}/.venv/Scripts/python.exe",
+  "python.terminal.activateEnvironment": true,
+  "python.analysis.extraPaths": ["${workspaceFolder}/src"],
+  "python.testing.pytestEnabled": true,
+  "python.testing.pytestArgs": ["tests"],
+  "files.exclude": {
+    "**/__pycache__": true,
+    "**/*.egg-info": true
+  }
+}
diff --git a/API/3a_stations_sensors.json b/API/3a_stations_sensors.json
new file mode 100644
index 0000000..7c6103f
--- /dev/null
+++ b/API/3a_stations_sensors.json
@@ -0,0 +1,965 @@
+{
+  "source": "https://api.3acloud.it (getAvailableStations, getAvailableStationHourlySensors, getAvailableStationDailySensors)",
+  "checked": "2026-10-21",
+  "note": "Sensors = HOURLY sensors (what the blocks fetch). DailyOnlyParameters = columns the station offers only in daily data (/getWeatherDaily). Units from /getAvailableHourlyColumns.",
+  "NumStations": 14,
+  "Stations": [
+    {
+      "StationId": 3,
+      "StationName": "Lagnasco",
+      "Municipality": "Lagnasco",
+      "Latitude_WGS84": "44.634745",
+      "Longitude_WGS84": "7.581672",
+      "Elevation_m": "335",
+      "TimeZone": "UTC+1",
+      "NumSensors": 6,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
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+      "StationName": "Castellar",
+      "Municipality": "Castellar",
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+      "Elevation_m": "360",
+      "TimeZone": "UTC+1",
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+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 6,
+      "StationName": "Cavour",
+      "Municipality": "Cavour",
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+      "Longitude_WGS84": "7.370115",
+      "Elevation_m": "307",
+      "TimeZone": "UTC+1",
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+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
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+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 7,
+      "StationName": "CostigliolediSaluzzo",
+      "Municipality": "CostigliolediSaluzzo",
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+      "Longitude_WGS84": "7.465652",
+      "Elevation_m": "470",
+      "TimeZone": "UTC+1",
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+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
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+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 9,
+      "StationName": "Centallo",
+      "Municipality": "Centallo",
+      "Latitude_WGS84": "44.5001",
+      "Longitude_WGS84": "7.61836",
+      "Elevation_m": "413",
+      "TimeZone": "UTC+1",
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+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
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+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 10,
+      "StationName": "Revello",
+      "Municipality": "Revello",
+      "Latitude_WGS84": "44.6728",
+      "Longitude_WGS84": "7.3906",
+      "Elevation_m": "316",
+      "TimeZone": "UTC+1",
+      "NumSensors": 6,
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+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        }
+      ],
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+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 12,
+      "StationName": "Saluzzo",
+      "Municipality": "Saluzzo",
+      "Latitude_WGS84": "44.6513",
+      "Longitude_WGS84": "7.45032",
+      "Elevation_m": "350",
+      "TimeZone": "UTC+1",
+      "NumSensors": 4,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "BagnaturaFogliare",
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 30,
+      "StationName": "FossanoMaddalene",
+      "Municipality": "Fossano",
+      "Latitude_WGS84": "44.502946",
+      "Longitude_WGS84": "7.656242",
+      "Elevation_m": "397",
+      "TimeZone": "UTC+1",
+      "NumSensors": 6,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 145,
+      "StationName": "Falicetto_viaPomarolo",
+      "Municipality": "Falicetto",
+      "Latitude_WGS84": "44.5922",
+      "Longitude_WGS84": "7.5342",
+      "Elevation_m": "360",
+      "TimeZone": "UTC+1",
+      "NumSensors": 5,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 153,
+      "StationName": "Caraglio",
+      "Municipality": "Caraglio",
+      "Latitude_WGS84": "44.4379",
+      "Longitude_WGS84": "7.45428",
+      "Elevation_m": "575",
+      "TimeZone": "UTC+1",
+      "NumSensors": 6,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "TemperaturaTerreno",
+          "name": "Soil temperature sensor 1",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaTerrenoMedia"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot"
+      ]
+    },
+    {
+      "StationId": 293,
+      "StationName": "CuneoSanBenigno",
+      "Municipality": "Cuneo",
+      "Latitude_WGS84": "44.4690930",
+      "Longitude_WGS84": "7.5447420",
+      "Elevation_m": "460",
+      "TimeZone": "UTC+1",
+      "NumSensors": 6,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia",
+            "RainRateMax"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "Pressione",
+          "name": "Barometric pressure sensor",
+          "unit": "mbar",
+          "api_parameters": [
+            "PressioneMax",
+            "PressioneMed",
+            "PressioneMin"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot",
+        "RadNettaInt",
+        "RadiazioneSolareInt"
+      ]
+    },
+    {
+      "StationId": 544,
+      "StationName": "RevelloSanPietro",
+      "Municipality": "Revello",
+      "Latitude_WGS84": "44.636467",
+      "Longitude_WGS84": "7.395118",
+      "Elevation_m": "300",
+      "TimeZone": "UTC+1",
+      "NumSensors": 8,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        },
+        {
+          "sensor": "RadiazioneSolare",
+          "name": "Solar radiation sensor (pyranometer, irradiance)",
+          "unit": "W/m²",
+          "api_parameters": [
+            "RadiazioneSolareMax",
+            "RadiazioneSolareMin"
+          ]
+        },
+        {
+          "sensor": "Suolo1",
+          "name": "Soil moisture sensor 1",
+          "unit": "%",
+          "api_parameters": [
+            "Suolo1"
+          ]
+        },
+        {
+          "sensor": "Suolo2",
+          "name": "Soil moisture sensor 2",
+          "unit": "%",
+          "api_parameters": [
+            "Suolo2"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot",
+        "RadiazioneSolareInt"
+      ]
+    },
+    {
+      "StationId": 566,
+      "StationName": "CuneoGiordano",
+      "Municipality": "Cuneo",
+      "Latitude_WGS84": "44.450417",
+      "Longitude_WGS84": "7.544556",
+      "Elevation_m": "460",
+      "TimeZone": "UTC+1",
+      "NumSensors": 7,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "TBulb",
+          "name": "Wet-bulb temperature (mean, supplied by 3A)",
+          "unit": "°C",
+          "api_parameters": [
+            "TBulbMed"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        },
+        {
+          "sensor": "RadiazioneSolare",
+          "name": "Solar radiation sensor (pyranometer, irradiance)",
+          "unit": "W/m²",
+          "api_parameters": [
+            "RadiazioneSolareMax",
+            "RadiazioneSolareMin"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot",
+        "RadiazioneSolareInt"
+      ]
+    },
+    {
+      "StationId": 600,
+      "StationName": "CascinaFrati",
+      "Municipality": "Revello",
+      "Latitude_WGS84": "44.668629",
+      "Longitude_WGS84": "7.379682",
+      "Elevation_m": "0",
+      "TimeZone": "",
+      "NumSensors": 10,
+      "Sensors": [
+        {
+          "sensor": "TemperaturaAria",
+          "name": "Air temperature sensor",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaAriaMax",
+            "TemperaturaAriaMedia",
+            "TemperaturaAriaMin"
+          ]
+        },
+        {
+          "sensor": "UmiditaRelativa",
+          "name": "Relative humidity sensor",
+          "unit": "%",
+          "api_parameters": [
+            "UmiditaRelativaMax",
+            "UmiditaRelativaMedia",
+            "UmiditaRelativaMin"
+          ]
+        },
+        {
+          "sensor": "Pioggia",
+          "name": "Rain gauge (RainRateMax in mm/h)",
+          "unit": "mm",
+          "api_parameters": [
+            "Pioggia"
+          ]
+        },
+        {
+          "sensor": "BagnaturaFogliare",
+          "name": "Leaf wetness sensor",
+          "unit": "min",
+          "api_parameters": [
+            "BagnaturaFogliare"
+          ]
+        },
+        {
+          "sensor": "VelocitaVento2m",
+          "name": "Wind speed sensor (anemometer, 2 m)",
+          "unit": "m/s",
+          "api_parameters": [
+            "VelocitaVento2mMax",
+            "VelocitaVento2mMedia"
+          ]
+        },
+        {
+          "sensor": "RadiazioneSolare",
+          "name": "Solar radiation sensor (pyranometer, irradiance)",
+          "unit": "W/m²",
+          "api_parameters": [
+            "RadiazioneSolareMax",
+            "RadiazioneSolareMin"
+          ]
+        },
+        {
+          "sensor": "Suolo1",
+          "name": "Soil moisture sensor 1",
+          "unit": "%",
+          "api_parameters": [
+            "Suolo1"
+          ]
+        },
+        {
+          "sensor": "Suolo2",
+          "name": "Soil moisture sensor 2",
+          "unit": "%",
+          "api_parameters": [
+            "Suolo2"
+          ]
+        },
+        {
+          "sensor": "TemperaturaTerreno",
+          "name": "Soil temperature sensor 1",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaTerrenoMedia"
+          ]
+        },
+        {
+          "sensor": "TemperaturaTerreno2",
+          "name": "Soil temperature sensor 2",
+          "unit": "°C",
+          "api_parameters": [
+            "TemperaturaTerrenoMedia2"
+          ]
+        }
+      ],
+      "DailyOnlyParameters": [
+        "EvapoPot",
+        "RadiazioneSolareInt"
+      ]
+    }
+  ]
+}
diff --git a/API/fetch_3a.py b/API/fetch_3a.py
new file mode 100644
index 0000000..8b5b630
--- /dev/null
+++ b/API/fetch_3a.py
@@ -0,0 +1,493 @@
+#!/usr/bin/env python3
+"""
+fetch_3a.py - fetch stations, sensors and weather data from the 3A API
+(https://api.3acloud.it, spec in 3a_api_dump/spec__serve_docs_base.json).
+
+    python fetch_3a.py overview                      # stations count, sensors, columns, date ranges
+    python fetch_3a.py stations                      # list of stations (+ count)
+    python fetch_3a.py sensors                       # hourly/daily sensors per station
+    python fetch_3a.py catalogue                     # ask again (3 requests), rewrite 3a_stations_sensors.json
+    python fetch_3a.py columns                       # all hourly/daily column names
+    python fetch_3a.py daily  --from 2024-01-01 --to 2024-12-31
+    python fetch_3a.py hourly --from 2024-01-01 --to 2024-01-31 --stations 4,46 \
+                              --columns CodiceStazione,Stazione,Timestamp,TemperaturaAriaMedia,Pioggia
+
+    options: --stations 1,2,3   (default: all stations)
+             --columns a,b,c    (default: all available columns)
+             --chunk-days N     (split long periods into N-day requests)
+             --refresh          (ignore cached metadata and ask the server again)
+
+How auth works (from the spec):
+    POST /login with headers name, password, totp  -> token
+    every other call sends header  token: <token>
+
+Credentials are not stored in this file:
+    THREEA_USER         username (default: mohsen)
+    THREEA_PASSWORD     password (else asked with a hidden prompt)
+    THREEA_TOTP_SECRET  optional base32 TOTP secret -> codes generated automatically
+                        (else you are asked to paste the 6-digit code when a login is needed)
+
+The token is cached in 3a_data/.token, so you only need a new TOTP when it expires.
+The server allows only 5 requests per 15 minutes; the script waits automatically.
+Results are written to 3a_data/ as .json and .csv.
+"""
+
+import argparse
+import getpass
+import json
+import os
+import sys
+import time
+from datetime import datetime, timedelta
+from pathlib import Path
+
+import pandas as pd
+import requests
+
+BASE_URL = "https://api.3acloud.it"
+OUT = Path(__file__).resolve().parent / "3a_data"
+OUT.mkdir(exist_ok=True)
+TOKEN_FILE = OUT / ".token"
+TIMEOUT = 120
+
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+session = requests.Session()
+session.headers["output-fmt"] = "json"
+_password = None
+
+
+# ─────────────────────────── rate limit ───────────────────────────
+
+_rate = {"remaining": None, "reset": None}
+
+
+def _note_rate_limit(resp):
+    h = resp.headers
+    if "X-RateLimit-Remaining" in h:
+        _rate["remaining"] = int(h["X-RateLimit-Remaining"])
+    if "X-RateLimit-Reset" in h:
+        _rate["reset"] = int(h["X-RateLimit-Reset"])
+
+
+def _wait_until(epoch, why):
+    while True:
+        left = int(epoch - time.time()) + 2
+        if left <= 0:
+            break
+        print(f"\r  {why}: waiting {left // 60:02d}:{left % 60:02d} ", end="", flush=True)
+        time.sleep(min(left, 5))
+    print("\r" + " " * 60 + "\r", end="")
+
+
+def _respect_rate_limit():
+    if _rate["remaining"] == 0 and _rate["reset"] and _rate["reset"] > time.time():
+        _wait_until(_rate["reset"], "rate limit (5 requests / 15 min)")
+
+
+def _send(method, path, **kw):
+    """One HTTP request, waiting for the rate-limit window if needed."""
+    while True:
+        _respect_rate_limit()
+        r = session.request(method, BASE_URL + path, timeout=TIMEOUT, **kw)
+        _note_rate_limit(r)
+        if r.status_code == 429:
+            reset = _rate["reset"] or time.time() + 15 * 60
+            _wait_until(reset, "server said 429 Too Many Requests")
+            continue
+        return r
+
+
+# ─────────────────────────── auth ───────────────────────────
+
+CRED_FILE = Path(__file__).resolve().parent / "3a_credentials.json"
+
+
+def _cred(key, env):
+    """Value from the environment, else from 3a_credentials.json (if filled in)."""
+    if os.environ.get(env):
+        return os.environ[env]
+    if CRED_FILE.exists():
+        value = json.loads(CRED_FILE.read_text(encoding="utf-8")).get(key, "")
+        if value and not value.startswith("PUT_"):
+            return value
+    return None
+
+
+def _ask_totp():
+    secret = _cred("totp_secret", "THREEA_TOTP_SECRET")
+    if secret:
+        import pyotp  # pip install pyotp
+        return pyotp.TOTP(secret.replace(" ", "")).now()
+    while True:
+        code = input("Paste a fresh 6-digit TOTP code (it changes every 30 s): ").strip().replace(" ", "")
+        if code.isdigit() and len(code) == 6:
+            return code
+        print("  That doesn't look like a 6-digit code, try again.")
+
+
+def _extract_token(resp):
+    try:
+        data = resp.json()
+    except ValueError:
+        return resp.text.strip().strip('"') or None
+    if isinstance(data, str):
+        return data
+    if isinstance(data, dict):
+        for k in ("token", "access_token", "Token"):
+            if k in data:
+                return data[k]
+    return None
+
+
+def login():
+    global _password
+    user = _cred("user", "THREEA_USER") or "mohsen"
+    if _password is None:
+        _password = _cred("password", "THREEA_PASSWORD") or getpass.getpass(
+            f"Password for {user} (typing is hidden, press Enter when done): ")
+    for attempt in range(3):
+        totp = _ask_totp()
+        r = _send("POST", "/login", headers={"name": user, "password": _password, "totp": totp})
+        token = _extract_token(r) if r.ok else None
+        if token:
+            session.headers["token"] = token
+            TOKEN_FILE.write_text(token)
+            print("  ✓ logged in, token cached")
+            return
+        print(f"  ✗ login failed ({r.status_code}): {r.text[:200]}")
+        if r.status_code not in (401, 403):
+            break
+        print("  The TOTP may have expired or been mistyped.")
+    sys.exit("Could not log in.")
+
+
+def _load_cached_token():
+    if TOKEN_FILE.exists():
+        session.headers["token"] = TOKEN_FILE.read_text().strip()
+        return True
+    return False
+
+
+def call(method, path, body=None):
+    """Authenticated API call; logs in (asking for a TOTP) only when needed."""
+    if "token" not in session.headers and not _load_cached_token():
+        login()
+    r = _send(method, path, json=body)
+    if r.status_code in (401, 403):
+        print(f"  token rejected ({r.status_code}: {r.text[:80]}) -> new login needed")
+        TOKEN_FILE.unlink(missing_ok=True)
+        session.headers.pop("token", None)
+        login()
+        r = _send(method, path, json=body)
+    if not r.ok:
+        sys.exit(f"{method} {path} failed ({r.status_code}): {r.text[:500]}")
+    try:
+        return r.json()
+    except ValueError:
+        return r.text
+
+
+# ─────────────────────────── saving ───────────────────────────
+
+def save(name, data):
+    (OUT / f"{name}.json").write_text(json.dumps(data, indent=2, ensure_ascii=False), encoding="utf-8")
+    df = to_frame(data)
+    if df is not None:
+        df.to_csv(OUT / f"{name}.csv", index=False, encoding="utf-8-sig")
+    return df
+
+
+def to_frame(data):
+    if isinstance(data, list) and data and all(isinstance(x, dict) for x in data):
+        return pd.json_normalize(data)
+    if isinstance(data, dict):
+        # e.g. {"data": [...]} or {station_id: [...]}
+        lists = {k: v for k, v in data.items() if isinstance(v, list)}
+        if len(lists) == 1:
+            return to_frame(next(iter(lists.values())))
+        if lists and all(not isinstance(x, dict) for v in lists.values() for x in v):
+            return pd.DataFrame([(k, x) for k, v in lists.items() for x in v], columns=["key", "value"])
+    return None
+
+
+def cached(name, method, path, body=None, refresh=False):
+    """Metadata changes rarely and requests are scarce, so reuse what's on disk."""
+    f = OUT / f"{name}.json"
+    if f.exists() and not refresh:
+        return json.loads(f.read_text(encoding="utf-8"))
+    print(f"→ {method} {path}")
+    data = call(method, path, body)
+    save(name, data)
+    return data
+
+
+# ─────────────────────────── commands ───────────────────────────
+
+def get_stations(refresh=False):
+    data = cached("stations", "GET", "/getAvailableStations", refresh=refresh)
+    df = to_frame(data)
+    return data, df
+
+
+def station_ids(args):
+    if args.stations:
+        return [int(s) for s in args.stations.split(",")]
+    _, df = get_stations(args.refresh)
+    col = next((c for c in ("StationId", "id") if df is not None and c in df.columns), None)
+    if col is None:
+        sys.exit("Could not read station ids from stations.json; pass --stations explicitly.")
+    return sorted(int(i) for i in df[col])
+
+
+def cmd_stations(args):
+    data, df = get_stations(args.refresh)
+    if df is None:
+        print(json.dumps(data, indent=2, ensure_ascii=False)[:3000])
+        return
+    print(f"\nNumber of stations: {len(df)}")
+    with pd.option_context("display.max_rows", 500, "display.width", 200):
+        print(df.to_string(index=False))
+    print(f"\nSaved → {OUT / 'stations.csv'}")
+
+
+def cmd_sensors(args):
+    ids = station_ids(args)
+    for kind in ("Hourly", "Daily"):
+        data = cached(f"sensors_{kind.lower()}", "POST", f"/getAvailableStation{kind}Sensors",
+                      {"stationsIds": ids}, args.refresh)
+        print(f"\n{kind} sensors:")
+        print(json.dumps(data, indent=1, ensure_ascii=False)[:4000])
+        print(f"  saved → {OUT / f'sensors_{kind.lower()}.json'}")
+
+
+def cmd_summary(args):
+    """Station x sensor tables built from stations.json + sensors_*.json."""
+    _, stations = get_stations(args.refresh)
+    names = dict(zip(stations["StationId"].astype(int), stations["StationName"]))
+    rows = []
+    for kind in ("Hourly", "Daily"):
+        data = cached(f"sensors_{kind.lower()}", "POST", f"/getAvailableStation{kind}Sensors",
+                      {"stationsIds": station_ids(args)}, args.refresh)
+        items = data if isinstance(data, list) else [data]
+        for item in items:
+            for sid, sensors in item.items():
+                for s in sensors:
+                    rows.append({"StationId": int(sid), "StationName": names.get(int(sid), ""),
+                                 "Resolution": kind.lower(), "Sensor": s.get("name"),
+                                 "Description": s.get("description"), "Unit": s.get("unit")})
+    long = pd.DataFrame(rows).sort_values(["StationId", "Resolution", "Sensor"])
+    long.to_csv(OUT / "station_sensors_list.csv", index=False, encoding="utf-8-sig")
+
+    hourly = long[long.Resolution == "hourly"]
+    matrix = pd.crosstab([hourly.StationId, hourly.StationName], hourly.Sensor).clip(upper=1)
+    matrix = matrix.reindex(pd.MultiIndex.from_arrays(
+        [stations["StationId"].astype(int), stations["StationName"]]), fill_value=0)
+    matrix.insert(0, "NumSensors", matrix.sum(axis=1))
+    matrix.to_csv(OUT / "station_sensors_matrix_hourly.csv", encoding="utf-8-sig")
+
+    with pd.option_context("display.max_rows", 500, "display.width", 250):
+        print(f"\n{len(stations)} stations. Hourly sensors per station (1 = present):\n")
+        print(matrix.to_string())
+        print("\nSensor catalogue (how many stations have each, hourly / daily):\n")
+        cat = long.groupby(["Sensor", "Description", "Unit", "Resolution"]).StationId.nunique()
+        print(cat.unstack("Resolution").fillna(0).astype(int).to_string())
+    print(f"\nSaved → {OUT / 'station_sensors_list.csv'}\n        {OUT / 'station_sensors_matrix_hourly.csv'}")
+
+
+# ─────────────────────────── catalogue (3a_stations_sensors.json) ───────────────────────────
+
+CATALOGUE_FILE = Path(__file__).resolve().parent / "3a_stations_sensors.json"
+
+# sensor group (name used by the engine's stations.py) -> label, unit, its hourly api_parameters
+SENSOR_GROUPS = [
+    ("TemperaturaAria", "Air temperature sensor", "°C",
+     ["TemperaturaAriaMax", "TemperaturaAriaMedia", "TemperaturaAriaMin"]),
+    ("UmiditaRelativa", "Relative humidity sensor", "%",
+     ["UmiditaRelativaMax", "UmiditaRelativaMedia", "UmiditaRelativaMin"]),
+    ("TBulb", "Wet-bulb temperature (mean, supplied by 3A)", "°C", ["TBulbMed"]),
+    ("Pioggia", "Rain gauge (RainRateMax in mm/h)", "mm", ["Pioggia", "RainRateMax"]),
+    ("BagnaturaFogliare", "Leaf wetness sensor", "min", ["BagnaturaFogliare"]),
+    ("VelocitaVento2m", "Wind speed sensor (anemometer, 2 m)", "m/s",
+     ["VelocitaVento2mMax", "VelocitaVento2mMedia"]),
+    ("Pressione", "Barometric pressure sensor", "mbar", ["PressioneMax", "PressioneMed", "PressioneMin"]),
+    ("RadiazioneSolare", "Solar radiation sensor (pyranometer, irradiance)", "W/m²",
+     ["RadiazioneSolareMax", "RadiazioneSolareMin"]),
+    ("Suolo1", "Soil moisture sensor 1", "%", ["Suolo1"]),
+    ("Suolo2", "Soil moisture sensor 2", "%", ["Suolo2"]),
+    ("TemperaturaTerreno", "Soil temperature sensor 1", "°C", ["TemperaturaTerrenoMedia"]),
+    ("TemperaturaTerreno2", "Soil temperature sensor 2", "°C", ["TemperaturaTerrenoMedia2"]),
+]
+
+
+def _sensors_by_station(data):
+    """{station id: {api_parameter: sensor entry}} from a getAvailableStation*Sensors answer."""
+    out = {}
+    for item in (data if isinstance(data, list) else [data]):
+        for sid, sensors in item.items():
+            out.setdefault(int(sid), {}).update({s["name"]: s for s in sensors})
+    return out
+
+
+def build_catalogue(stations, hourly, daily, checked):
+    """Catalogue dict from the three metadata answers (no API call).
+
+    Sensors = HOURLY sensors grouped as in SENSOR_GROUPS; a parameter not in
+    any group gets its own group (named after it, label/unit from the API).
+    DailyOnlyParameters = columns offered only in daily data.
+    """
+    hourly, daily = _sensors_by_station(hourly), _sensors_by_station(daily)
+    known = {p for g in SENSOR_GROUPS for p in g[3]}
+    result = []
+    for st in sorted(stations, key=lambda s: int(s["StationId"])):
+        sid = int(st["StationId"])
+        have = hourly.get(sid, {})
+        sensors = []
+        for key, label, unit, params in SENSOR_GROUPS:
+            present = [p for p in params if p in have]
+            if present:
+                sensors.append({"sensor": key, "name": label, "unit": unit, "api_parameters": present})
+        for p in sorted(set(have) - known):
+            print(f"  note: station {sid} has new hourly parameter {p!r}; listed as its own sensor")
+            sensors.append({"sensor": p, "name": have[p].get("description", p),
+                            "unit": have[p].get("unit", ""), "api_parameters": [p]})
+        entry = {k: st.get(k) for k in ("StationId", "StationName", "Municipality", "Latitude_WGS84",
+                                        "Longitude_WGS84", "Elevation_m", "TimeZone")}
+        entry.update(NumSensors=len(sensors), Sensors=sensors,
+                     DailyOnlyParameters=sorted(set(daily.get(sid, {})) - set(have)))
+        result.append(entry)
+    return {
+        "source": "https://api.3acloud.it (getAvailableStations, getAvailableStationHourlySensors, "
+                  "getAvailableStationDailySensors)",
+        "checked": checked,
+        "note": "Sensors = HOURLY sensors (what the blocks fetch). DailyOnlyParameters = columns the "
+                "station offers only in daily data (/getWeatherDaily). Units from /getAvailableHourlyColumns.",
+        "NumStations": len(result),
+        "Stations": result,
+    }
+
+
+def cmd_catalogue(args):
+    """Ask the server again (3 requests) and rewrite 3a_stations_sensors.json."""
+    stations, _ = get_stations(refresh=True)
+    ids = sorted(int(s["StationId"]) for s in stations)
+    sensors = {kind: cached(f"sensors_{kind.lower()}", "POST", f"/getAvailableStation{kind}Sensors",
+                            {"stationsIds": ids}, refresh=True) for kind in ("Hourly", "Daily")}
+    old = json.loads(CATALOGUE_FILE.read_text(encoding="utf-8")) if CATALOGUE_FILE.exists() else {}
+    new = build_catalogue(stations, sensors["Hourly"], sensors["Daily"], datetime.now().strftime("%Y-%m-%d"))
+    params = lambda cat: {s["StationId"]: {p for x in s["Sensors"] for p in x["api_parameters"]}
+                          for s in cat.get("Stations", [])}
+    before, after = params(old), params(new)
+    print("\nChanges in hourly parameters since the last catalogue:")
+    for sid in sorted(set(before) | set(after)):
+        added, removed = after.get(sid, set()) - before.get(sid, set()), before.get(sid, set()) - after.get(sid, set())
+        if added or removed:
+            print(f"  {sid}: added {sorted(added)}  removed {sorted(removed)}")
+    if before == after:
+        print("  none")
+    CATALOGUE_FILE.write_text(json.dumps(new, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+    print(f"\nSaved → {CATALOGUE_FILE}  (update README.md if anything changed)")
+
+
+def get_columns(kind, refresh=False):
+    data = cached(f"columns_{kind.lower()}", "GET", f"/getAvailable{kind}Columns", refresh=refresh)
+    if isinstance(data, dict):
+        data = next((v for v in data.values() if isinstance(v, list)), data)
+    if isinstance(data, list) and data and isinstance(data[0], dict):
+        key = next(iter(data[0]))
+        data = [d[key] for d in data]
+    return data
+
+
+def cmd_columns(args):
+    for kind in ("Hourly", "Daily"):
+        cols = get_columns(kind, args.refresh)
+        print(f"\n{kind} columns ({len(cols)}):\n  " + ", ".join(map(str, cols)))
+
+
+def cmd_overview(args):
+    cmd_stations(args)
+    cmd_sensors(args)
+    cmd_columns(args)
+    ids = station_ids(args)
+    print("\nAvailable periods:")
+    for kind in ("Daily", "Hourly"):
+        for edge in ("First", "Last"):
+            data = cached(f"{edge.lower()}_{kind.lower()}_timestamp", "POST",
+                          f"/get{edge}Available{kind}Timestamp", {"stationsIds": ids}, args.refresh)
+            print(f"  {edge:5s} {kind:6s}: {json.dumps(data, ensure_ascii=False)[:300]}")
+
+
+def _parse_date(s):
+    for fmt in ("%Y-%m-%d %H:%M", "%Y-%m-%d"):
+        try:
+            return datetime.strptime(s, fmt)
+        except ValueError:
+            pass
+    sys.exit(f"Bad date {s!r}; use YYYY-MM-DD or 'YYYY-MM-DD HH:MM'")
+
+
+def _periods(start, end, chunk_days):
+    if not chunk_days:
+        yield start, end
+        return
+    cur = start
+    while cur <= end:
+        nxt = min(cur + timedelta(days=chunk_days) - timedelta(minutes=1), end)
+        yield cur, nxt
+        cur = nxt + timedelta(minutes=1)
+
+
+def cmd_weather(args, kind):
+    ids = station_ids(args)
+    columns = args.columns.split(",") if args.columns else get_columns(kind, args.refresh)
+    start, end = _parse_date(args.date_from), _parse_date(args.date_to)
+    if len(args.date_to) == 10:
+        end = end.replace(hour=23, minute=59)
+
+    frames, raw = [], []
+    for a, b in _periods(start, end, args.chunk_days):
+        body = {"stationsIds": ids, "dateFrom": a.strftime("%Y-%m-%d %H:%M"),
+                "dateTo": b.strftime("%Y-%m-%d %H:%M"), "columns": columns}
+        print(f"→ {kind} data {body['dateFrom']} … {body['dateTo']} for {len(ids)} station(s)")
+        data = call("POST", f"/getWeather{kind}", body)
+        raw.append(data)
+        df = to_frame(data)
+        if df is not None:
+            frames.append(df)
+            print(f"   {len(df)} rows")
+        else:
+            print("   (response not tabular, kept in JSON)", str(data)[:200])
+
+    name = f"weather_{kind.lower()}_{start:%Y%m%d}_{end:%Y%m%d}"
+    (OUT / f"{name}.json").write_text(json.dumps(raw if len(raw) > 1 else raw[0], indent=1,
+                                                ensure_ascii=False), encoding="utf-8")
+    if frames:
+        df = pd.concat(frames, ignore_index=True)
+        df.to_csv(OUT / f"{name}.csv", index=False, encoding="utf-8-sig")
+        print(f"\n{len(df)} rows, {df.shape[1]} columns → {OUT / (name + '.csv')}")
+        print(df.head(10).to_string(index=False))
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("command", choices=["overview", "stations", "sensors", "summary", "catalogue", "columns",
+                                        "daily", "hourly", "login"])
+    p.add_argument("--from", dest="date_from")
+    p.add_argument("--to", dest="date_to")
+    p.add_argument("--stations", help="comma-separated station ids (default: all)")
+    p.add_argument("--columns", help="comma-separated column names (default: all)")
+    p.add_argument("--chunk-days", type=int, help="split the period into N-day requests")
+    p.add_argument("--refresh", action="store_true", help="re-download cached metadata")
+    args = p.parse_args()
+
+    if args.command in ("daily", "hourly") and not (args.date_from and args.date_to):
+        p.error("--from and --to are required for daily/hourly")
+
+    {"overview": cmd_overview, "stations": cmd_stations, "sensors": cmd_sensors, "summary": cmd_summary,
+     "catalogue": cmd_catalogue,
+     "columns": cmd_columns, "login": lambda a: login(),
+     "daily": lambda a: cmd_weather(a, "Daily"), "hourly": lambda a: cmd_weather(a, "Hourly")}[args.command](args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..fa9bb75
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,11 @@
+# Instructions for Claude
+
+1. Before any task, read `README.md`. It is the starting point: project
+   layout, rules, block status, known issues and the change log.
+2. This project is private: never upload it, create git/GitHub files, or
+   print or send the 3A credentials (`API/3a_credentials.json`,
+   `API/mohsen_new_api.html`).
+3. 3A API: 5 requests per 15 minutes, hourly data only for about the last
+   180 days. Keep live calls to a minimum and prefer unit tests.
+4. After every change, update `README.md`: the matching section (layout,
+   block status, known issues) and a dated entry in the *Change log*.
diff --git a/Diagram.png b/Diagram.png
new file mode 100644
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9d5e03d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,27 @@
+# Makes src/damage_engine an installable package, so every file can import
+# every other file with   from damage_engine.<folder>.<file> import ...
+# Install once (inside the virtual environment):   pip install -e .
+
+[build-system]
+requires = ["setuptools>=68"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "damage-engine"
+version = "0.1.0"
+requires-python = ">=3.10"
+dependencies = [
+    # add libraries here as you need them, e.g. "numpy", "pandas", "pyyaml"
+    "numpy",
+    "pandas",
+    "requests",
+    "pyotp",
+    "fpdf2",        # station PDF reports (report/station_pdf.py), pure Python
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+# logo and fonts used by the station PDF
+[tool.setuptools.package-data]
+"damage_engine.report" = ["assets/*.png", "assets/fonts/*"]
diff --git a/src/damage_engine/CV_models/__init__.py b/src/damage_engine/CV_models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/CV_models/brina_gelo_snow.py b/src/damage_engine/CV_models/brina_gelo_snow.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/CV_models/drought.py b/src/damage_engine/CV_models/drought.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/CV_models/flood.py b/src/damage_engine/CV_models/flood.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/CV_models/hail.py b/src/damage_engine/CV_models/hail.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/CV_models/strong_wind.py b/src/damage_engine/CV_models/strong_wind.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/__init__.py b/src/damage_engine/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/detectors/__init__.py b/src/damage_engine/detectors/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/detectors/brina.py b/src/damage_engine/detectors/brina.py
new file mode 100644
index 0000000..d156ad4
--- /dev/null
+++ b/src/damage_engine/detectors/brina.py
@@ -0,0 +1,378 @@
+"""
+Brina (hoar frost) detector. Runs every morning on the night just ended.
+
+Contract (UnipolSai Model 1506, p.7): freezing of dew / sublimation of water
+vapour on the crop due to nocturnal radiation. The contract gives no number,
+so every value below is a criterion for radiative frost with ice deposition.
+All values are read from damage_engine.thresholds:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [LITERATURE]  WSU EB0913 / MSU "Critical spring temperatures for tree fruit bud stages"
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+No 3A station measures wet-bulb temperature. Brina uses the shared WetBulbMin
+of damage_engine.sources.derived: the lowest wet-bulb of the hour, from the
+hourly minimum temperature and maximum humidity, with the formula of the 3A
+document [3A-DOC] (docs/ImplementazioneAlgoritmiSensori.pdf, section 4). Every
+block uses that same value. The frost point is derived.dew_point_c, also from
+the 3A document (section 5: ice constants for T <= 0 °C, i.e. the frost point),
+on the hourly temperature used by Brina and the hourly mean humidity.
+3A's own "TBulbMed" is the same formula on the hourly MEAN
+temperature and humidity, so it is warmer than the coldest part of the hour.
+It is NOT used for detection; on an event its lowest value is reported next to
+ours (details["min_wet_bulb_3a_c"]) as a check.
+
+Night window - BRINA_SCHEDULE [PROJECT], from RUN_SCHEDULE "sunset -> sunrise":
+  18:00 of the previous day (night_start_hour) -> 06:00 of the run day (run_hour).
+ Stat
+An hour of the window is a brina hour when all of these hold (BRINA):
+  - it is really night: the hour's highest solar irradiance (RadiazioneSolareMax)
+    <= night_solar_max_wm2 = 10 W/m² [ENGINEERING].
+    Why: brina forms only by nocturnal radiation (contract p.7), but the window
+    uses fixed clock hours, while sunset and sunrise move during the cover
+    season (budburst -> 10 May). In April-May 18:00-20:00 and often 06:00 are
+    still daylight. Where a pyranometer exists, these sunlit hours are dropped;
+    elsewhere the fixed window is the approximation of the night.
+  - air temperature <= t_air_max_c = 2.0 °C [ENGINEERING]
+    (2 m air may be slightly above 0 °C while buds freeze).
+  - wet-bulb <= t_wet_max_c = 0.0 °C [ENGINEERING] (supports ice deposition).
+  - frost on the buds, depending on the wind:
+      calm hour  (wind <= calm_wind_max_ms = 2.0 m/s [ENGINEERING], or no wind data):
+                 ice deposition, bud temperature <= min(0, frost point)
+                 (BRINA["deposition"]), with bud = air - radiative_offset_c
+                 = 2.0 °C [ENGINEERING] (on calm nights buds cool below the air
+                 by radiation).
+      windy hour (wind > calm_wind_max_ms): air < 0 °C, the contract's frost
+                 trigger (GELO["t_air_trigger_c"] = 0.0 °C, operator "<",
+                 [CONTRACT p.7]), with bud = air (wind mixes the air). No ice
+                 test: with buds at air temperature it would need 100 % humidity,
+                 so windy sub-zero nights would almost never count. On a windy
+                 hour the air and wet-bulb tests above always hold.
+So every sub-zero night hour with wind above 2 m/s is a brina hour.ions
+without a wind sensor: calm assumed, details["wind_checked"] = False.
+Event when the brina hours reach min_duration_h = 0.5 h [LITERATURE]
+(critical temperatures refer to ~30 min exposure), so one hourly record is enough.
+
+Severity, per field, from the night's lowest bud temperature:
+  - BBCH < APPLE_DORMANT_BELOW_BBCH = 51: dormant, severity none [ENGINEERING].
+  - with a BBCH stage: stage from APPLE_BBCH_TO_STAGE [ENGINEERING, approx.],
+    T10/T90 bud kill temperatures from APPLE_CRITICAL_TEMPS_C [LITERATURE, WSU/MSU],
+    kill fraction from FROST_KILL_CURVE (0% at T10 + 1 °C, 100% at T90 - 2 °C)
+    [ENGINEERING], graded with FROST_KILL_BANDS (kill >= 0.01%/10%/50%/90% ->
+    light/moderate/severe/extreme) [ENGINEERING].
+  - without a stage: FROST_FALLBACK_BANDS_C (bud temp < 0/-2/-4/-6 °C ->
+    light/moderate/severe/extreme) [ENGINEERING].
+Missing sensors / missing values - the detector still runs:
+  - an empty solar value:
+    the night test is not applied, every hour of the window counts as night.
+  - an empty wind value:
+    the hour counts as calm and the bud offset is used (worst case: coldest
+    buds); details["wind_checked"] = False tells the report.
+  - no hourly minimum temperature: the hourly mean temperature is used.
+  - no hourly maximum humidity: the hourly mean humidity is used for the
+    wet-bulb (derived.add_wet_bulb).
+  - no TBulbMed: the check value is left out; it is not
+    used for detection (we compute the minimum ourselves)
+  - temperature and humidity are essential (needed for wet-bulb and frost
+    point): a station without these sensors gives status "no_data"
+    ("station lacks required sensors"), hours missing either value are
+    skipped, and a night with no usable hour gives "no_data".
+  Missing columns are read as empty (_col), so no sensor causes an error.
+  Every case above is written to Analysis.data_notes (sensor missing at the
+  station, no data last night, or N of M hours missing, plus what was done),
+  so the report shows how each result was obtained.
+
+Cover window: COVER_WINDOWS["brina"] = budburst -> 10 May [CONTRACT Special Art.5 p.31].
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    APPLE_CRITICAL_TEMPS_C,
+    APPLE_DORMANT_BELOW_BBCH,
+    BRINA,
+    BRINA_SCHEDULE,
+    COVER_WINDOWS,
+    FROST_FALLBACK_BANDS_C,
+    FROST_KILL_BANDS,
+    FROST_KILL_CURVE,
+    GELO,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "brina"
+RUN_HOUR = BRINA_SCHEDULE["run_hour"]
+NIGHT_START_HOUR = BRINA_SCHEDULE["night_start_hour"]
+HOURS_PER_RECORD = 1.0                # hourly data
+SUB_ZERO_OP = {"<": operator.lt, "<=": operator.le}[GELO["t_air_trigger_op"]]   # windy-hour test
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("TemperaturaAria", "UmiditaRelativa")
+SENSOR_WIND = "VelocitaVento2m"       # optional sensors (3A sensor names)
+SENSOR_SOLAR = "RadiazioneSolare"
+COL_TEMP = "TemperaturaAriaMin"       # hourly minimum air temperature, °C
+COL_TEMP_FALLBACK = "TemperaturaAriaMedia"
+COL_RH = "UmiditaRelativaMedia"       # %, frost point
+COL_RH_MAX = "UmiditaRelativaMax"     # %, wet-bulb of the coldest moment (derived.WET_BULB_MIN)
+COL_WIND = "VelocitaVento2mMedia"     # m/s
+COL_SOLAR = "RadiazioneSolareMax"     # W/m², highest irradiance of the hour; stations 544, 566, 600
+                                      # (not RadiazioneSolareInt: MJ/m², daily only, empty hourly)
+COL_WET_BULB_3A = "TBulbMed"          # °C, 3A hourly mean wet-bulb: reported only, not used to detect
+COLUMNS = list(dict.fromkeys([COL_TEMP, COL_TEMP_FALLBACK, COL_RH, COL_RH_MAX,
+                               *derived.WET_BULB_INPUTS, COL_WIND, COL_SOLAR, COL_WET_BULB_3A]))
+
+
+# ─────────────────────────── physics ───────────────────────────
+# wet-bulb and frost point: damage_engine.sources.derived (3A document, shared by every block)
+
+def stage_for(bbch: int) -> str:
+    """Apple bud stage for a BBCH code (first stage whose highest code is >= bbch)."""
+    return next(APPLE_BBCH_TO_STAGE[c] for c in sorted(APPLE_BBCH_TO_STAGE) if bbch <= c)
+
+
+def kill_fraction(bud_temp_c: float, stage: str) -> float:
+    """Fraction of buds killed: 0 at T10+offset, 10% at T10, 90% at T90, 100% at T90-offset."""
+    t10, t90 = APPLE_CRITICAL_TEMPS_C[stage]
+    xs = [t90 - FROST_KILL_CURVE["full_kill_offset_c"], t90, t10,
+          t10 + FROST_KILL_CURVE["no_damage_offset_c"]]
+    return float(np.interp(bud_temp_c, xs, [1.0, 0.90, 0.10, 0.0]))
+
+
+def field_severity(min_bud_temp_c: float, bbch: int | None):
+    """(Severity, score, details) for one field from the night's lowest bud temperature."""
+    if bbch is None:
+        sev, score = grade(min_bud_temp_c, FROST_FALLBACK_BANDS_C)
+        return sev, score, {"severity_method": "fallback_min_bud_temp"}
+    if bbch < APPLE_DORMANT_BELOW_BBCH:
+        return Severity.NONE, 0.0, {"severity_method": "dormant", "bbch": bbch, "kill_fraction": 0.0}
+    stage = stage_for(bbch)
+    kill = kill_fraction(min_bud_temp_c, stage)
+    sev, score = grade(kill, FROST_KILL_BANDS)
+    return sev, score, {"severity_method": "kill_fraction", "bbch": bbch, "stage": stage,
+                        "kill_fraction": round(kill, 3)}
+
+
+def in_cover_window(run_day: date, bbch: int | None):
+    """Contract cover window (budburst -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[BLOCK]
+    if run_day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return bbch >= APPLE_DORMANT_BELOW_BBCH       # approx.: buds out of dormancy = budburst
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def night_window(run_day: date):
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    start = datetime.combine(run_day - timedelta(days=1), time(NIGHT_START_HOUR))
+    return start, end
+
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def brina_hours(df: pd.DataFrame) -> pd.DataFrame:
+    """Add temp/bud/wet-bulb/frost-point columns and a boolean `brina` column to hourly rows."""
+    df = derived.add_wet_bulb(df)                 # WetBulbMin / WetBulbMax, same in every block
+    df["temp_c"] = _col(df, COL_TEMP).fillna(_col(df, COL_TEMP_FALLBACK))
+    wind, solar = _col(df, COL_WIND), _col(df, COL_SOLAR)
+    night = solar.isna() | (solar <= BRINA["night_solar_max_wm2"])   # no pyranometer -> not applied
+    df["calm"] = wind.isna() | (wind <= BRINA["calm_wind_max_ms"])   # no wind data -> calm (worst case)
+    # buds cool below the 2 m air by radiation only on calm hours
+    df["bud_temp_c"] = df["temp_c"] - BRINA["radiative_offset_c"] * df["calm"].astype(float)
+    df["wet_bulb_c"] = df[derived.WET_BULB_MIN]   # lowest wet-bulb of the hour
+    df["frost_point_c"] = derived.dew_point_c(df["temp_c"], df[COL_RH])   # over ice when <= 0 °C
+    deposition = df["bud_temp_c"] <= np.minimum(0.0, df["frost_point_c"])     # calm hours
+    sub_zero = SUB_ZERO_OP(df["temp_c"], GELO["t_air_trigger_c"])          # windy hours
+    df["brina"] = (night
+                   & (df["temp_c"] <= BRINA["t_air_max_c"])
+                   & (df["wet_bulb_c"] <= BRINA["t_wet_max_c"])
+                   & deposition.where(df["calm"], sub_zero))
+    return df
+
+
+def _gap_note(values: pd.Series, label: str, has_sensor: bool | None, effect: str) -> str | None:
+    """One data note for an optional input: no sensor, no data, or some hours missing."""
+    n, missing = len(values), int(values.isna().sum())
+    if has_sensor is False:
+        return f"No {label} sensor at this station: {effect}."
+    if missing == n:
+        return f"No {label} data last night: {effect}."
+    if missing:
+        return f"{label.capitalize()} missing for {missing} of {n} hours: {effect} for those hours."
+    return None
+
+
+def data_notes(rows: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing sensors / data of one station night and how they were handled.
+
+    `sensors` = the station's 3A sensor names (None = unknown: only the data is checked).
+    """
+    def has(sensor):
+        return None if sensors is None else sensor in sensors
+
+    offset = BRINA["radiative_offset_c"]
+    notes = [
+        _gap_note(_col(rows, COL_SOLAR), "solar radiation", has(SENSOR_SOLAR),
+                  "night test not applied, every hour of the window counts as night"),
+        _gap_note(_col(rows, COL_WIND), "wind", has(SENSOR_WIND),
+                  f"calm assumed, buds {offset:g} °C below the air (worst case)"),
+    ]
+    n = len(rows)
+    mean_used = int((_col(rows, COL_TEMP).isna() & _col(rows, COL_TEMP_FALLBACK).notna()).sum())
+    if mean_used:
+        notes.append(f"Hourly minimum temperature missing for {mean_used} of {n} hours: hourly mean used.")
+    rh_mean_used = int((_col(rows, COL_RH_MAX).isna() & _col(rows, COL_RH).notna()).sum())
+    if rh_mean_used:
+        notes.append(f"Hourly maximum humidity missing for {rh_mean_used} of {n} hours: "
+                     f"hourly mean humidity used for the wet-bulb.")
+    skipped = int((rows["temp_c"].isna() | _col(rows, COL_RH).isna()).sum())
+    if skipped:
+        notes.append(f"Temperature or humidity missing for {skipped} of {n} hours: those hours skipped.")
+    return [x for x in notes if x]
+
+
+def analyse_station(rows: pd.DataFrame, sensors: set[str] | None = None) -> dict:
+    """Station-level result of one night: status, duration, times, measured extremes, data notes."""
+    notes = data_notes(rows, sensors)
+    rows = rows.dropna(subset=["temp_c", COL_RH])
+    if rows.empty:
+        return {"status": NO_DATA, "details": {"reason": "no temperature/humidity records"},
+                "notes": notes}
+    hits = rows[rows["brina"]]
+    has_wind = rows[COL_WIND].notna().any() if COL_WIND in rows else False
+    base = {"wind_checked": bool(has_wind), "records": int(len(rows)),
+            "night_min_temp_c": round(float(rows["temp_c"].min()), 2)}
+    duration = len(hits) * HOURS_PER_RECORD
+    if duration < BRINA["min_duration_h"]:
+        return {"status": NO_EVENT, "details": base, "notes": notes}
+    check = {}
+    wet_3a = _col(hits, COL_WET_BULB_3A).dropna()
+    if not wet_3a.empty:                      # 3A's own wet-bulb, for comparison only
+        check["min_wet_bulb_3a_c"] = round(float(wet_3a.min()), 2)
+    wind = _col(hits, COL_WIND).dropna()
+    if not wind.empty:                        # wind decides only the bud offset
+        check["max_wind_ms"] = round(float(wind.max()), 1)
+        check["windy_hours"] = int((~hits["calm"]).sum()) * HOURS_PER_RECORD
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": duration,
+        "start": hits["Timestamp"].min().to_pydatetime(),
+        "end": hits["Timestamp"].max().to_pydatetime(),
+        "min_bud_temp_c": float(hits["bud_temp_c"].min()),
+        "details": {**base,
+                    "min_temp_c": round(float(hits["temp_c"].min()), 2),
+                    "min_bud_temp_c": round(float(hits["bud_temp_c"].min()), 2),
+                    "min_wet_bulb_c": round(float(hits["wet_bulb_c"].min()), 2),
+                    **check,
+                    "max_rh_pct": round(float(hits[COL_RH].max()), 1)},
+    }
+
+
+def _summary(res, sev, sdet):
+    if res["status"] == NO_DATA:
+        return "No data from the station for last night."
+    if res["status"] == NO_EVENT:
+        return "No brina last night."
+    text = (f"Brina for {res['duration_h']:g} h, lowest bud temperature "
+            f"{res['details']['min_bud_temp_c']} °C: severity {sev.value}")
+    if "kill_fraction" in sdet:
+        text += f" (stage {sdet.get('stage', 'dormant')}, about {sdet['kill_fraction']:.0%} buds killed)"
+    return text + "."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None) -> list[Analysis]:
+    """One Analysis per field: the night of its station, graded with the field's stage.
+
+    station_sensors: 3A sensor names per station, so the data notes can tell
+    "no sensor" from "no data"; without it only the data is checked.
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in brina_hours(df).groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(rows, station_sensors.get(int(sid)))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station last night."]})
+        sev, score, sdet = Severity.NONE, 0.0, {}
+        if res["status"] == EVENT:
+            sev, score, sdet = field_severity(res["min_bud_temp_c"], f.bbch)
+        details = {**res["details"], **sdet}
+        if res["status"] == EVENT:
+            details["in_cover_window"] = in_cover_window(run_day, f.bbch)
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev, sdet), details=details,
+            data_notes=res.get("notes", []),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch last night's hourly data for every field's station and detect brina."""
+    run_day = run_day or date.today()
+    start, end = night_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            if known is None:
+                note = "Station not in the 3A catalogue: Brina cannot be judged."
+            else:
+                lacking = [s for s in REQUIRED_SENSORS if not known.has(s)]
+                note = (f"No {' / '.join(lacking)} sensor at this station "
+                        f"(needed for wet-bulb and frost point): Brina cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no temperature/humidity sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("Brina %s: %d event(s) over %d field(s), %d station(s)", run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Brina detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="morning to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/drought.py b/src/damage_engine/detectors/drought.py
new file mode 100644
index 0000000..56a119a
--- /dev/null
+++ b/src/damage_engine/detectors/drought.py
@@ -0,0 +1,395 @@
+"""
+Drought detector: a rain deficit against the climate average. Runs every month (on the
+DROUGHT_SCHEDULE["run_day_of_month"] = 1st), on the last complete calendar months.
+
+What it detects: a meteorological drought (siccità) = much less rain than usual for the
+time of year. The rain measured by the 3A station over the last DROUGHT["window_months"]
+= 3 complete calendar months [ENGINEERING] is compared with the climate average of the
+same months (thresholds.RAIN_NORMALS_MM). Run on 1 Nov 2026, it checks Aug, Sep and Oct
+2026. The block does not know about irrigation, so an event means "dry period", not
+"crop damaged". No contract trigger is in thresholds.py for drought.
+
+Thresholds (all from damage_engine.thresholds):
+  - DROUGHT["window_months"] = 3            months checked                  [ENGINEERING]
+  - DROUGHT["rain_pct_of_normal_max"] = 60 %, operator "<=": EVENT when the rain of the
+    window is at most 60 % of its average (a deficit of 40 % or more)        [ENGINEERING]
+  - DROUGHT["max_missing_days_pct"] = 20 %: more days without rain data -> no_data
+                                                                             [ENGINEERING]
+  - DROUGHT_BANDS_PCT: severity on the same % of average, lower is worse:
+    light <= 60, moderate <= 45, severe <= 30, extreme <= 15 %               [ENGINEERING]
+  - RAIN_NORMALS_MM: monthly rain average, mm, per station, 1991-2020, CERRA
+    reanalysis read once from Open-Meteo (RAIN_NORMALS_SOURCE)               [EXTERNAL]
+    It is a number in thresholds.py, edited by hand: the block never calls Open-Meteo
+    (only the 3A API is used at run time). The average of the window = the sum of its
+    months' averages.
+  - DROUGHT_SCHEDULE["run_day_of_month"] = 1: due() tells main.py when to run [PROJECT]
+
+Inputs (Diagram.png: "All sensors"; the block reads the ones that tell about water):
+  - Pioggia    = daily rain, mm (3A daily data, all 14 stations): decides the event.
+  - EvapoPot   = daily reference evapotranspiration ET0, mm (3A daily-only column, all
+                 14 stations, calculated by 3A): water balance rain - ET0, information only.
+  - Suolo1/2   = soil moisture, % (hourly sensor, stations 544 and 600 only): mean of the
+                 last month, of its first and of its last 7 days, information only.
+Requests: one daily request (all stations, the whole window) and, when a station has a
+soil moisture sensor, one hourly request for the last month (within 3A's ~180 days).
+
+Missing sensors / missing values - the detector still runs (Analysis.data_notes):
+  - no rain gauge at the station, or no climate average for it in RAIN_NORMALS_MM ->
+    "no_data" (the comparison cannot be made).
+  - days without rain data: counted as 0 mm, so the rain is a lower bound (a note gives
+    the number of days). More than max_missing_days_pct of the days -> "no_data": the
+    deficit could be only missing data.
+  - days without ET0: the water balance uses only the days that have both values
+    (note); no ET0 at all -> no water balance (note).
+  - no soil moisture sensor (12 of 14 stations), no soil data, or the soil request
+    failed -> no soil values (note). Never changes the result.
+  Missing columns are read as empty, so no sensor causes an error. detect(...,
+  station_sensors=...) lets the notes tell "no sensor" from "no data".
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_daily, fetch_hourly
+from damage_engine.thresholds import (
+    DROUGHT,
+    DROUGHT_BANDS_PCT,
+    DROUGHT_SCHEDULE,
+    RAIN_NORMALS_MM,
+    RAIN_NORMALS_SOURCE,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "drought"
+PHENOMENON = "drought"
+RUN_DAY_OF_MONTH = DROUGHT_SCHEDULE["run_day_of_month"]
+MONTHS = int(DROUGHT["window_months"])
+TRIGGER_PCT = DROUGHT["rain_pct_of_normal_max"]
+TRIGGER_OP = DROUGHT["rain_pct_of_normal_op"]
+OPS = {"<=": operator.le, "<": operator.lt}
+SOIL_WEEK_DAYS = 7                     # first / last days of the month compared (soil)
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("Pioggia",)
+SOIL_SENSORS = ("Suolo1", "Suolo2")    # sensor name = api_parameter (soil moisture, %)
+COL_DAY = "Data"                       # daily rows: date column
+COL_RAIN = "Pioggia"                   # daily rain, mm
+COL_ET0 = "EvapoPot"                   # daily reference evapotranspiration ET0, mm
+DAILY_COLUMNS = [COL_RAIN, COL_ET0]
+
+
+# ─────────────────────────── schedule and window ───────────────────────────
+
+def due(run_day: date) -> bool:
+    """True on the day of the month the block runs (main.py skips it on other days)."""
+    return run_day.day == RUN_DAY_OF_MONTH
+
+
+def _add_months(day: date, n: int) -> date:
+    m = day.year * 12 + day.month - 1 + n
+    return date(m // 12, m % 12 + 1, 1)
+
+
+def window_months(run_day: date) -> list[date]:
+    """First day of each complete calendar month checked (the MONTHS before run_day's month)."""
+    this_month = run_day.replace(day=1)
+    return [_add_months(this_month, -MONTHS + i) for i in range(MONTHS)]
+
+
+def window(run_day: date) -> tuple[datetime, datetime]:
+    """(start, end) of the window: from the 1st of the first month to the 1st of the run month."""
+    months = window_months(run_day)
+    return datetime.combine(months[0], time()), datetime.combine(run_day.replace(day=1), time())
+
+
+def months_label(months: list[date]) -> str:
+    """'Aug-Oct 2026', 'Nov 2025-Jan 2026' or 'Oct 2026'."""
+    first, last = months[0], months[-1]
+    if first == last:
+        return f"{first:%b %Y}"
+    if first.year == last.year:
+        return f"{first:%b}–{last:%b %Y}"
+    return f"{first:%b %Y}–{last:%b %Y}"
+
+
+def normals_for(station_id: int, months: list[date]) -> list[float] | None:
+    """Climate average (mm) of each month for the station; None when the station has none."""
+    row = RAIN_NORMALS_MM.get(int(station_id))
+    return None if row is None else [float(row[m.month - 1]) for m in months]
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return pd.to_numeric(df[name], errors="coerce") if name in df else pd.Series(np.nan, index=df.index)
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def daily_grid(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's daily rows on the full grid of the window (missing day = empty row),
+    with day, month (1st of the month), rain_mm and et0_mm."""
+    start, end = window(run_day)
+    grid = pd.date_range(start, end - timedelta(days=1), freq="D", name="day")
+    rows = rows.assign(day=pd.to_datetime(rows[COL_DAY]).dt.normalize()).drop_duplicates("day")
+    df = rows.set_index("day").reindex(grid).reset_index()
+    df["month"] = df["day"].dt.to_period("M").dt.to_timestamp()
+    df["rain_mm"] = _col(df, COL_RAIN)
+    df["et0_mm"] = _col(df, COL_ET0)
+    return df
+
+
+def month_table(df: pd.DataFrame, months: list[date], normals: list[float]) -> list[dict]:
+    """Per month: rain (missing days = 0 mm), its average and % of it, ET0 and rain - ET0."""
+    out = []
+    for m, normal in zip(months, normals):
+        rows = df[df["month"] == pd.Timestamp(m)]
+        rain = float(rows["rain_mm"].sum())
+        both = rows["rain_mm"].notna() & rows["et0_mm"].notna()
+        et0_ok = bool(rows["et0_mm"].notna().any())
+        out.append({
+            "month": f"{m:%Y-%m}",
+            "label": f"{m:%b %Y}",
+            "days": int(len(rows)),
+            "missing_days": int(rows["rain_mm"].isna().sum()),
+            "rain_mm": _round(rain),
+            "normal_mm": _round(normal),
+            "rain_pct_of_normal": _round(100 * rain / normal) if normal > 0 else None,
+            "et0_mm": _round(rows.loc[both, "et0_mm"].sum()) if et0_ok else None,
+            "water_balance_mm": (_round(rows.loc[both, "rain_mm"].sum() - rows.loc[both, "et0_mm"].sum())
+                                 if et0_ok else None),
+        })
+    return out
+
+
+def soil_summary(rows: pd.DataFrame | None, run_day: date, sensors: set[str] | None) -> dict:
+    """Soil moisture of the last month, per sensor the station has: mean, first and last
+    SOIL_WEEK_DAYS days (% ). Empty dict when there is nothing to show."""
+    if rows is None or rows.empty:
+        return {}
+    end = datetime.combine(run_day.replace(day=1), time())
+    start = datetime.combine(window_months(run_day)[-1], time())
+    ts = pd.to_datetime(rows["Timestamp"])
+    rows = rows[(ts > start) & (ts <= end)].assign(Timestamp=ts)
+    out = {}
+    for name in SOIL_SENSORS:
+        if sensors is not None and name not in sensors:
+            continue
+        v = _col(rows, name)
+        if v.notna().sum() == 0:
+            continue
+        first = v[rows["Timestamp"] <= start + timedelta(days=SOIL_WEEK_DAYS)]
+        last = v[rows["Timestamp"] > end - timedelta(days=SOIL_WEEK_DAYS)]
+        out[name] = {"mean_pct": _round(v.mean()), "first_week_pct": _round(first.mean()),
+                     "last_week_pct": _round(last.mean()), "hours": int(v.notna().sum())}
+    return out
+
+
+def data_notes(table: list[dict], sensors: set[str] | None, soil: dict,
+               soil_error: str | None) -> list[str]:
+    """Missing data of one station's window and how it was handled."""
+    notes = [f"Average rain of each month: {RAIN_NORMALS_SOURCE}, at this station's coordinates "
+             f"(modelled, not measured)."]
+    days = sum(m["days"] for m in table)
+    missing = sum(m["missing_days"] for m in table)
+    if missing:
+        which = ", ".join(f"{m['label']} {m['missing_days']}" for m in table if m["missing_days"])
+        notes.append(f"Rain missing for {missing} of {days} days ({which}): counted as 0 mm, so the "
+                     f"rain shown is a lower bound.")
+    if all(m["et0_mm"] is None for m in table):
+        notes.append(f"No evapotranspiration data ({COL_ET0}): water balance not shown.")
+    elif any(m["et0_mm"] is None for m in table):
+        notes.append(f"Evapotranspiration ({COL_ET0}) missing for some months: water balance only "
+                     f"for the months with data.")
+    has_soil = sensors is None or any(s in sensors for s in SOIL_SENSORS)
+    if sensors is not None and not has_soil:
+        notes.append("No soil moisture sensor at this station: soil moisture not shown.")
+    elif soil_error:
+        notes.append(f"Soil moisture could not be read from 3A ({soil_error}): not shown.")
+    elif has_soil and not soil and sensors is not None:
+        notes.append("No soil moisture data in the last month: not shown.")
+    return notes
+
+
+def analyse_station(daily: pd.DataFrame, run_day: date, station_id: int,
+                    sensors: set[str] | None = None, soil_rows: pd.DataFrame | None = None,
+                    soil_error: str | None = None) -> dict:
+    """Station-level result of the window: status, severity, details, notes."""
+    months = window_months(run_day)
+    label = months_label(months)
+    normals = normals_for(station_id, months)
+    if normals is None:
+        return {"status": NO_DATA, "details": {"reason": "no climate average for this station"},
+                "notes": [f"No average rain for station {station_id} in thresholds.RAIN_NORMALS_MM: "
+                          f"the rain of {label} cannot be compared with its average."]}
+    grid = daily_grid(daily, run_day)
+    table = month_table(grid, months, normals)
+    soil = {} if soil_error else soil_summary(soil_rows, run_day, sensors)
+    notes = data_notes(table, sensors, soil, soil_error)
+    other = [n for n in notes if not n.startswith("Rain missing")]   # for no_data results
+    days = sum(m["days"] for m in table)
+    missing = sum(m["missing_days"] for m in table)
+    if missing == days:
+        return {"status": NO_DATA, "details": {"reason": "no daily rain data in the window"},
+                "notes": [f"No daily rain data for {label}: drought cannot be judged."] + other}
+    if 100 * missing / days > DROUGHT["max_missing_days_pct"]:
+        return {"status": NO_DATA,
+                "details": {"reason": "too many days without rain data", "missing_days": missing,
+                            "days": days},
+                "notes": [f"Rain missing for {missing} of {days} days of {label} (more than "
+                          f"{DROUGHT['max_missing_days_pct']:g} %): a deficit could be only missing "
+                          f"data, so drought is not judged."] + other}
+    rain = float(grid["rain_mm"].sum())                  # unrounded (the table is rounded)
+    normal = sum(normals)
+    pct = 100 * rain / normal
+    both = grid["rain_mm"].notna() & grid["et0_mm"].notna()
+    et0 = [m for m in table if m["et0_mm"] is not None]
+    last = table[-1]
+    details = {
+        "months": table,
+        "window_label": label,
+        "window_months": MONTHS,
+        "days": days,
+        "missing_days": missing,
+        "rain_mm": _round(rain),
+        "normal_mm": _round(normal),
+        "rain_pct_of_normal": _round(pct),
+        "deficit_mm": _round(normal - rain),
+        "trigger_pct": TRIGGER_PCT,
+        "trigger_op": TRIGGER_OP,
+        "last_month_label": last["label"],
+        "last_month_rain_mm": last["rain_mm"],
+        "last_month_normal_mm": last["normal_mm"],
+        "last_month_pct_of_normal": last["rain_pct_of_normal"],
+        "et0_mm": _round(grid.loc[both, "et0_mm"].sum()) if et0 else None,
+        "water_balance_mm": (_round(grid.loc[both, "rain_mm"].sum() - grid.loc[both, "et0_mm"].sum())
+                             if et0 else None),
+        "water_balance_months": len(et0),
+        "soil_moisture": soil or None,
+        "normals_source": RAIN_NORMALS_SOURCE,
+    }
+    if not OPS[TRIGGER_OP](pct, TRIGGER_PCT):
+        return {"status": NO_EVENT, "details": details, "notes": notes}
+    severity, score = grade(pct, DROUGHT_BANDS_PCT)
+    start, end = window(run_day)
+    return {"status": EVENT, "details": details, "notes": notes, "severity": severity,
+            "score": score, "duration_h": float(days * 24), "start": start, "end": end}
+
+
+def _summary(res) -> str:
+    if res["status"] == NO_DATA:
+        return res["notes"][0] if res.get("notes") else "No usable rain data from the station."
+    d = res["details"]
+    text = (f"Rain in {d['window_label']}: {d['rain_mm']:g} mm, {d['rain_pct_of_normal']:g} % of the "
+            f"average ({d['normal_mm']:g} mm)")
+    if res["status"] == NO_EVENT:
+        return text + f": no drought (drought at ≤ {TRIGGER_PCT:g} % of the average)."
+    text = (f"Dry period: {d['rain_mm']:g} mm of rain in {d['window_label']}, only "
+            f"{d['rain_pct_of_normal']:g} % of the average ({d['normal_mm']:g} mm, "
+            f"{d['deficit_mm']:g} mm below it).")
+    if d["last_month_pct_of_normal"] is not None:
+        text += f" {d['last_month_label']}: {d['last_month_pct_of_normal']:g} % of its average."
+    return text
+
+
+def detect(daily: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None,
+           soil: pd.DataFrame | None = None, soil_error: str | None = None) -> list[Analysis]:
+    """One Analysis per field: the rain of its station's window against the climate average.
+
+    daily: daily rows (CodiceStazione, Data, Pioggia, EvapoPot) of the window; soil: hourly
+    rows (CodiceStazione, Timestamp, Suolo1, Suolo2) of the last month, or None.
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    groups = {int(k): v for k, v in daily.groupby("CodiceStazione")} if not daily.empty else {}
+    soil_groups = ({int(k): v for k, v in soil.groupby("CodiceStazione")}
+                   if soil is not None and not soil.empty else {})
+    for sid in {f.station_id for f in fields}:
+        rows = groups.get(sid, pd.DataFrame(columns=["CodiceStazione", COL_DAY, *DAILY_COLUMNS]))
+        by_station[sid] = analyse_station(rows, run_day, sid, station_sensors.get(sid),
+                                          soil_groups.get(sid), soil_error)
+    start, end = window(run_day)
+    period = {"window_start": start.isoformat(timespec="minutes"),     # for the report: the
+              "window_end": end.isoformat(timespec="minutes"),         # months checked
+              "window_label": months_label(window_months(run_day))}
+    out = []
+    for f in fields:
+        res = by_station[f.station_id]
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"],
+            severity=res.get("severity", Severity.NONE),
+            severity_score=res.get("score", 0.0),
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res), details={**res["details"], **period},
+            data_notes=list(res.get("notes", [])),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the daily rain and ET0 of the window (and the last month's soil moisture where a
+    station has the sensor) and compare the rain with its climate average."""
+    run_day = run_day or date.today()
+    start, end = window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    daily = fetch_daily(ids, start, end - timedelta(minutes=1), DAILY_COLUMNS) if ids else pd.DataFrame()
+    soil_ids = [i for i in ids if any(stations[i].has(s) for s in SOIL_SENSORS)]
+    soil, soil_error = None, None
+    if soil_ids:
+        last_month = datetime.combine(window_months(run_day)[-1], time())
+        try:
+            soil = fetch_hourly(soil_ids, last_month, end, list(SOIL_SENSORS))
+        except Exception as e:                       # information only: never stops the block
+            log.warning("Drought: soil moisture request failed: %s", e)
+            soil_error = type(e).__name__
+    out = detect(daily, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()}, soil, soil_error)
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: drought cannot be judged." if known is None
+                    else "No rain gauge (Pioggia) at this station: drought cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no rain gauge.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n = sum(a.detected for a in out)
+    log.info("Drought %s (%s): dry period for %d of %d field(s), %d station(s)",
+             run_day, months_label(window_months(run_day)), n, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Drought detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day (default: today); the "
+                   "complete months before it are checked")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/flood.py b/src/damage_engine/detectors/flood.py
new file mode 100644
index 0000000..087b51c
--- /dev/null
+++ b/src/damage_engine/detectors/flood.py
@@ -0,0 +1,365 @@
+"""
+Flood detector = contract "excessive rain" (eccesso di pioggia). Runs every
+24 h on the day just ended.
+
+Contract (UnipolSai Model 1506, p.8): excessive rain is ANY ONE of three
+criteria. The "Flood" box of Diagram.png is this peril: flooding by rivers
+(alluvione) is not covered for apples, and the box only gets the rain gauge.
+All values are read from damage_engine.thresholds; sources as tagged there:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Input (Diagram.png): rain gauge only, 3A "Pioggia" (hourly rain, mm).
+
+Event day - FLOOD_SCHEDULE [PROJECT]: the 24 h ending at run_hour = 07:00 of
+the run day (records after 07:00 of the previous day up to 07:00 of the run
+day). lookback_days = 10 days of hourly rain are fetched (one API request) so
+the 72 h and 10-day sums can be computed for windows ending in the event day.
+
+Criteria (EXCESSIVE_RAIN), each with the contract operator from its "_op" key:
+  - downpour:  one hourly record >= downpour_1h_mm = 30 mm  [CONTRACT p.8]
+    ("at least 30 mm over 1 hour"; hourly data are clock hours, so a downpour
+    split over two hours can be missed).
+  - intense:   rain in 72 h >= intense_72h_mm = 80 mm  [CONTRACT p.8]
+    ("at least 80 mm within 72 hours"), rolling 72 h sums.
+  - prolonged: 10-day rain > prolonged_10d_ratio = 1.5 x the 5-year average of
+    the same period AND >= prolonged_10d_min_mm = 80 mm  [CONTRACT p.8]
+    ("exceeding the 5-year average by more than 50%, in no case less than 80 mm").
+    Needs a reference average (detect(..., reference_10d_mm=...)). 3A gives
+    hourly data for only ~180 days and fetch_daily does not work yet, so there
+    is no average today: the criterion is not applied and a data note says so.
+A criterion counts only for a window that ends on rain fallen during the event
+day, so a dry day after heavy rain does not report the same event again.
+
+Claim helper: reported only, it does not decide the event. the latest 72 h window in the last
+4 days whose rain reaches intense_72h_mm = 80 mm.:
+  - details["wettest_72h"]: the wettest 72 h window in those days.
+
+Severity, per field: rainfall / trigger of the criterion exceeded the most
+(prolonged: trigger = max(1.5 x average, 80 mm)), graded with
+EXCESSIVE_RAIN_BANDS_RATIO (ratio >= 1.0/1.5/2.0/3.0 -> light/moderate/severe/
+extreme) [ENGINEERING]. Same for every stage: the contract gives no
+stage-dependent rain damage.
+
+Cover window: COVER_WINDOWS["excessive_rain"] = fruit set -> 31 Oct
+[CONTRACT Special Art.5 p.31]; rain during flowering is excluded (Art.5 q),
+which the window already does. Fruit set = BBCH above the highest
+"post_bloom" code of APPLE_BBCH_TO_STAGE [ENGINEERING, approx.].
+
+Missing sensors / missing values - the detector still runs:
+  - rain is essential: a station without a rain gauge (none since 2026-10-21,
+    all 14 stations have one) gives
+    "no_data" ("station lacks required sensors"), and so does an event day
+    with no rain record at all.
+  - missing hours are counted as 0 mm, so every total is a lower bound: an
+    event found is real, a "no event" may miss rain. Missing hours of the
+    event day and of the days before it are written to Analysis.data_notes.
+  - no 5-year reference average: prolonged criterion not applied (data note).
+  Missing rows are read as empty (the hourly grid is rebuilt), never an error.
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    COVER_WINDOWS,
+    EXCESSIVE_RAIN,
+    EXCESSIVE_RAIN_BANDS_RATIO,
+    FLOOD_SCHEDULE,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "flood"
+PHENOMENON = "excessive_rain"         # contract name, key in COVER_WINDOWS
+RUN_HOUR = FLOOD_SCHEDULE["run_hour"]
+LOOKBACK_DAYS = FLOOD_SCHEDULE["lookback_days"]
+CLAIM_SEARCH_DAYS = FLOOD_SCHEDULE["claim_search_days"]
+HOURS_PER_RECORD = 1.0                # hourly data
+HOUR = timedelta(hours=1)
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("Pioggia",)
+COL_RAIN = "Pioggia"                  # hourly rain, mm
+COLUMNS = [COL_RAIN]
+
+OPS = {">=": operator.ge, ">": operator.gt, "<=": operator.le, "<": operator.lt}
+
+
+def _met(value, key, op_key):
+    """Contract comparison: value <op> EXCESSIVE_RAIN[key], operator from EXCESSIVE_RAIN[op_key]."""
+    return value is not None and OPS[EXCESSIVE_RAIN[op_key]](value, EXCESSIVE_RAIN[key])
+
+
+# ─────────────────────────── windows ───────────────────────────
+
+def event_window(run_day: date):
+    """(start, end): the event day is the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+def fetch_window(run_day: date):
+    """[start, end] to fetch: the event day plus the look-back for the 72 h / 10-day sums."""
+    _, end = event_window(run_day)
+    return end - timedelta(days=LOOKBACK_DAYS) + HOUR, end
+
+
+def fruit_set(bbch: int) -> bool:
+    """Fruit set (start of the excessive-rain cover): BBCH above the last post-bloom code."""
+    return bbch > max(c for c, s in APPLE_BBCH_TO_STAGE.items() if s == "post_bloom")
+
+
+def in_cover_window(run_day: date, bbch: int | None):
+    """Contract cover window (fruit set -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if run_day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return fruit_set(bbch)
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def hourly_rain(rows: pd.DataFrame, run_day: date) -> pd.Series:
+    """Rain of one station on the full hourly grid of the fetch window (NaN = missing hour)."""
+    start, end = fetch_window(run_day)
+    grid = pd.date_range(start, end, freq="h")
+    rain = pd.Series(_col(rows, COL_RAIN).to_numpy(dtype=float),
+                     index=pd.DatetimeIndex(rows["Timestamp"]))
+    rain = rain[~rain.index.duplicated()]
+    return rain.reindex(grid)
+
+
+def _round(x):
+    return None if x is None else round(float(x), 1)
+
+
+def data_notes(rain: pd.Series, run_day: date, reference_mm: float | None) -> list[str]:
+    """Missing data of one station and how it was handled."""
+    day_start, _ = event_window(run_day)
+    day, before = rain[rain.index > day_start], rain[rain.index <= day_start]
+    notes = []
+    if day.isna().any() and day.notna().any():
+        notes.append(f"Rain missing for {int(day.isna().sum())} of {len(day)} hours of the event day: "
+                     f"counted as 0 mm, so the totals are lower bounds.")
+    if before.isna().any():
+        notes.append(f"Rain missing for {int(before.isna().sum())} of {len(before)} hours of the "
+                     f"{LOOKBACK_DAYS - 1} days before the event day: counted as 0 mm, "
+                     f"so the 72 h and 10-day sums are lower bounds.")
+    if reference_mm is None:
+        notes.append(f"No 5-year reference average for the 10-day rain: prolonged-rain criterion "
+                     f"not applied (10-day total {rain.fillna(0).sum():.1f} mm).")
+    return notes
+
+
+def _iso(t) -> str:
+    return t.to_pydatetime().isoformat(timespec="minutes")
+
+
+def _window_info(watch: pd.Series, sum_72h: pd.Series, end, earlier: pd.Series) -> dict:
+    """Total, first/last rainy hour of the 72 h window ending at `end` (rain of the watch only).
+
+    earlier: rain before the watch period; "partial" = some of it falls in this window's 72 h
+    (not counted, so the storm may have started before the days checked).
+    """
+    window = watch[end - 71 * HOUR:end]                 # 72 hourly records
+    wet = window[window > 0].index
+    return {"mm": _round(sum_72h[end]), "first_rain": _iso(wet.min()), "last_rain": _iso(wet.max()),
+            "partial": bool(earlier[end - 71 * HOUR:].sum() > 0)}
+
+
+def heavy_72h(filled: pd.Series) -> dict:
+    """Claim helper (Heavy Rain Watch): only the rain of the last CLAIM_SEARCH_DAYS counts
+    (asked by the user 2026-10-26, so the row never shows rain from before those days).
+    Latest 72 h window in that period that reaches intense_72h_mm, and the wettest one.
+
+    filled: hourly rain of the look-back (missing hours = 0).
+    """
+    out = {"last_heavy_72h": None, "wettest_72h": None}
+    search_from = filled.index[-1] - timedelta(days=CLAIM_SEARCH_DAYS)
+    inside = filled.index > search_from                  # records of the watch period
+    watch = filled.where(inside, 0.0)                    # rain before the period is not counted
+    earlier = filled.where(~inside, 0.0)
+    sum_72h = watch.rolling(72, min_periods=1).sum()
+    ends = sum_72h[inside]                               # windows ending in the watch period
+    if not ends.max() > 0:
+        return out
+    out["wettest_72h"] = _window_info(watch, sum_72h, ends.idxmax(), earlier)   # first max: ends on rain
+    # round away float noise of the rolling sum (e.g. 79.99999999 for 80 mm)
+    hit = ends.round(6).apply(lambda v: _met(v, "intense_72h_mm", "intense_72h_op"))
+    if hit.any():
+        last = hit[hit].index.max()
+        upto = hit[:last]
+        below = upto[~upto]
+        first = below.index.max() + HOUR if len(below) else upto.index[0]   # start of that run
+        run = sum_72h[first:last]
+        info = _window_info(watch, sum_72h, run.idxmax(), earlier)
+        info["reached_at"] = _iso(first)
+        out["last_heavy_72h"] = info
+    return out
+
+
+def analyse_station(rain: pd.Series, run_day: date, reference_mm: float | None = None) -> dict:
+    """Station-level result of one event day: status, criteria met, rain totals, data notes."""
+    day_start, day_end = event_window(run_day)
+    notes = data_notes(rain, run_day, reference_mm)
+    day = rain[rain.index > day_start]
+    if day.notna().sum() == 0:
+        return {"status": NO_DATA, "details": {"reason": "no rain records in the event day"},
+                "notes": [f"No rain data for the event day ({day_start:%Y-%m-%d %H:%M} -> "
+                          f"{day_end:%Y-%m-%d %H:%M})."] + notes}
+
+    filled = rain.fillna(0.0)
+    sum_72h = filled.rolling(72, min_periods=1).sum()   # grid is complete: 72 rows = 72 h
+    rainy = day[day > 0].index                          # windows must end on rain of the event day
+    max_1h = float(day.max())
+    max_72h = float(sum_72h[rainy].max()) if len(rainy) else None
+    rain_10d = float(filled.sum())
+
+    ratios = {}
+    if _met(max_1h, "downpour_1h_mm", "downpour_1h_op"):
+        ratios["downpour_1h"] = max_1h / EXCESSIVE_RAIN["downpour_1h_mm"]
+    if _met(max_72h, "intense_72h_mm", "intense_72h_op"):
+        ratios["intense_72h"] = max_72h / EXCESSIVE_RAIN["intense_72h_mm"]
+    if reference_mm and len(rainy):
+        ratio_avg = rain_10d / reference_mm
+        if (_met(ratio_avg, "prolonged_10d_ratio", "prolonged_10d_ratio_op")
+                and _met(rain_10d, "prolonged_10d_min_mm", "prolonged_10d_min_op")):
+            trigger = max(EXCESSIVE_RAIN["prolonged_10d_ratio"] * reference_mm,
+                          EXCESSIVE_RAIN["prolonged_10d_min_mm"])
+            ratios["prolonged_10d"] = rain_10d / trigger
+
+    details = {"records": int(day.notna().sum()),
+               "rain_day_mm": _round(day.sum()),
+               "max_1h_mm": _round(max_1h),
+               "max_72h_mm": _round(max_72h),
+               "rain_72h_to_run_mm": _round(sum_72h.iloc[-1]),
+               "rain_10d_mm": _round(rain_10d),
+               "reference_10d_mm": _round(reference_mm),
+               **heavy_72h(filled)}
+    if not ratios:
+        return {"status": NO_EVENT, "details": details, "notes": notes}
+    worst = max(ratios, key=ratios.get)
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": len(rainy) * HOURS_PER_RECORD,
+        "start": rainy.min().to_pydatetime(),
+        "end": rainy.max().to_pydatetime(),
+        "ratio": ratios[worst],
+        "details": {**details, "criteria_met": sorted(ratios), "worst_criterion": worst,
+                    "rain_trigger_ratio": round(ratios[worst], 3)},
+    }
+
+
+CRITERION_TEXT = {
+    "downpour_1h": lambda d: f"{d['max_1h_mm']} mm in 1 h",
+    "intense_72h": lambda d: f"{d['max_72h_mm']} mm in 72 h",
+    "prolonged_10d": lambda d: f"{d['rain_10d_mm']} mm in 10 days (average {d['reference_10d_mm']} mm)",
+}
+
+
+def _summary(res, sev):
+    if res["status"] == NO_DATA:
+        return "No rain data from the station for the last 24 h."
+    d = res["details"]
+    if res["status"] == NO_EVENT:
+        return (f"No excessive rain: {d['rain_day_mm']} mm in the last 24 h, "
+                f"{d['rain_72h_to_run_mm']} mm in 72 h.")
+    met = ", ".join(CRITERION_TEXT[c](d) for c in d["criteria_met"])
+    return f"Excessive rain ({met}): severity {sev.value}."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           reference_10d_mm: dict[int, float] | None = None) -> list[Analysis]:
+    """One Analysis per field: the event day of its station.
+
+    df: hourly rows (CodiceStazione, Timestamp, Pioggia) covering fetch_window(run_day).
+    reference_10d_mm: 5-year average rain of the same 10 days per station; without
+    it the prolonged-rain criterion is not applied.
+    """
+    station_names = station_names or {}
+    reference_10d_mm = reference_10d_mm or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            sid = int(sid)
+            by_station[sid] = analyse_station(hourly_rain(rows, run_day), run_day,
+                                              reference_10d_mm.get(sid))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No rain records from the station for the last "
+                                                      f"{LOOKBACK_DAYS} days."]})
+        sev, score = Severity.NONE, 0.0
+        details = dict(res["details"])
+        if res["status"] == EVENT:
+            sev, score = grade(res["ratio"], EXCESSIVE_RAIN_BANDS_RATIO)
+            details["in_cover_window"] = in_cover_window(run_day, f.bbch)
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev), details=details,
+            data_notes=res.get("notes", []),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the last LOOKBACK_DAYS of hourly rain for every field's station and detect."""
+    run_day = run_day or date.today()
+    start, end = fetch_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: Flood cannot be judged." if known is None
+                    else "No rain gauge (Pioggia) at this station: Flood cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no rain gauge.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("Flood %s: %d event(s) over %d field(s), %d station(s)", run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Flood detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/freezing.py b/src/damage_engine/detectors/freezing.py
new file mode 100644
index 0000000..ee18d02
--- /dev/null
+++ b/src/damage_engine/detectors/freezing.py
@@ -0,0 +1,404 @@
+"""
+Freezing (gelo) detector: air below 0 °C in DAYLIGHT. Runs every 24 h.
+
+Split with Brina: Brina checks the night, Freezing checks
+the daylight. For one run day both blocks look at the same 24 h and every hour
+belongs to exactly one of them:
+  - Brina:    the hours of its night window (BRINA_SCHEDULE: 18:00 -> 06:00,
+              records 18:00 ... 06:00) that are dark (RadiazioneSolareMax
+              <= BRINA["night_solar_max_wm2"], or no solar value).
+  - Freezing: every other hour = the day hours before the night window
+              (records 07:00 ... 17:00), plus the sunlit hours of the night
+              window (RadiazioneSolareMax > night_solar_max_wm2 = 10 W/m²
+              [ENGINEERING], the hours Brina drops as daytime; in April-May
+              often 18:00-20:00 and 06:00).
+Both limits are Brina's settings in thresholds.py (BRINA_SCHEDULE [PROJECT],
+BRINA["night_solar_max_wm2"]), read here and not copied, so a change there
+moves both blocks and leaves no hour unchecked or checked twice.
+
+Contract (UnipolSai Model 1506, p.7): gelo = "a drop in temperature below 0 °C
+caused by cold air masses". In daylight the sun stops the radiative cooling of
+the crop (the cause of brina), so sub-zero air during the day comes from a cold
+air mass. All values are read from damage_engine.thresholds; sources as tagged there:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [LITERATURE]  WSU EB0913 / MSU "Critical spring temperatures for tree fruit bud stages"
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Inputs (Diagram.png): temperature, solar radiation, wet-bulb temperature,
+humidity, wind speed.
+  - temperature: hourly minimum (TemperaturaAriaMin), for detection and severity.
+  - solar radiation: RadiazioneSolareMax (highest irradiance of the hour, W/m²,
+    stations 544, 566, 600) finds the sunlit hours of the night window (above).
+  - wind (VelocitaVento2mMedia): reported only. The mean wind of the freezing
+    hours is compared with GELO["advective_wind_min_ms"] = 2.0 m/s [ENGINEERING]
+    (details["advective_wind"]): it shows whether wind backs the contract's
+    "cold air masses". Not needed to detect: in daylight any sub-zero hour
+    counts, with or without wind (and a windy sub-zero night hour is Brina's).
+  - wet-bulb and humidity: reported only. The lowest WetBulbMin of the freezing
+    hours (damage_engine.sources.derived, 3A document [3A-DOC] section 4, the
+    same value as in Brina): a wet bud (rain, dew) cools by evaporation towards
+    the wet-bulb, so it shows how cold wet buds could get. Not used to detect or
+    grade: the contract trigger is the air temperature, and a "wet-bulb <= 0 °C"
+    test would change nothing, because the wet-bulb is never warmer than the air
+    (equal at 100 % humidity): whenever the air is below 0 °C, so is the wet-bulb.
+
+A daylight hour is a freezing hour when (GELO):
+  - air temperature < t_air_trigger_c = 0.0 °C, operator t_air_trigger_op = "<"
+    [CONTRACT p.7, "a drop in temperature below 0 °C"]; exactly 0 °C does not count.
+Event when the freezing hours reach min_duration_h = 0.5 h [LITERATURE]
+(critical temperatures refer to ~30 min exposure), so one hourly record is enough.
+
+Bud temperature = air temperature: in daylight the buds do not cool below the
+air by radiation (Brina's radiative_offset_c is for calm nights only).
+
+Severity, per field, from the lowest air temperature of the daylight hours
+(same frost tables as Brina):
+  - BBCH < APPLE_DORMANT_BELOW_BBCH = 51: dormant, severity none [ENGINEERING].
+  - with a BBCH stage: stage from APPLE_BBCH_TO_STAGE [ENGINEERING, approx.],
+    T10/T90 bud kill temperatures from APPLE_CRITICAL_TEMPS_C [LITERATURE, WSU/MSU],
+    kill fraction from FROST_KILL_CURVE (0% at T10 + 1 °C, 100% at T90 - 2 °C)
+    [ENGINEERING], graded with FROST_KILL_BANDS (kill >= 0.01%/10%/50%/90% ->
+    light/moderate/severe/extreme) [ENGINEERING].
+  - without a stage: FROST_FALLBACK_BANDS_C (temperature < 0/-2/-4/-6 °C ->
+    light/moderate/severe/extreme) [ENGINEERING].
+
+Missing sensors / missing values - the detector still runs:
+  - temperature is essential: a station without a temperature sensor gives
+    "no_data" ("station lacks required sensors"); daylight hours without a
+    temperature (no record, or an empty value) are not checked; a day with no
+    usable daylight hour gives "no_data".
+  - no hourly minimum temperature: the hourly mean temperature is used.
+  - no pyranometer (all stations except 544, 566 and 600), or no solar value
+    in an hour of the night window: that hour counts as night and is left to
+    Brina (Brina counts it as night too). Daylight = the fixed day hours only.
+  - no wind sensor (all stations except 3, 6, 10, 30, 544, 566, 600) or no wind
+    value: wind not reported (details["advective_wind"] = None); detection unchanged.
+  - no humidity value: no wet-bulb for that hour (reported only); detection unchanged.
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no
+  sensor causes an error. Every case is written to Analysis.data_notes (sensor
+  missing at the station, no data, or N of M hours missing, plus what was done).
+
+Wind does not decide the block: a windy sub-zero day hour
+is freezing, a windy sub-zero night hour is Brina's (Brina has no calm-air test).
+
+Cover window: COVER_WINDOWS["gelo"] = budburst -> 10 May [CONTRACT Special Art.5 p.31],
+checked on the day of the first freezing hour.
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    APPLE_CRITICAL_TEMPS_C,
+    APPLE_DORMANT_BELOW_BBCH,
+    BRINA,
+    BRINA_SCHEDULE,
+    COVER_WINDOWS,
+    FROST_FALLBACK_BANDS_C,
+    FROST_KILL_BANDS,
+    FROST_KILL_CURVE,
+    GELO,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "freezing"
+PHENOMENON = "gelo"                   # contract name, key in COVER_WINDOWS
+RUN_HOUR = BRINA_SCHEDULE["run_hour"]               # the 24 h end where Brina's night ends
+NIGHT_START_HOUR = BRINA_SCHEDULE["night_start_hour"]   # Brina's night window starts here
+NIGHT_SOLAR_MAX_WM2 = BRINA["night_solar_max_wm2"]      # above = sunlit, Freezing's hour
+HOURS_PER_RECORD = 1.0                # hourly data
+HOUR = timedelta(hours=1)
+FIXED_DAY_TEXT = f"records {RUN_HOUR + 1:02d}:00-{NIGHT_START_HOUR - 1:02d}:00"
+NIGHT_TEXT = f"{NIGHT_START_HOUR:02d}:00-{RUN_HOUR:02d}:00"
+
+OPS = {"<": operator.lt, "<=": operator.le, ">": operator.gt, ">=": operator.ge}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("TemperaturaAria",)
+SENSOR_WIND = "VelocitaVento2m"       # optional sensors (3A sensor names)
+SENSOR_SOLAR = "RadiazioneSolare"
+SENSOR_RH = "UmiditaRelativa"
+COL_TEMP = "TemperaturaAriaMin"       # hourly minimum air temperature, °C
+COL_TEMP_FALLBACK = "TemperaturaAriaMedia"
+COL_RH = "UmiditaRelativaMedia"       # %
+COL_RH_MAX = "UmiditaRelativaMax"     # %, wet-bulb of the coldest moment (derived.WET_BULB_MIN)
+COL_WIND = "VelocitaVento2mMedia"     # m/s
+COL_SOLAR = "RadiazioneSolareMax"     # W/m², highest irradiance of the hour; stations 544, 566, 600
+COLUMNS = list(dict.fromkeys([COL_TEMP, COL_TEMP_FALLBACK, COL_RH, COL_RH_MAX,
+                               *derived.WET_BULB_INPUTS, COL_WIND, COL_SOLAR]))
+
+
+# ─────────────────────────── frost severity (same tables as Brina) ───────────────────────────
+
+def stage_for(bbch: int) -> str:
+    """Apple bud stage for a BBCH code (first stage whose highest code is >= bbch)."""
+    return next(APPLE_BBCH_TO_STAGE[c] for c in sorted(APPLE_BBCH_TO_STAGE) if bbch <= c)
+
+
+def kill_fraction(bud_temp_c: float, stage: str) -> float:
+    """Fraction of buds killed: 0 at T10+offset, 10% at T10, 90% at T90, 100% at T90-offset."""
+    t10, t90 = APPLE_CRITICAL_TEMPS_C[stage]
+    xs = [t90 - FROST_KILL_CURVE["full_kill_offset_c"], t90, t10,
+          t10 + FROST_KILL_CURVE["no_damage_offset_c"]]
+    return float(np.interp(bud_temp_c, xs, [1.0, 0.90, 0.10, 0.0]))
+
+
+def field_severity(min_temp_c: float, bbch: int | None):
+    """(Severity, score, details) for one field from the lowest daylight air (= bud) temperature."""
+    if bbch is None:
+        sev, score = grade(min_temp_c, FROST_FALLBACK_BANDS_C)
+        return sev, score, {"severity_method": "fallback_min_bud_temp"}
+    if bbch < APPLE_DORMANT_BELOW_BBCH:
+        return Severity.NONE, 0.0, {"severity_method": "dormant", "bbch": bbch, "kill_fraction": 0.0}
+    stage = stage_for(bbch)
+    kill = kill_fraction(min_temp_c, stage)
+    sev, score = grade(kill, FROST_KILL_BANDS)
+    return sev, score, {"severity_method": "kill_fraction", "bbch": bbch, "stage": stage,
+                        "kill_fraction": round(kill, 3)}
+
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (budburst -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return bbch >= APPLE_DORMANT_BELOW_BBCH       # approx.: buds out of dormancy = budburst
+
+
+# ─────────────────────────── windows ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+def night_start(run_day: date) -> datetime:
+    """First record of Brina's night window (the hours from here to the end are Brina's when dark)."""
+    return datetime.combine(run_day - timedelta(days=1), time(NIGHT_START_HOUR))
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def day_hours(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's rows on the full hourly grid of the 24 h (missing hour = empty row), with
+    temp_c, wet_bulb_c and the booleans night_window, sunlit, daylight and freezing."""
+    start, end = day_window(run_day)
+    grid = pd.date_range(start + HOUR, end, freq="h", name="Timestamp")
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()   # rows outside the 24 h dropped
+    df = derived.add_wet_bulb(df)                 # WetBulbMin / WetBulbMax, same in every block
+    df["temp_c"] = _col(df, COL_TEMP).fillna(_col(df, COL_TEMP_FALLBACK))
+    df["wet_bulb_c"] = df[derived.WET_BULB_MIN]   # lowest wet-bulb of the hour
+    df["night_window"] = df["Timestamp"] >= night_start(run_day)
+    # exact opposite of Brina's night test (solar <= limit or no value = night)
+    df["sunlit"] = df["night_window"] & (_col(df, COL_SOLAR) > NIGHT_SOLAR_MAX_WM2)
+    df["daylight"] = ~df["night_window"] | df["sunlit"]
+    below = OPS[GELO["t_air_trigger_op"]](df["temp_c"], GELO["t_air_trigger_c"])   # NaN -> False
+    df["freezing"] = df["daylight"] & below
+    return df
+
+
+def _gap_note(values: pd.Series, label: str, has_sensor: bool | None, effect: str) -> str | None:
+    """One data note for an optional input over the checked daylight hours."""
+    n, missing = len(values), int(values.isna().sum())
+    if has_sensor is False:
+        return f"No {label} sensor at this station: {effect}."
+    if n == 0:
+        return None
+    if missing == n:
+        return f"No {label} data in yesterday's daylight hours: {effect}."
+    if missing:
+        return f"{label.capitalize()} missing for {missing} of {n} daylight hours: {effect} for those hours."
+    return None
+
+
+def data_notes(df: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing sensors / data of one station day and how they were handled.
+
+    df = day_hours(); `sensors` = the station's 3A sensor names (None = unknown:
+    only the data is checked).
+    """
+    def has(sensor):
+        return None if sensors is None else sensor in sensors
+
+    notes = []
+    night_solar = _col(df[df["night_window"]], COL_SOLAR)
+    fixed_only = f"daylight = the fixed day hours ({FIXED_DAY_TEXT}), the night window {NIGHT_TEXT} is left to Brina"
+    if has(SENSOR_SOLAR) is False:
+        notes.append(f"No solar radiation sensor at this station: {fixed_only}.")
+    elif night_solar.isna().all():
+        notes.append(f"No solar radiation data in the night window: {fixed_only}.")
+    elif night_solar.isna().any():
+        notes.append(f"Solar radiation missing for {int(night_solar.isna().sum())} of {len(night_solar)} "
+                     f"hours of the night window: those hours counted as night (left to Brina).")
+
+    day = df[df["daylight"]]
+    n_day = len(day)
+    no_temp = int(day["temp_c"].isna().sum())
+    if no_temp:
+        notes.append(f"Air temperature missing for {no_temp} of {n_day} daylight hours: "
+                     f"those hours not checked.")
+    checked = day[day["temp_c"].notna()]
+    mean_used = int((_col(checked, COL_TEMP).isna() & _col(checked, COL_TEMP_FALLBACK).notna()).sum())
+    if mean_used:
+        notes.append(f"Hourly minimum temperature missing for {mean_used} of {len(checked)} daylight "
+                     f"hours: hourly mean used.")
+    rh = _col(checked, COL_RH_MAX).fillna(_col(checked, COL_RH))
+    notes += [
+        _gap_note(_col(checked, COL_WIND), "wind", has(SENSOR_WIND),
+                  "wind during the frost not reported (not needed to detect daylight freezing)"),
+        _gap_note(rh, "humidity", has(SENSOR_RH),
+                  "wet-bulb not reported (not needed to detect daylight freezing)"),
+    ]
+    return [x for x in notes if x]
+
+
+def _round(x, digits=2):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def analyse_station(df: pd.DataFrame, sensors: set[str] | None = None) -> dict:
+    """Station-level result of one day: status, duration, times, measured extremes, data notes."""
+    notes = data_notes(df, sensors)
+    day = df[df["daylight"] & df["temp_c"].notna()]
+    if day.empty:
+        return {"status": NO_DATA, "details": {"reason": "no temperature records in the daylight hours"},
+                "notes": notes}
+    hits = day[day["freezing"]]
+    base = {"records": int(len(day)),
+            "daylight_hours": int(df["daylight"].sum()),
+            "sunlit_hours_in_night_window": int(df["sunlit"].sum()),
+            "solar_checked": bool(_col(df[df["night_window"]], COL_SOLAR).notna().any()),
+            "day_min_temp_c": _round(day["temp_c"].min())}
+    duration = len(hits) * HOURS_PER_RECORD
+    if duration < GELO["min_duration_h"]:
+        return {"status": NO_EVENT, "details": base, "notes": notes}
+    wind = _col(hits, COL_WIND).dropna()
+    mean_wind = float(wind.mean()) if not wind.empty else None
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": duration,
+        "start": hits["Timestamp"].min().to_pydatetime(),
+        "end": hits["Timestamp"].max().to_pydatetime(),
+        "min_temp_c": float(hits["temp_c"].min()),
+        "details": {**base,
+                    "mean_wind_ms": _round(mean_wind, 1),
+                    "advective_wind": (None if mean_wind is None
+                                       else bool(mean_wind >= GELO["advective_wind_min_ms"])),
+                    "min_wet_bulb_c": _round(hits["wet_bulb_c"].min())},
+    }
+
+
+def _summary(res, sev, sdet):
+    if res["status"] == NO_DATA:
+        return "No temperature data from the station for yesterday's daylight hours."
+    d = res["details"]
+    if res["status"] == NO_EVENT:
+        return f"No daylight freezing: lowest daytime air temperature {d['day_min_temp_c']} °C."
+    text = (f"Daylight freezing for {res['duration_h']:g} h, lowest air temperature "
+            f"{d['day_min_temp_c']} °C: severity {sev.value}")
+    if "kill_fraction" in sdet:
+        text += f" (stage {sdet.get('stage', 'dormant')}, about {sdet['kill_fraction']:.0%} buds killed)"
+    return text + "."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None) -> list[Analysis]:
+    """One Analysis per field: the daylight hours of its station, graded with the field's stage.
+
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering day_window(run_day);
+    rows outside it are ignored. station_sensors: 3A sensor names per station, so the
+    data notes can tell "no sensor" from "no data"; without it only the data is checked.
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            sid = int(sid)
+            by_station[sid] = analyse_station(day_hours(rows, run_day), station_sensors.get(sid))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station for yesterday's "
+                                                      "daylight hours."]})
+        sev, score, sdet = Severity.NONE, 0.0, {}
+        details = dict(res["details"])
+        if res["status"] == EVENT:
+            sev, score, sdet = field_severity(res["min_temp_c"], f.bbch)
+            details.update(sdet)
+            details["in_cover_window"] = in_cover_window(res["start"].date(), f.bbch)
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev, sdet), details=details,
+            data_notes=res.get("notes", []),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch yesterday's 24 h of hourly data for every field's station and detect daylight freezing."""
+    run_day = run_day or date.today()
+    start, end = day_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start + HOUR, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: Freezing cannot be judged." if known is None
+                    else "No air temperature sensor (TemperaturaAria) at this station: "
+                         "Freezing cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no air temperature sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("Freezing %s: %d event(s) over %d field(s), %d station(s)",
+             run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Freezing detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run morning to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/hail.py b/src/damage_engine/detectors/hail.py
new file mode 100644
index 0000000..9351004
--- /dev/null
+++ b/src/damage_engine/detectors/hail.py
@@ -0,0 +1,587 @@
+"""
+Hail detector: flags POSSIBLE hail and asks the camera for a photo. Runs every morning on the
+24 h ending at 07:00 of the run day and, on hail-risk days, also at 13:05 and 19:05 on the
+6 h before (daytime checks, run_daytime(); hail melts fast, so the photo is asked for at once).
+
+No 3A station has a hail sensor, so this block cannot measure hail. It only checks whether
+the weather data shows the signs of a hailstorm and, if so, writes a camera request
+(schemas.CameraRequest in details["camera_request"], the same format as Snow); the Hail/Snow
+CV model analyses the photo, confirms the hail and grades it (HAIL_SIZE_BANDS_MM is kept for
+it). Goal: high recall - a photo too many costs little, a missed hailstorm is a missed claim
+(user's choice 2026-10-26: one sign is enough for a photo).
+
+Contract (UnipolSai Model 1506, p.8): any hail counts, no size threshold, the 3 km rule does
+not apply. All values are read from damage_engine.thresholds (HAIL, HAIL_RISK, HAIL_SCHEDULE);
+sources:
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Inputs (Diagram.png: temperature, rain gauge). 3A hourly values:
+  - Pioggia              = rain of the hour, mm (sign A; "rain near" for signs B and C)
+  - RainRateMax          = highest rain rate of the hour, mm/h (station 293 only, part of
+                           its rain gauge): sign A
+  - TemperaturaAriaMin   = lowest air temperature of the hour, °C (sign B; the hourly mean
+                           TemperaturaAriaMedia is the fallback)
+  - TemperaturaAriaMedia = mean air temperature of the previous hour (sign B; dew point)
+  - TemperaturaAriaMax   = highest air temperature of the hour (hail-risk day only; mean
+                           fallback)
+  Extra inputs, not in the diagram:
+  - VelocitaVento2mMax   = highest wind of the hour, m/s (stations 3, 6, 10, 30, 544, 566,
+                           600): sign C; VelocitaVento2mMedia is the fallback. Allowed because
+                           HAIL["gust_ms"] is one of the three hail precursors of thresholds.py,
+                           and the squall at the front of a hailstorm is one of its clearest signs.
+  - UmiditaRelativaMedia = mean humidity of the hour, %: only for the hail-risk day (dew point
+                           = moist air that feeds thunderstorms, user's idea 2026-10-26); never
+                           a sign.
+
+Windows - HAIL_SCHEDULE [PROJECT]:
+  - morning run, run_hour = 7: records after 07:00 of the previous day up to 07:00 of the run
+    day (24 hourly records; the hourly Timestamp is the end of the hour, as in the other
+    blocks). Photo at photo_hour = 09:00 of the run day.
+  - daytime checks (run_daytime, hail-risk days only, started by main.py): at each
+    daytime_run_hours = 13, 19 the daytime_window_h = 6 hours before it (records after 07:00
+    up to 13:00, after 13:00 up to 19:00). Photo at the end of those hours (= at once). No night
+    check: the camera cannot take a photo in the dark; the night is in the next morning's 24 h,
+    which also re-checks the daytime hours (a second photo then, accepted).
+  HAIL["storm_window_h"] (1) more hours before each window are fetched, for the temperature
+  drop and the rain near the first hour (one API request per run).
+
+Signs, per hour (3A sends hourly values, so the 10-min / 30-min values of HAIL are read per
+hour):
+  A  burst rain: rain of the hour >= HAIL["rain_1h_mm"] = 20 mm [ENGINEERING], or (293
+     only) RainRateMax >= 6 x HAIL["rain_10min_mm"] = 30 mm/h, the rate of 5 mm in 10 min
+     [ENGINEERING].
+  B  sudden temperature drop (cold outflow of the storm): mean temperature of the previous
+     hour minus the lowest temperature of this hour >= HAIL["t_drop_30min_c"] = 4 °C
+     [ENGINEERING]. With hourly data this is the closest to "4 °C in 30 min" (the drop can
+     span up to about 90 min, so it is a little more permissive).
+  C  strong gust: hourly maximum wind >= HAIL["gust_ms"] = 13.9 m/s [ENGINEERING].
+  B and C count only within HAIL["storm_window_h"] = 1 h [ENGINEERING] of an hour with rain
+  (hail melts in the rain gauge, so a hailstorm leaves rain); an hour with unknown rain counts
+  as possibly rainy (high recall). Operators: ">=" (HAIL has no contract "_op" keys).
+Suspicion level (schemas.SUSPECT_*): "likely" = at least HAIL["min_indicators"] = 2 different
+signs within storm_window_h hours of each other (one storm); "possible" = one sign; none
+otherwise. Both levels ask for a photo (user's choice 2026-10-26).
+
+Hail-risk day (morning run only, details["hail_risk"]; main.py runs the daytime checks for the
+stations where it is not False): inside HAIL_RISK["season"] = 1 May ... 30 Sep [ENGINEERING]
+and (a hail sign in the 24 h, or highest air temperature >= HAIL_RISK["t_max_min_c"] = 25 °C
+and highest hourly dew point >= HAIL_RISK["dew_point_min_c"] = 14 °C [ENGINEERING]). The dew
+point is derived.dew_point_c (3A formula) on the hourly mean temperature and humidity. No
+humidity data -> temperature alone; no data at all in the season -> None (unknown, checked).
+3A has no forecast (and only the 3A API is allowed), so this means "today like yesterday".
+
+Result: status EVENT = "hail suspected, photo requested", with severity NONE (the CV model
+grades it); NO_EVENT when no sign fired. The camera request holds station, photo time, level,
+signs and reason. duration_h / start / end = the hours with a sign. Daytime checks are stored
+as block DAYTIME_BLOCK ("hail_daytime"), one run per check (report.submit(..., slot="13h")),
+so they never replace the morning run; the station report merges them into Hail.
+
+Cover window: COVER_WINDOWS["hail"] = fruit set -> 31 Oct [CONTRACT Special Art.5 p.31],
+fruit set = BBCH above the last post_bloom code of APPLE_BBCH_TO_STAGE (> 69), the same rule
+as Flood / Strong wind (copied: a detector may not import another detector). The photo is
+asked for all year; details["in_cover_window"] says if the day is inside the cover (None
+when the stage is unknown). HAIL_NET_COVER_END (25 May, orchards under anti-hail nets) is
+not applied: fields.json does not say which orchards have nets (data note on events after
+25 May).
+
+Missing sensors / missing values - the detector still runs (Analysis.data_notes):
+  - no rain gauge / no rain data (or in some hours): sign A not checked there; signs B and C
+    there counted as if it rained (worst case).
+  - RainRateMax exists only at 293: elsewhere sign A uses the hourly rain only (no note,
+    it is not a separate sensor).
+  - no temperature sensor / data (or in some hours, or in the hour before): sign B not
+    checked there; hourly minimum missing -> hourly mean (a drop may look smaller).
+  - no wind sensor (7 of 14 stations) / no wind data: sign C not checked; hourly maximum
+    missing -> hourly mean (never above the maximum, so a gust may be missed).
+  - no humidity data: hail-risk day on the temperature alone (morning run, note).
+  - no rain, temperature or wind value in the hours checked -> "no_data".
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no sensor
+  causes an error. detect(..., station_sensors=...) lets the notes tell "no sensor" from
+  "no data".
+"""
+
+import argparse
+import logging
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import (
+    EVENT,
+    NO_DATA,
+    NO_EVENT,
+    SUSPECT_LIKELY,
+    SUSPECT_POSSIBLE,
+    Analysis,
+    CameraRequest,
+)
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import all_stations, station_by_id
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    COVER_WINDOWS,
+    HAIL,
+    HAIL_NET_COVER_END,
+    HAIL_RISK,
+    HAIL_SCHEDULE,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "hail"
+DAYTIME_BLOCK = "hail_daytime"        # where the daytime checks are stored
+PHENOMENON = "hail"                   # contract name, key in COVER_WINDOWS
+RUN_HOUR = HAIL_SCHEDULE["run_hour"]
+PHOTO_HOUR = HAIL_SCHEDULE["photo_hour"]
+DAYTIME_HOURS = tuple(HAIL_SCHEDULE["daytime_run_hours"])
+DAYTIME_H = int(HAIL_SCHEDULE["daytime_window_h"])
+HOUR = timedelta(hours=1)
+STORM_H = int(HAIL["storm_window_h"])
+LOOKBACK_H = max(1, STORM_H)          # the hour before the window is needed for the drop
+RATE_MMH = HAIL["rain_10min_mm"] * 60 / 10    # 5 mm in 10 min as a rate in mm/h (30)
+
+SIGNALS = {
+    "A": "burst rain",
+    "B": "sudden temperature drop near rain",
+    "C": "strong gust near rain",
+}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+RAIN_SENSOR = "Pioggia"
+TEMP_SENSOR = "TemperaturaAria"
+WIND_SENSOR = "VelocitaVento2m"
+HUMIDITY_SENSOR = "UmiditaRelativa"
+SENSORS = (RAIN_SENSOR, TEMP_SENSOR, WIND_SENSOR)   # a station needs at least one of them
+COL_RAIN = "Pioggia"                  # rain of the hour, mm
+COL_RATE = "RainRateMax"              # highest rain rate of the hour, mm/h (293 only)
+COL_T_MIN = derived.T_MIN             # lowest temperature of the hour, °C
+COL_T_MEAN = derived.T_MEAN           # mean temperature of the hour, °C
+COL_T_MAX = derived.T_MAX             # highest temperature of the hour, °C (risk day)
+COL_RH_MEAN = derived.RH_MEAN         # mean humidity of the hour, % (risk day: dew point)
+COL_WIND = "VelocitaVento2mMax"       # highest wind of the hour, m/s
+COL_WIND_MEAN = "VelocitaVento2mMedia"
+COLUMNS = [COL_RAIN, COL_RATE, COL_T_MIN, COL_T_MEAN, COL_T_MAX, COL_RH_MEAN, COL_WIND,
+           COL_WIND_MEAN]
+
+
+# ─────────────────────────── cover window, season ───────────────────────────
+
+def fruit_set(bbch: int) -> bool:
+    """Fruit set (start of the hail cover): BBCH above the last post-bloom code."""
+    return bbch > max(c for c, s in APPLE_BBCH_TO_STAGE.items() if s == "post_bloom")
+
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (fruit set -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return fruit_set(bbch)
+
+
+def in_season(day: date) -> bool:
+    """Inside the hail season of HAIL_RISK (the daytime checks run only then)."""
+    first, last = HAIL_RISK["season"]
+    return first <= day.strftime("%m-%d") <= last
+
+
+# ─────────────────────────── windows ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+def daytime_window(day: date, hour: int):
+    """(start, end) of the daytime check at `hour` of `day`: the DAYTIME_H hours before it."""
+    end = datetime.combine(day, time(hour))
+    return end - timedelta(hours=DAYTIME_H), end
+
+
+def photo_time(run_day: date) -> datetime:
+    return datetime.combine(run_day, time(PHOTO_HOUR))
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return pd.to_numeric(df[name], errors="coerce") if name in df else pd.Series(np.nan, index=df.index)
+
+
+def _near(flag: pd.Series) -> pd.Series:
+    """True where `flag` is True within STORM_H hours (before or after, on the hourly grid)."""
+    return flag.astype(float).rolling(2 * STORM_H + 1, center=True, min_periods=1).max().fillna(0) > 0
+
+
+def day_hours(rows: pd.DataFrame, run_day: date, window=None) -> pd.DataFrame:
+    """One station's rows on the full hourly grid (the hours checked plus LOOKBACK_H hours
+    before; missing hour = empty row), with in_window, rain_mm, rate_mmh, t_min_c, t_max_c,
+    drop_c, dew_c, wind_ms, near_rain, the signs sig_a, sig_b, sig_c and storm_signs (different
+    signs within STORM_H hours). window: (start, end) of a daytime check; None = the 24 h of
+    the morning run."""
+    start, end = window or day_window(run_day)
+    grid = pd.date_range(start + HOUR - timedelta(hours=LOOKBACK_H), end, freq="h", name="Timestamp")
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()
+    df["in_window"] = df["Timestamp"] > start
+    df["rain_mm"] = _col(df, COL_RAIN)
+    df["rate_mmh"] = _col(df, COL_RATE)
+    t_min, t_mean = _col(df, COL_T_MIN), _col(df, COL_T_MEAN)
+    df["t_min_c"] = t_min.fillna(t_mean)
+    df["t_fallback"] = t_min.isna() & t_mean.notna()
+    df["t_max_c"] = _col(df, COL_T_MAX).fillna(t_mean)
+    df["drop_c"] = t_mean.fillna(t_min).shift(1) - df["t_min_c"]
+    df["dew_c"] = derived.dew_point_c(t_mean, _col(df, COL_RH_MEAN))
+    wind, wind_mean = _col(df, COL_WIND), _col(df, COL_WIND_MEAN)
+    df["wind_ms"] = wind.fillna(wind_mean)
+    df["wind_fallback"] = wind.isna() & wind_mean.notna()
+    rain_maybe = (df["rain_mm"] > 0) | (df["rate_mmh"] > 0) | df["rain_mm"].isna()
+    df["near_rain"] = _near(rain_maybe)
+    inside = df["in_window"]
+    df["raw_b"] = inside & (df["drop_c"] >= HAIL["t_drop_30min_c"])           # NaN -> False
+    df["raw_c"] = inside & (df["wind_ms"] >= HAIL["gust_ms"])
+    df["sig_a"] = inside & ((df["rain_mm"] >= HAIL["rain_1h_mm"]) | (df["rate_mmh"] >= RATE_MMH))
+    df["sig_b"] = df["raw_b"] & df["near_rain"]
+    df["sig_c"] = df["raw_c"] & df["near_rain"]
+    df["storm_signs"] = sum(_near(df[s]).astype(int) for s in ("sig_a", "sig_b", "sig_c"))
+    return df
+
+
+def data_notes(w: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing data of one station's hours checked (w = the in-window rows of day_hours()) and
+    how it was handled. sensors: the station's 3A sensor names (None = unknown: only the data)."""
+    notes, n = [], len(w)
+    no_rain = int(w["rain_mm"].isna().sum())
+    if sensors is not None and RAIN_SENSOR not in sensors:
+        notes.append(f"No rain gauge ({RAIN_SENSOR}) at this station: burst rain (sign A) not "
+                     f"checked; temperature drop and gust counted as if it rained.")
+    elif no_rain == n:
+        notes.append(f"No rain data in the {n} hours: burst rain (sign A) not checked; temperature "
+                     f"drop and gust counted as if it rained.")
+    elif no_rain:
+        notes.append(f"Rain missing for {no_rain} of {n} hours: burst rain (sign A) not checked "
+                     f"there; near those hours a drop or gust counts as if it rained.")
+    no_temp = int(w["t_min_c"].isna().sum())
+    if sensors is not None and TEMP_SENSOR not in sensors:
+        notes.append(f"No air temperature sensor ({TEMP_SENSOR}) at this station: temperature "
+                     f"drop (sign B) not checked.")
+    elif no_temp == n:
+        notes.append(f"No air temperature data in the {n} hours: temperature drop (sign B) not "
+                     f"checked.")
+    else:
+        no_drop = int(w["drop_c"].isna().sum())
+        if no_drop:
+            notes.append(f"Temperature drop not checked for {no_drop} of {n} hours (temperature "
+                         f"missing in that hour or in the hour before).")
+        fb = int(w["t_fallback"].sum())
+        if fb:
+            notes.append(f"Hourly minimum temperature missing for {fb} hour(s): hourly mean used "
+                         f"(a drop may look smaller).")
+    no_wind = int(w["wind_ms"].isna().sum())
+    if sensors is not None and WIND_SENSOR not in sensors:
+        notes.append(f"No wind sensor ({WIND_SENSOR}) at this station: strong gust (sign C) not "
+                     f"checked.")
+    elif no_wind == n:
+        notes.append(f"No wind data in the {n} hours: strong gust (sign C) not checked.")
+    else:
+        if no_wind:
+            notes.append(f"Wind missing for {no_wind} of {n} hours: strong gust (sign C) not "
+                         f"checked there.")
+        fb = int(w["wind_fallback"].sum())
+        if fb:
+            notes.append(f"Hourly maximum wind missing for {fb} hour(s): hourly mean used (never "
+                         f"above the maximum, so a gust may be missed).")
+    return notes
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def _iso(t):
+    return None if t is None or pd.isna(t) else pd.Timestamp(t).isoformat(timespec="minutes")
+
+
+def _peak(w: pd.DataFrame, col: str, digits=1):
+    """(highest value, its hour) of a column, or (None, None) without data."""
+    s = w[col].dropna()
+    if s.empty:
+        return None, None
+    i = s.idxmax()
+    return _round(s[i], digits), _iso(w.loc[i, "Timestamp"])
+
+
+def hail_risk(w: pd.DataFrame, run_day: date, signals: list[str]) -> tuple[bool | None, str, list[str]]:
+    """(risk, reason, notes) of the hail-risk day, from the morning run's 24 h (w)."""
+    if not in_season(run_day):
+        return False, "outside the hail season", []
+    if signals:
+        return True, "a hail sign in the last 24 hours", []
+    t_need, dew_need = HAIL_RISK["t_max_min_c"], HAIL_RISK["dew_point_min_c"]
+    t_max, dew = w["t_max_c"].max(), w["dew_c"].max()
+    if pd.isna(t_max):
+        return None, "no air temperature data (risk unknown: daytime checks run)", []
+    if t_max < t_need:
+        return False, f"highest air temperature {t_max:.1f} °C (< {t_need:g} °C)", []
+    if pd.isna(dew):
+        return (True, f"highest air temperature {t_max:.1f} °C, no humidity data",
+                ["No humidity data: the hail-risk day was decided on the air temperature alone."])
+    if dew < dew_need:
+        return False, f"warm ({t_max:.1f} °C) but dry air (dew point {dew:.1f} °C < {dew_need:g} °C)", []
+    return True, f"warm, moist air ({t_max:.1f} °C, dew point {dew:.1f} °C)", []
+
+
+def analyse_station(df: pd.DataFrame, run_day: date, station_id: int,
+                    sensors: set[str] | None = None, photo_at: datetime | None = None,
+                    daytime: bool = False) -> dict:
+    """Station-level result of the hours checked: status, signs, level, camera request, notes;
+    the morning run (daytime=False) also decides the hail-risk day."""
+    w = df[df["in_window"]]
+    notes = data_notes(w, sensors)
+    has_value = w["rain_mm"].notna() | w["rate_mmh"].notna() | w["t_min_c"].notna() | w["wind_ms"].notna()
+    hours = len(w)
+    if not has_value.any():
+        details = {"reason": f"no rain, temperature or wind records in the {hours} h", "hours": hours}
+        if not daytime:
+            details["hail_risk"] = None if in_season(run_day) else False
+        return {"status": NO_DATA, "details": details, "notes": notes}
+    a, b, c = w[w["sig_a"]], w[w["sig_b"]], w[w["sig_c"]]
+    sign_hours = w[w["sig_a"] | w["sig_b"] | w["sig_c"]]
+    rain_1h, rain_1h_at = _peak(w, "rain_mm")
+    rate, rate_at = _peak(w, "rate_mmh")
+    drop, drop_at = _peak(w, "drop_c")
+    gust, gust_at = _peak(w, "wind_ms")
+    storm = int(sign_hours["storm_signs"].max()) if len(sign_hours) else 0
+    signals = [s for s, hit in (("A", len(a)), ("B", len(b)), ("C", len(c))) if hit]
+    if storm >= HAIL["min_indicators"]:
+        level = SUSPECT_LIKELY
+    elif signals:
+        level = SUSPECT_POSSIBLE
+    else:
+        level = None
+    rain_ok = bool(w["rain_mm"].notna().any())
+    details = {
+        "hours": hours,
+        "records": int(has_value.sum()),
+        "rain_checked": rain_ok,
+        "rate_checked": bool(w["rate_mmh"].notna().any()),
+        "temp_checked": bool(w["drop_c"].notna().any()),
+        "wind_checked": bool(w["wind_ms"].notna().any()),
+        "rain_day_mm": _round(w["rain_mm"].sum()) if rain_ok else None,
+        "rain_hours": int((w["rain_mm"] > 0).sum()),
+        "max_rain_1h_mm": rain_1h,
+        "max_rain_1h_at": rain_1h_at,
+        "max_rain_rate_mmh": rate,
+        "max_rain_rate_at": rate_at,
+        "max_t_drop_c": drop,
+        "max_t_drop_at": drop_at,
+        "max_gust_ms": gust,
+        "max_gust_at": gust_at,
+        "burst_hours": int(len(a)),
+        "drop_hours": int(len(b)),
+        "gust_hours": int(len(c)),
+        "drop_dry_hours": int((w["raw_b"] & ~w["near_rain"]).sum()),   # crossed, but no rain near
+        "gust_dry_hours": int((w["raw_c"] & ~w["near_rain"]).sum()),
+        "temp_fallback_hours": int(w["t_fallback"].sum()),
+        "wind_fallback_hours": int(w["wind_fallback"].sum()),
+        "storm_signs": storm,
+        "signals": signals,
+        "suspicion": level,
+    }
+    if daytime:
+        details["check"] = "daytime"
+        details["window_start"] = _iso(w["Timestamp"].min() - HOUR)
+        details["window_end"] = _iso(w["Timestamp"].max())
+    else:
+        risk, why, risk_notes = hail_risk(w, run_day, signals)
+        notes += risk_notes
+        details.update({"max_temp_c": _round(w["t_max_c"].max()), "max_dew_point_c": _round(w["dew_c"].max()),
+                        "hail_risk": risk, "hail_risk_reason": why})
+    if level is None:
+        return {"status": NO_EVENT, "details": details, "notes": notes}
+    reason = "; ".join(SIGNALS[s] for s in signals)
+    request = CameraRequest(phenomenon=PHENOMENON, station_id=station_id,
+                            photo_at=photo_at or photo_time(run_day),
+                            level=level, signals=signals, reason=f"Hail {level}: {reason}.")
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": float(len(sign_hours)),
+        "start": sign_hours["Timestamp"].min().to_pydatetime(),
+        "end": sign_hours["Timestamp"].max().to_pydatetime(),
+        "details": {**details, "camera_request": request.to_dict()},
+    }
+
+
+def _hh(iso: str) -> str:
+    return datetime.fromisoformat(iso).strftime("%d %b %H:%M")
+
+
+def _risk_text(d: dict) -> str:
+    """Morning run: one sentence on the daytime checks of the run day."""
+    if d.get("hail_risk") is False or "hail_risk" not in d:
+        return ""
+    at = " and ".join(f"{h:02d}:00" for h in DAYTIME_HOURS)
+    return f" Hail-risk day: the station is checked again today at {at}."
+
+
+def _summary(res) -> str:
+    d = res["details"]
+    hours = d.get("hours", 24)
+    span = "last 24 hours" if hours == 24 else f"{hours} hours checked"
+    if res["status"] == NO_DATA:
+        return f"No rain, temperature or wind data from the station for the {span}." + _risk_text(d)
+    if res["status"] == NO_EVENT:
+        parts = []
+        if d["max_rain_1h_mm"] is not None:
+            parts.append(f"wettest hour {d['max_rain_1h_mm']:g} mm (sign at ≥ {HAIL['rain_1h_mm']:g} mm)")
+        if d["max_t_drop_c"] is not None:
+            parts.append(f"largest temperature drop {max(d['max_t_drop_c'], 0):g} °C "
+                         f"(≥ {HAIL['t_drop_30min_c']:g} °C)")
+        if d["max_gust_ms"] is not None:
+            parts.append(f"highest wind {d['max_gust_ms']:g} m/s (≥ {HAIL['gust_ms']:g} m/s)")
+        if d["rain_checked"] and not d["rain_hours"] and not d["max_rain_rate_mmh"]:
+            text = f"No hail suspected: no rain in the {hours} hours."
+        else:
+            text = "No hail suspected" + (": " + ", ".join(parts) if parts else "") + "."
+        return text + _risk_text(d)
+    parts = []
+    if d["burst_hours"]:
+        if d["max_rain_1h_mm"] is not None and d["max_rain_1h_mm"] >= HAIL["rain_1h_mm"]:
+            parts.append(f"burst rain of {d['max_rain_1h_mm']:g} mm in one hour ({_hh(d['max_rain_1h_at'])})")
+        else:
+            parts.append(f"rain rate up to {d['max_rain_rate_mmh']:g} mm/h ({_hh(d['max_rain_rate_at'])})")
+    if d["drop_hours"]:
+        parts.append(f"temperature drop of {d['max_t_drop_c']:g} °C near rain")
+    if d["gust_hours"]:
+        parts.append(f"gust of {d['max_gust_ms']:g} m/s near rain")
+    photo = datetime.fromisoformat(d["camera_request"]["photo_at"])
+    when = "at once" if d.get("check") == "daytime" else "for"
+    return (f"Hail {d['suspicion']}: " + "; ".join(parts) +
+            f". Photo requested {when} {photo:%d %b %H:%M} to confirm." + _risk_text(d))
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None,
+           window=None) -> list[Analysis]:
+    """One Analysis per field; EVENT = hail suspected, photo requested.
+
+    Morning run (window None): the 24 h of day_window(run_day), photo at photo_time(run_day),
+    plus the hail-risk day. Daytime check: window = daytime_window(day, hour), block
+    DAYTIME_BLOCK, photo at the window's end (at once), no risk day.
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering the hours checked and the
+    LOOKBACK_H hours before; rows outside are ignored. station_sensors: 3A sensor names per
+    station, so the data notes can tell "no sensor" from "no data".
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    daytime = window is not None
+    photo_at = window[1] if daytime else None
+    hours = DAYTIME_H if daytime else 24
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(day_hours(rows, run_day, window), run_day, int(sid),
+                                                   station_sensors.get(int(sid)), photo_at, daytime)
+    missing = {"reason": "no records", "hours": hours}
+    if not daytime:
+        missing["hail_risk"] = None if in_season(run_day) else False
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": missing,
+                                            "notes": [f"No records from the station for the "
+                                                      f"{'last 24' if hours == 24 else hours} hours."]})
+        details = dict(res["details"])
+        notes = list(res.get("notes", []))
+        if res["status"] == EVENT:
+            day = res["start"].date()
+            details["in_cover_window"] = in_cover_window(day, f.bbch)
+            details["net_checked"] = False
+            if f.bbch is not None:
+                details["bbch"] = f.bbch
+            if day.strftime("%m-%d") > HAIL_NET_COVER_END and details["in_cover_window"] is not False:
+                end = datetime.strptime(HAIL_NET_COVER_END, "%m-%d")
+                notes.append(f"Anti-hail nets are not known for this field: under nets the hail "
+                             f"cover ended on {end:%d %B}.")
+        out.append(Analysis(
+            block=DAYTIME_BLOCK if daytime else BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"],                       # severity stays NONE: the CV model grades
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary({**res, "details": details}), details=details,
+            data_notes=notes,
+        ))
+    return out
+
+
+def _run(run_day: date, window, station_ids: set[int] | None) -> list[Analysis]:
+    """Fetch the hours of `window` (None = the morning 24 h) plus LOOKBACK_H and detect."""
+    start, end = window or day_window(run_day)
+    daytime = window is not None
+    stations = {s.id: s for s in all_stations() if any(s.has(x) for x in SENSORS)}
+    fields = [f for f in all_fields() if station_ids is None or f.station_id in station_ids]
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    first = start + HOUR - timedelta(hours=LOOKBACK_H)
+    df = fetch_hourly(ids, first, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()}, window)
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: hail cannot be judged." if known is None
+                    else "No rain gauge, air temperature or wind sensor at this station: hail "
+                         "cannot be judged.")
+            details = {"reason": "station lacks required sensors"}
+            if not daytime:
+                details["hail_risk"] = False                # nothing to check later either
+            out.append(Analysis(block=DAYTIME_BLOCK if daytime else BLOCK, run_day=run_day,
+                                field_id=f.id, field_name=f.name, station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no rain, temperature or "
+                                                        "wind sensor.",
+                                details=details, data_notes=[note]))
+    n = sum(a.detected for a in out)
+    log.info("Hail %s %s → %s: hail suspected (photo requested) for %d of %d field(s), %d station(s)",
+             "daytime check" if daytime else "morning run", start, end, n, len(out), len(ids))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Morning run: fetch the 24 h (plus LOOKBACK_H hours) of hourly rain, temperature, wind
+    and humidity, flag possible hail and decide the hail-risk day of every station."""
+    return _run(run_day or date.today(), None, None)
+
+
+def run_daytime(day: date, hour: int, station_ids: set[int] | None = None) -> list[Analysis]:
+    """Daytime check at `hour` (one of DAYTIME_HOURS) of `day`: the DAYTIME_H hours before it,
+    for the fields of `station_ids` (None = every station). Photo at once when suspected.
+    main.py calls it on hail-risk days and stores it as DAYTIME_BLOCK with slot "<hour>h"."""
+    if hour not in DAYTIME_HOURS:
+        raise ValueError(f"daytime check hour must be one of {DAYTIME_HOURS}, not {hour}")
+    return _run(day, daytime_window(day, hour), station_ids)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Hail detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    p.add_argument("--daytime", type=int, choices=DAYTIME_HOURS,
+                   help="daytime check at this hour instead of the morning run")
+    args = p.parse_args()
+    results = (run_daytime(args.date or date.today(), args.daytime) if args.daytime
+               else run(args.date))
+    for a in results:
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/high_temperature.py b/src/damage_engine/detectors/high_temperature.py
new file mode 100644
index 0000000..fe02e17
--- /dev/null
+++ b/src/damage_engine/detectors/high_temperature.py
@@ -0,0 +1,407 @@
+"""
+High temperature detector (contract "sunscald" / "sunstroke"): air of at least
+40 °C at 2 m. Runs every 24 h, on the 24 h ending at 07:00 of the run day.
+
+Contract (UnipolSai Model 1506, p.8): sunscald = air temperature of at least 40 °C
+at 2 m. Heatwave (15 days) is out of scope (thresholds.py). All values are read
+from damage_engine.thresholds; sources as tagged there:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [LITERATURE]  WSU / Schrader et al. 2003, Racsko & Schrader 2012 (fruit sunburn)
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Inputs (Diagram.png): air temperature, solar radiation, wet-bulb, humidity. 3A sends
+hourly values:
+  - TemperaturaAriaMax   = highest temperature of the hour, °C: compared with the
+                           trigger (as Hot wind and Strong wind: a peak shorter than
+                           one hour is still found)
+  - TemperaturaAriaMedia = mean temperature of the hour: fallback, reported
+  - RadiazioneSolareMax  = highest irradiance of the hour, W/m² (stations 544, 566,
+                           600 only): fruit surface temperature (FST, below)
+  - UmiditaRelativaMin   = lowest humidity of the hour, %: reported only
+                           (UmiditaRelativaMedia fallback)
+  - wet-bulb: no station measures it; the shared WetBulbMax of sources/derived.py
+    (3A formula on the hourly maximum temperature + minimum humidity) is reported
+    for the hottest hour only. TemperaturaAriaMin / UmiditaRelativaMax are fetched
+    because derived.add_wet_bulb needs them.
+  Extra input, not in the diagram: VelocitaVento2mMedia (mean wind of the hour,
+  m/s). The FST formula of thresholds.py cools the fruit with wind, so without it
+  the FST would always be the calm-air (highest) value. Only the FST uses it.
+
+Event day - HIGH_TEMPERATURE_SCHEDULE["run_hour"] = 7 [PROJECT]: records after
+07:00 of the previous day up to 07:00 of the run day (24 hourly records; the hourly
+Timestamp is taken as the end of the hour, as in Flood, Strong wind, Hot wind).
+
+EVENT (user's choice 2026-10-22: the contract trigger):
+  an hour with air temperature (hourly maximum) >= HOT_TEMPERATURE["contract_t_max_c"]
+  = 40 °C, operator contract_t_max_op = ">=" [CONTRACT p.8, "at least 40 °C" at
+  2 m]. Event = at least one such hour (no minimum duration in the contract; one
+  hourly record is the smallest unit).
+
+Fruit surface temperature (FST), thresholds.py formula, per hour with a solar value:
+    FST = air + fst_gain_max_c * (solar / 1000) / (1 + fst_wind_coeff * wind)
+  fst_gain_max_c = 15 °C [LITERATURE], fst_wind_coeff = 0.3 per m/s [ENGINEERING].
+  The sun gain counts only when solar >= daytime_solar_min_wm2 = 300 W/m²
+  [ENGINEERING, "only sunlit records count"]; a weaker-sun hour has FST = air.
+  Air and solar are hourly maxima, so the FST is the peak of the hour. One hourly
+  record counts as 60 min, so browning_duration_min (60) and necrosis_duration_min
+  (10) [LITERATURE] are both taken as met by one hour (upper estimate).
+
+Severity, the same for every field and crop stage:
+  - with solar data: the highest FST of the 24 h graded with
+    HOT_TEMPERATURE_FST_BANDS_C = 46 / 49 / 52 / 55 °C -> light / moderate / severe
+    / extreme [LITERATURE, 55 ENGINEERING]. Below 46 °C: severity none (no
+    browning expected), the event stays an event (the contract trigger was met).
+  - without solar data (no pyranometer at 11 of 14 stations, or no value): the
+    highest air temperature of the event hours graded with HOT_WIND_BANDS_C = 40 /
+    42 / 44 / 46 °C [ENGINEERING] (the air-temperature scale of the contract's
+    other 40 °C peril, read only).
+
+Sunburn risk, information only, never an event: hours
+with air >= HOT_TEMPERATURE["risk_t_air_c"] = 32 °C [LITERATURE WSU] and FST >= the
+first FST band edge (46 °C, browning). Below 40 °C air this damage is real but not
+insurable under Model 1506. Shown in the summary of days without an event.
+Needs solar data: details["sunburn_risk_hours"] = None without it.
+
+Cover window: COVER_WINDOWS["hot_temperature"] = fruit set -> 31 Oct [CONTRACT
+Special Art.5 p.31], checked on the day of the first hot hour; fruit set = BBCH
+above the last "post_bloom" code of APPLE_BBCH_TO_STAGE [ENGINEERING, approx.], the
+same rule as Flood, Strong wind and Hot wind (a copy: a detector may not import
+another detector). None when the stage is unknown.
+
+Missing sensors / missing values - the detector still runs:
+  - temperature is essential: a station without TemperaturaAria (none today) gives
+    "no_data"; hours without any temperature value are not checked; a day without
+    any usable hour gives "no_data".
+  - hourly maximum temperature missing but mean present: the mean is used for that
+    hour (never above the maximum, so a hit is still a hit, but a short peak may be
+    missed).
+  - no solar sensor or no solar value in the 24 h: no FST, severity on the air
+    temperature, sunburn risk not checked. Solar missing in some hours: no FST in
+    those hours (the highest FST may be underestimated).
+  - no wind value in an hour with sun gain (no anemometer, or no data): calm air,
+    wind = 0 (worst case: the highest FST).
+  - no humidity: humidity and wet-bulb not reported, detection unchanged.
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no
+  sensor causes an error. Every case is written to Analysis.data_notes. detect(...,
+  station_sensors=...) lets the notes tell "no sensor" from "no data".
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    COVER_WINDOWS,
+    HIGH_TEMPERATURE_SCHEDULE,
+    HOT_TEMPERATURE,
+    HOT_TEMPERATURE_FST_BANDS_C,
+    HOT_WIND_BANDS_C,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "high_temperature"
+PHENOMENON = "hot_temperature"        # contract name, key in COVER_WINDOWS
+RUN_HOUR = HIGH_TEMPERATURE_SCHEDULE["run_hour"]
+HOURS_PER_RECORD = 1                  # hourly data
+HOUR = timedelta(hours=1)
+TRIGGER = "hourly_max"                # which temperature is compared (stored in details)
+AIR_BANDS = HOT_WIND_BANDS_C          # severity without solar data (user's choice 2026-10-22)
+SUNBURN_FST_C = HOT_TEMPERATURE_FST_BANDS_C["edges"][0]   # 46 °C: browning starts
+
+OPS = {"<": operator.lt, "<=": operator.le, ">": operator.gt, ">=": operator.ge}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("TemperaturaAria",)
+SOLAR_SENSOR = "RadiazioneSolare"
+WIND_SENSOR = "VelocitaVento2m"
+HUMIDITY_SENSOR = "UmiditaRelativa"
+COL_T_MAX = derived.T_MAX             # TemperaturaAriaMax: highest temperature of the hour, °C
+COL_T_MEAN = derived.T_MEAN           # TemperaturaAriaMedia: fallback, reported
+COL_SOLAR = "RadiazioneSolareMax"     # highest irradiance of the hour, W/m²: FST
+COL_WIND = "VelocitaVento2mMedia"     # mean wind of the hour, m/s: FST cooling only
+COL_RH = derived.RH_MIN               # UmiditaRelativaMin: lowest humidity of the hour, reported
+COL_RH_MEAN = derived.RH_MEAN         # fallback for the lowest humidity
+COLUMNS = [*derived.WET_BULB_INPUTS, COL_SOLAR, COL_WIND]
+
+
+# ─────────────────────────── cover window (same rule as Flood, Strong wind, Hot wind) ───────────────────────────
+
+def fruit_set(bbch: int) -> bool:
+    """Fruit set (start of the hot temperature cover): BBCH above the last post-bloom code."""
+    return bbch > max(c for c, s in APPLE_BBCH_TO_STAGE.items() if s == "post_bloom")
+
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (fruit set -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return fruit_set(bbch)
+
+
+# ─────────────────────────── window ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+# ─────────────────────────── fruit surface temperature ───────────────────────────
+
+def fruit_surface_c(air_c, solar_wm2, wind_ms):
+    """FST (°C) of thresholds.py: air + sun gain, cooled by wind. The sun gain counts only
+    when solar >= daytime_solar_min_wm2; missing wind = calm (worst case). NaN without solar."""
+    p = HOT_TEMPERATURE
+    air, solar = pd.Series(air_c, dtype=float), pd.Series(solar_wm2, dtype=float)
+    wind = pd.Series(wind_ms, dtype=float).fillna(0.0).clip(lower=0.0)
+    gain = p["fst_gain_max_c"] * (solar / 1000.0) / (1.0 + p["fst_wind_coeff"] * wind)
+    gain = gain.where(solar >= p["daytime_solar_min_wm2"], 0.0).where(solar.notna())
+    return air + gain
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def day_hours(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's rows on the full hourly grid of the 24 h (missing hour = empty row), with
+    temp_c (hourly max, mean filled in), temp_fallback, solar_wm2, wind_ms, sunlit, fst_c,
+    rh_pct, wet_bulb_c, hot (contract trigger) and risk (sunburn risk)."""
+    start, end = day_window(run_day)
+    grid = pd.date_range(start + HOUR, end, freq="h", name="Timestamp")
+    rows = derived.add_wet_bulb(rows)
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()   # rows outside the 24 h dropped
+    t_max = _col(df, COL_T_MAX)
+    df["temp_fallback"] = t_max.isna() & _col(df, COL_T_MEAN).notna()
+    df["temp_c"] = t_max.fillna(_col(df, COL_T_MEAN))
+    df["solar_wm2"] = _col(df, COL_SOLAR)
+    df["wind_ms"] = _col(df, COL_WIND)
+    df["sunlit"] = df["solar_wm2"] >= HOT_TEMPERATURE["daytime_solar_min_wm2"]      # NaN -> False
+    df["fst_c"] = fruit_surface_c(df["temp_c"], df["solar_wm2"], df["wind_ms"]).to_numpy()
+    df["rh_pct"] = _col(df, COL_RH).fillna(_col(df, COL_RH_MEAN))
+    df["wet_bulb_c"] = _col(df, derived.WET_BULB_MAX)
+    p = HOT_TEMPERATURE
+    df["hot"] = OPS[p["contract_t_max_op"]](df["temp_c"], p["contract_t_max_c"])  # NaN -> False
+    df["risk"] = (df["temp_c"] >= p["risk_t_air_c"]) & (df["fst_c"] >= SUNBURN_FST_C)
+    return df
+
+
+def data_notes(df: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing data of one station day (df = day_hours()) and how it was handled.
+
+    sensors: the station's 3A sensor names (None = unknown: only the data is checked).
+    """
+    notes, n = [], len(df)
+    no_temp = int(df["temp_c"].isna().sum())
+    if no_temp == n:
+        return [f"No air temperature data in the {n} hours checked: high temperature cannot be judged."]
+    if no_temp:
+        notes.append(f"Air temperature missing for {no_temp} of {n} hours: those hours not checked.")
+    fallback = int(df["temp_fallback"].sum())
+    if fallback:
+        notes.append(f"Hourly maximum temperature missing for {fallback} of {n} hours: hourly mean "
+                     f"used (a short peak in those hours may be missed).")
+    has_temp = df["temp_c"].notna()
+    no_solar = int((has_temp & df["solar_wm2"].isna()).sum())
+    without = ("fruit surface temperature not estimated, so the severity is graded on the air "
+               "temperature and the sunburn risk is not checked.")
+    if sensors is not None and SOLAR_SENSOR not in sensors:
+        notes.append(f"No solar radiation sensor ({SOLAR_SENSOR}) at this station: {without}")
+    elif no_solar == int(has_temp.sum()):
+        notes.append(f"No solar radiation data in the 24 hours: {without}")
+    else:
+        if no_solar:
+            notes.append(f"Solar radiation missing for {no_solar} of {n} hours: fruit surface "
+                         f"temperature not estimated in those hours (it may be underestimated).")
+        calm = int((df["sunlit"] & df["wind_ms"].isna()).sum())
+        if sensors is not None and WIND_SENSOR not in sensors:
+            notes.append(f"No wind sensor ({WIND_SENSOR}) at this station: fruit surface "
+                         f"temperature computed for calm air (worst case).")
+        elif calm:
+            notes.append(f"Wind missing for {calm} sunlit hour(s): fruit surface temperature "
+                         f"computed for calm air in those hours (worst case).")
+    if sensors is not None and HUMIDITY_SENSOR not in sensors:
+        notes.append(f"No humidity sensor ({HUMIDITY_SENSOR}) at this station: humidity and "
+                     f"wet-bulb not reported (not used to detect).")
+    elif df.loc[has_temp, "rh_pct"].isna().all():
+        notes.append("No humidity data in the 24 hours: humidity and wet-bulb not reported "
+                     "(not used to detect).")
+    return notes
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def _iso(t):
+    return None if t is None or pd.isna(t) else pd.Timestamp(t).isoformat(timespec="minutes")
+
+
+def analyse_station(df: pd.DataFrame, sensors: set[str] | None = None) -> dict:
+    """Station-level result of one day: status, duration, times, temperatures, FST, notes."""
+    notes = data_notes(df, sensors)
+    checked = df[df["temp_c"].notna()]
+    if checked.empty:
+        return {"status": NO_DATA, "details": {"reason": "no air temperature records in the 24 h"},
+                "notes": notes}
+    top = checked.loc[checked["temp_c"].idxmax()]
+    with_fst = checked[checked["fst_c"].notna()]
+    solar = not with_fst.empty
+    fst_top = with_fst.loc[with_fst["fst_c"].idxmax()] if solar else None
+    risk = checked[checked["risk"]]
+    base = {"records": int(len(checked)),
+            "trigger": TRIGGER,
+            "max_temp_c": _round(top["temp_c"]),
+            "max_temp_at": _iso(top["Timestamp"]),
+            "max_mean_temp_c": _round(_col(checked, COL_T_MEAN).max()),
+            "fallback_hours": int(checked["temp_fallback"].sum()),
+            "humidity_at_max_temp_pct": _round(top["rh_pct"], 0),
+            "wet_bulb_at_max_temp_c": _round(top["wet_bulb_c"]),
+            "solar_checked": solar,
+            "sunlit_hours": int(checked["sunlit"].sum()) if solar else None,
+            "max_solar_wm2": _round(with_fst["solar_wm2"].max(), 0) if solar else None,
+            "max_fst_c": _round(fst_top["fst_c"]) if solar else None,
+            "max_fst_at": _iso(fst_top["Timestamp"]) if solar else None,
+            "solar_at_max_fst_wm2": _round(fst_top["solar_wm2"], 0) if solar else None,
+            "wind_at_max_fst_ms": _round(fst_top["wind_ms"]) if solar else None,
+            "sunburn_risk_hours": int(len(risk)) if solar else None,
+            "max_risk_fst_c": _round(risk["fst_c"].max()) if solar and len(risk) else None}
+    hits = checked[checked["hot"]]
+    if len(hits) == 0:
+        return {"status": NO_EVENT, "details": base, "notes": notes}
+    if solar:
+        method, value = "fst", float(with_fst["fst_c"].max())
+    else:
+        method, value = "air", float(hits["temp_c"].max())
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": len(hits) * HOURS_PER_RECORD,
+        "start": hits["Timestamp"].min().to_pydatetime(),
+        "end": hits["Timestamp"].max().to_pydatetime(),
+        "graded_value": value,
+        "details": {**base,
+                    "hot_hours": int(len(hits)),
+                    "severity_method": method,
+                    "severity_value_c": _round(value)},
+    }
+
+
+def _summary(res, sev):
+    if res["status"] == NO_DATA:
+        return "No air temperature data from the station for the last 24 hours."
+    d = res["details"]
+    if res["status"] == NO_EVENT:
+        text = f"No high temperature: highest air temperature {d['max_temp_c']:g} °C."
+        if d["sunburn_risk_hours"]:
+            text += (f" Sunburn risk: fruit surface about {d['max_risk_fst_c']:g} °C in "
+                     f"{d['sunburn_risk_hours']} hour(s) (below the contract's 40 °C air, "
+                     f"not insurable).")
+        return text
+    text = f"High temperature for {res['duration_h']:g} h, highest air temperature {d['max_temp_c']:g} °C"
+    if d["severity_method"] == "fst":
+        text += f", fruit surface about {d['max_fst_c']:g} °C"
+    return text + f": severity {sev.value}."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None) -> list[Analysis]:
+    """One Analysis per field: the 24 h of its station, graded on the FST (or the air).
+
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering day_window(run_day);
+    rows outside it are ignored. station_sensors: 3A sensor names per station, so the data
+    notes can tell "no sensor" from "no data"; without it only the data is checked.
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(day_hours(rows, run_day),
+                                                   station_sensors.get(int(sid)))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station for the last "
+                                                      "24 hours."]})
+        sev, score = Severity.NONE, 0.0
+        details = dict(res["details"])
+        if res["status"] == EVENT:
+            bands = HOT_TEMPERATURE_FST_BANDS_C if details["severity_method"] == "fst" else AIR_BANDS
+            sev, score = grade(res["graded_value"], bands)
+            details["in_cover_window"] = in_cover_window(res["start"].date(), f.bbch)
+            if f.bbch is not None:
+                details["bbch"] = f.bbch
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev), details=details,
+            data_notes=list(res.get("notes", [])),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the last 24 h of hourly temperature, solar, wind and humidity and detect high
+    temperature."""
+    run_day = run_day or date.today()
+    start, end = day_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start + HOUR, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: high temperature cannot be judged."
+                    if known is None
+                    else "No air temperature sensor (TemperaturaAria) at this station: high "
+                         "temperature cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no air temperature sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("High temperature %s: %d event(s) over %d field(s), %d station(s)",
+             run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the High temperature detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/hot_wind.py b/src/damage_engine/detectors/hot_wind.py
new file mode 100644
index 0000000..b8f966d
--- /dev/null
+++ b/src/damage_engine/detectors/hot_wind.py
@@ -0,0 +1,359 @@
+"""
+Hot wind detector (scirocco / libeccio): air of at least 40 °C carried by moving
+air masses. Runs every 24 h, on the 24 h ending at 07:00 of the run day.
+
+Contract (UnipolSai Model 1506, p.8): hot wind = warm air masses from south-east
+to south-west with a temperature of at least 40 °C at 2 m, causing withering in
+the 30 days before harvest. All values are read from damage_engine.thresholds;
+sources as tagged there:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Inputs (Diagram.png): air temperature, wind speed, humidity. 3A sends hourly values:
+  - TemperaturaAriaMax   = highest temperature of the hour, °C: compared with the
+                           trigger (chosen by the user 2026-10-22, as for Strong
+                           wind: a peak shorter than one hour is still found)
+  - TemperaturaAriaMedia = mean temperature of the hour: fallback, reported
+  - VelocitaVento2mMedia = mean wind of the hour, m/s: the "movement of air
+                           masses" test; VelocitaVento2mMax is its fallback
+  - UmiditaRelativaMin   = lowest humidity of the hour, %: reported only (dry air
+                           speeds up withering; not a contract condition)
+  No 3A station measures wind DIRECTION, so the contract's SE-SW rule is never
+  applied.
+
+Event day - HOT_WIND_SCHEDULE["run_hour"] = 7 [PROJECT]: records after 07:00 of
+the previous day up to 07:00 of the run day (24 hourly records; the hourly
+Timestamp is taken as the end of the hour, as in Flood and Strong wind).
+
+An hour is a hot-wind hour when (HOT_WIND):
+  - air temperature (hourly maximum) >= t_max_trigger_c = 40 °C, operator
+    t_max_trigger_op = ">=" [CONTRACT p.8, "at least 40 °C" at 2 m], and
+  - the air moves: hourly mean wind >= wind_min_ms = 1.0 m/s [ENGINEERING; the
+    contract says "movement of air masses" and gives no speed]. Without a wind
+    value for the hour (no anemometer: 7 of 14 stations, or no data) the air is
+    taken as moving (worst case, as Brina; user's choice 2026-10-22).
+  - NOT applied: wind from dir_from_min_deg = 135° to dir_from_max_deg = 225°
+    (SE-SW) [CONTRACT p.8]: no direction sensor at any station (user's choice
+    2026-10-22). details["direction_checked"] = False on every result.
+Event when there is at least one hot-wind hour: neither the contract nor
+thresholds.py gives a minimum duration, and one hourly record is the smallest unit.
+
+Severity, the same for every field and crop stage: the highest air temperature of
+the hot-wind hours graded with HOT_WIND_BANDS_C [ENGINEERING]: >= 40 / 42 / 44 /
+46 °C -> light / moderate / severe / extreme. The bands only grade an event that
+already passed the trigger.
+
+Cover window: COVER_WINDOWS["hot_wind"] = fruit set -> 31 Oct [CONTRACT Special
+Art.5 p.31], checked on the day of the first hot-wind hour; fruit set = BBCH above
+the last "post_bloom" code of APPLE_BBCH_TO_STAGE [ENGINEERING, approx.], the same
+rule as Flood and Strong wind (a copy: a detector may not import another detector).
+The contract also limits hot wind to HOT_WIND["days_before_harvest"] = 30 days
+before harvest [CONTRACT p.8]: fields have no harvest date yet, so this is not
+checked (data note on events, details["harvest_checked"] = False). So
+in_cover_window is never True: False when surely outside (after 31 Oct, or a
+known stage before fruit set), otherwise None.
+
+Missing sensors / missing values - the detector still runs:
+  - temperature is essential: a station without TemperaturaAria (none today) gives
+    "no_data"; hours without any temperature value are not checked; a day without
+    any usable hour gives "no_data".
+  - hourly maximum temperature missing but mean present: the mean is used for that
+    hour (never above the maximum, so a hit is still a hit, but a short peak may
+    be missed).
+  - no wind sensor, or no wind value in an hour: air movement not checked, taken
+    as moving (worst case); details["wind_checked"] = False when the day has no
+    wind value at all. Hourly mean wind missing but maximum present: the maximum
+    is used (if even the maximum is below 1 m/s, the air was still).
+  - no humidity: not reported, detection unchanged.
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no
+  sensor causes an error. Every case is written to Analysis.data_notes. detect(...,
+  station_sensors=...) lets the notes tell "no sensor" from "no data".
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    COVER_WINDOWS,
+    HOT_WIND,
+    HOT_WIND_BANDS_C,
+    HOT_WIND_SCHEDULE,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "hot_wind"
+PHENOMENON = "hot_wind"               # contract name, key in COVER_WINDOWS
+RUN_HOUR = HOT_WIND_SCHEDULE["run_hour"]
+HOURS_PER_RECORD = 1                  # hourly data
+HOUR = timedelta(hours=1)
+TRIGGER = "hourly_max"                # which temperature is compared (stored in details)
+
+OPS = {"<": operator.lt, "<=": operator.le, ">": operator.gt, ">=": operator.ge}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("TemperaturaAria",)
+WIND_SENSOR = "VelocitaVento2m"
+HUMIDITY_SENSOR = "UmiditaRelativa"
+COL_T_MAX = "TemperaturaAriaMax"      # highest temperature of the hour, °C: the trigger
+COL_T_MEAN = "TemperaturaAriaMedia"   # mean temperature of the hour: fallback, reported
+COL_WIND = "VelocitaVento2mMedia"     # mean wind of the hour, m/s: air movement test
+COL_WIND_MAX = "VelocitaVento2mMax"   # highest wind of the hour: fallback for the mean
+COL_RH = "UmiditaRelativaMin"         # lowest humidity of the hour, %: reported only
+COL_RH_MEAN = "UmiditaRelativaMedia"  # fallback for the lowest humidity
+COLUMNS = [COL_T_MAX, COL_T_MEAN, COL_WIND, COL_WIND_MAX, COL_RH, COL_RH_MEAN]
+
+
+# ─────────────────────────── cover window (same rule as Flood, Strong wind) ───────────────────────────
+
+def fruit_set(bbch: int) -> bool:
+    """Fruit set (start of the hot wind cover): BBCH above the last post-bloom code."""
+    return bbch > max(c for c, s in APPLE_BBCH_TO_STAGE.items() if s == "post_bloom")
+
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (fruit set -> end date, and the 30 days before harvest).
+
+    False when surely outside (after the end date, or before fruit set); otherwise None:
+    fields have no harvest date, so the 30-days-before-harvest rule cannot be checked.
+    """
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is not None and not fruit_set(bbch):
+        return False
+    return None
+
+
+# ─────────────────────────── window ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def day_hours(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's rows on the full hourly grid of the 24 h (missing hour = empty row), with
+    temp_c (hourly max, mean filled in), temp_fallback, wind_ms (mean, max filled in),
+    rh_pct, hot (temperature trigger), moving (air movement, unknown = moving) and hot_wind."""
+    start, end = day_window(run_day)
+    grid = pd.date_range(start + HOUR, end, freq="h", name="Timestamp")
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()   # rows outside the 24 h dropped
+    t_max = _col(df, COL_T_MAX)
+    df["temp_fallback"] = t_max.isna() & _col(df, COL_T_MEAN).notna()
+    df["temp_c"] = t_max.fillna(_col(df, COL_T_MEAN))
+    wind = _col(df, COL_WIND)
+    df["wind_fallback"] = wind.isna() & _col(df, COL_WIND_MAX).notna()
+    df["wind_ms"] = wind.fillna(_col(df, COL_WIND_MAX))
+    df["rh_pct"] = _col(df, COL_RH).fillna(_col(df, COL_RH_MEAN))
+    df["hot"] = OPS[HOT_WIND["t_max_trigger_op"]](df["temp_c"], HOT_WIND["t_max_trigger_c"])  # NaN -> False
+    df["moving"] = df["wind_ms"].isna() | (df["wind_ms"] >= HOT_WIND["wind_min_ms"])
+    df["hot_wind"] = df["hot"] & df["moving"]
+    return df
+
+
+DIRECTION_NOTE = ("No wind-direction sensor at any 3A station: the contract's south-east to "
+                  "south-west rule is not checked.")
+
+
+def data_notes(df: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing data of one station day (df = day_hours()) and how it was handled.
+
+    sensors: the station's 3A sensor names (None = unknown: only the data is checked).
+    """
+    notes, n = [], len(df)
+    no_temp = int(df["temp_c"].isna().sum())
+    if no_temp == n:
+        return [f"No air temperature data in the {n} hours checked: hot wind cannot be judged."]
+    if no_temp:
+        notes.append(f"Air temperature missing for {no_temp} of {n} hours: those hours not checked.")
+    fallback = int(df["temp_fallback"].sum())
+    if fallback:
+        notes.append(f"Hourly maximum temperature missing for {fallback} of {n} hours: hourly mean "
+                     f"used (a short peak in those hours may be missed).")
+    has_temp = df["temp_c"].notna()
+    no_wind = int((has_temp & df["wind_ms"].isna()).sum())
+    if sensors is not None and WIND_SENSOR not in sensors:
+        notes.append(f"No wind sensor ({WIND_SENSOR}) at this station: air movement not checked, "
+                     f"taken as moving (worst case).")
+    elif no_wind == int(has_temp.sum()):
+        notes.append("No wind data in the 24 hours: air movement not checked, taken as moving "
+                     "(worst case).")
+    elif no_wind:
+        notes.append(f"Wind missing for {no_wind} of {n} hours: air taken as moving in those hours "
+                     f"(worst case).")
+    wind_fb = int((has_temp & df["wind_fallback"]).sum())
+    if wind_fb:
+        notes.append(f"Hourly mean wind missing for {wind_fb} of {n} hours: hourly maximum used.")
+    if sensors is not None and HUMIDITY_SENSOR not in sensors:
+        notes.append(f"No humidity sensor ({HUMIDITY_SENSOR}) at this station: humidity not "
+                     f"reported (not used to detect).")
+    elif df.loc[has_temp, "rh_pct"].isna().all():
+        notes.append("No humidity data in the 24 hours: humidity not reported (not used to detect).")
+    notes.append(DIRECTION_NOTE)
+    return notes
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def _iso(t):
+    return None if t is None or pd.isna(t) else pd.Timestamp(t).isoformat(timespec="minutes")
+
+
+def analyse_station(df: pd.DataFrame, sensors: set[str] | None = None) -> dict:
+    """Station-level result of one day: status, duration, times, highest temperature, notes."""
+    notes = data_notes(df, sensors)
+    checked = df[df["temp_c"].notna()]
+    if checked.empty:
+        return {"status": NO_DATA, "details": {"reason": "no air temperature records in the 24 h"},
+                "notes": notes}
+    top = checked.loc[checked["temp_c"].idxmax()]
+    hot = checked[checked["hot"]]
+    base = {"records": int(len(checked)),
+            "trigger": TRIGGER,
+            "max_temp_c": _round(top["temp_c"]),
+            "max_temp_at": _iso(top["Timestamp"]),
+            "max_mean_temp_c": _round(_col(checked, COL_T_MEAN).max()),
+            "fallback_hours": int(checked["temp_fallback"].sum()),
+            "hot_hours": int(len(hot)),
+            "still_hot_hours": int((hot["wind_ms"] < HOT_WIND["wind_min_ms"]).sum()),
+            "wind_checked": bool(checked["wind_ms"].notna().any()),
+            "wind_at_max_temp_ms": _round(top["wind_ms"]),
+            "humidity_at_max_temp_pct": _round(top["rh_pct"], 0),
+            "direction_checked": False}
+    hits = checked[checked["hot_wind"]]
+    if len(hits) == 0:
+        return {"status": NO_EVENT, "details": base, "notes": notes}
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": len(hits) * HOURS_PER_RECORD,
+        "start": hits["Timestamp"].min().to_pydatetime(),
+        "end": hits["Timestamp"].max().to_pydatetime(),
+        "max_temp_c": float(hits["temp_c"].max()),
+        "details": {**base,
+                    "hot_wind_hours": int(len(hits)),
+                    "max_hot_wind_temp_c": _round(hits["temp_c"].max()),
+                    "wind_assumed_hours": int(hits["wind_ms"].isna().sum()),
+                    "min_humidity_pct": _round(hits["rh_pct"].min(), 0),
+                    "harvest_checked": False},
+    }
+
+
+def _summary(res, sev):
+    if res["status"] == NO_DATA:
+        return "No air temperature data from the station for the last 24 hours."
+    d = res["details"]
+    if res["status"] == NO_EVENT:
+        text = f"No hot wind: highest air temperature {d['max_temp_c']:g} °C."
+        if d["still_hot_hours"]:
+            text += f" {d['still_hot_hours']} hour(s) at 40 °C or more had still air."
+        return text
+    return (f"Hot wind for {res['duration_h']:g} h, highest air temperature "
+            f"{d['max_hot_wind_temp_c']:g} °C: severity {sev.value}.")
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None) -> list[Analysis]:
+    """One Analysis per field: the 24 h of its station, graded with HOT_WIND_BANDS_C.
+
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering day_window(run_day);
+    rows outside it are ignored. station_sensors: 3A sensor names per station, so the data
+    notes can tell "no sensor" from "no data"; without it only the data is checked.
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(day_hours(rows, run_day),
+                                                   station_sensors.get(int(sid)))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station for the last "
+                                                      "24 hours."]})
+        sev, score = Severity.NONE, 0.0
+        details = dict(res["details"])
+        notes = list(res.get("notes", []))
+        if res["status"] == EVENT:
+            sev, score = grade(res["max_temp_c"], HOT_WIND_BANDS_C)
+            details["in_cover_window"] = in_cover_window(res["start"].date(), f.bbch)
+            if f.bbch is not None:
+                details["bbch"] = f.bbch
+            notes.append(f"Harvest date not known: the contract's "
+                         f"{HOT_WIND['days_before_harvest']} days before harvest rule is not checked.")
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev), details=details,
+            data_notes=notes,
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the last 24 h of hourly temperature, wind and humidity and detect hot wind."""
+    run_day = run_day or date.today()
+    start, end = day_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start + HOUR, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: hot wind cannot be judged." if known is None
+                    else "No air temperature sensor (TemperaturaAria) at this station: hot wind "
+                         "cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no air temperature sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("Hot wind %s: %d event(s) over %d field(s), %d station(s)",
+             run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Hot wind detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/snow.py b/src/damage_engine/detectors/snow.py
new file mode 100644
index 0000000..a85a1bc
--- /dev/null
+++ b/src/damage_engine/detectors/snow.py
@@ -0,0 +1,424 @@
+"""
+Snow detector: flags POSSIBLE snow and asks the camera for a photo. Runs every 24 h,
+on the 24 h ending at 07:00 of the run day.
+
+No 3A station has a snow sensor, so this block cannot measure snow. It only checks whether snow
+was possible from the weather data and, if so, writes a camera request
+(schemas.CameraRequest in details["camera_request"]); the Hail/Snow CV model analyses the photo, confirms the snow and grades it. Goal: high recall - a photo too many costs little, a missed snowfall is a
+missed claim. So when the data is unclear the block suspects snow.
+
+Contract (UnipolSai Model 1506, p.8): snow causing significant mechanical damage, no
+depth threshold. All values are read from damage_engine.thresholds; sources as tagged:
+  [LITERATURE]  rain / snow split on the wet-bulb temperature
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Inputs (Diagram.png: rain, temperature, solar radiation, humidity). 3A hourly values:
+  - Pioggia              = rain of the hour, mm (signals A, B; snow-water estimate)
+  - TemperaturaAria*     = air temperature, °C (wet-bulb; air fallback)
+  - UmiditaRelativa*     = humidity, % (wet-bulb; signal C uses UmiditaRelativaMax,
+                           UmiditaRelativaMedia fallback)
+  - wet-bulb: the shared WetBulbMin of sources/derived.py (3A formula on the hourly
+    minimum temperature + maximum humidity) = the coldest moment of the hour, so an
+    hour that is partly snow counts as snow (worst case).
+  - RadiazioneSolareMax  = highest irradiance of the hour, W/m² (stations 544, 566,
+                           600 only): signal D.
+
+Window - SNOW_SCHEDULE["run_hour"] = 7 [PROJECT]: records after 07:00 of the previous
+day up to 07:00 of the run day (24 hourly records; the hourly Timestamp is the end of
+the hour, as in the other blocks). SNOW["melt_lookahead_h"] more hours before it are
+fetched so signal B sees cold hours just before the window (one API request).
+
+Cold hour: WetBulbMin <= SNOW["t_wet_snow_max_c"] = 1.0 °C [LITERATURE]. Without a
+wet-bulb in that hour (no humidity) the air temperature (hourly minimum, mean fallback)
+<= 1.0 °C is used instead.
+
+Signals, per hour of the 24 h:
+  A  precipitation in a cold hour (Pioggia > 0): snow probably fell.          strong
+  B  rain in a NOT cold hour, up to SNOW["melt_lookahead_h"] = 12 h [ENGINEERING]
+     after a cold hour: an unheated rain gauge records snow only when it melts in
+     the funnel, as "rain" once it warms up.                                   medium
+  C  cold hour with humidity (hourly max) >= SNOW["saturated_rh_min_pct"] = 95 %
+     [ENGINEERING] and no rain recorded: snow the gauge may not have caught
+     (also fog or frost).                                                      weak
+  D  (supporting, stations with a pyranometer) every midday record (records
+     ending SNOW["midday_hours"] = 11:00 ... 14:00 [ENGINEERING]) after the first
+     A/B/C hour has sun <= SNOW["covered_solar_max_wm2"] = 20 W/m² [ENGINEERING]
+     (overcast midday is about 50-200 W/m²): the sensor may be under snow. Never
+     triggers a photo on its own.
+Suspicion level (schemas.SUSPECT_*): "likely" = A or B, or C with D; "possible" = C
+only; none otherwise.
+
+Result: status EVENT = "snow suspected, photo requested",
+with severity NONE (the CV model grades it); NO_EVENT when no signal fired. The camera
+request holds station, photo time (SNOW_SCHEDULE["photo_hour"] = 09:00 of the run day
+[PROJECT]: daylight, before the snow melts off the trees), level, signals and reason.
+duration_h / start / end = the A, B, C hours.
+
+Snow-depth hint, information only: water of the A hours x SNOW["snow_liquid_ratio"] =
+10 [ENGINEERING] (mm water -> cm snow: x 10 / 10) = est_new_snow_cm; counting the B
+water too = est_new_snow_max_cm. SNOW_DEPTH_BANDS_CM and SNOW["foliage_level_bump"]
+are not used here: they are kept for the CV model's grading.
+
+Cover window: COVER_WINDOWS["snow"] = budburst -> 31 Oct [ENGINEERING, snow is not
+listed in Special Art.5 - verify], budburst = BBCH >= APPLE_DORMANT_BELOW_BBCH (51), as
+Brina. The photo is asked for all year; details["in_cover_window"] says if the day is
+inside the cover (None when the stage is unknown).
+
+Missing sensors / missing values - the detector still runs (Analysis.data_notes):
+  - temperature is essential: a station without TemperaturaAria gives "no_data"; hours
+    without temperature are not checked; no usable hour in the 24 h gives "no_data".
+  - no humidity (sensor or data, or in some hours): air <= 1 °C instead of the
+    wet-bulb in those hours; signal C not checked there.
+  - no rain gauge / no rain data (or in some hours): signals A and B not checked
+    there; an hour with unknown rain can still give C.
+  - no pyranometer / no solar data: signal D not checked (supporting only).
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no
+  sensor causes an error. detect(..., station_sensors=...) lets the notes tell "no
+  sensor" from "no data".
+"""
+
+import argparse
+import logging
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import (
+    EVENT,
+    NO_DATA,
+    NO_EVENT,
+    SUSPECT_LIKELY,
+    SUSPECT_POSSIBLE,
+    Analysis,
+    CameraRequest,
+)
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import APPLE_DORMANT_BELOW_BBCH, COVER_WINDOWS, SNOW, SNOW_SCHEDULE
+
+log = logging.getLogger(__name__)
+
+BLOCK = "snow"
+PHENOMENON = "snow"                   # contract name, key in COVER_WINDOWS
+RUN_HOUR = SNOW_SCHEDULE["run_hour"]
+PHOTO_HOUR = SNOW_SCHEDULE["photo_hour"]
+HOUR = timedelta(hours=1)
+LOOKBACK_H = int(SNOW["melt_lookahead_h"])
+MIDDAY_FIRST, MIDDAY_LAST = SNOW["midday_hours"]
+
+SIGNALS = {
+    "A": "precipitation in a cold hour",
+    "B": "rain soon after a cold hour (snow melting in the rain gauge)",
+    "C": "cold saturated air without rain recorded",
+    "D": "midday sun very low after a suspect hour (solar sensor may be under snow)",
+}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("TemperaturaAria",)
+RAIN_SENSOR = "Pioggia"
+HUMIDITY_SENSOR = "UmiditaRelativa"
+SOLAR_SENSOR = "RadiazioneSolare"
+COL_RAIN = "Pioggia"                  # rain of the hour, mm
+COL_SOLAR = "RadiazioneSolareMax"     # highest irradiance of the hour, W/m²: signal D
+COL_T_MIN = derived.T_MIN             # air fallback when there is no wet-bulb
+COL_T_MEAN = derived.T_MEAN
+COL_RH = derived.RH_MAX               # highest humidity of the hour: signal C
+COL_RH_MEAN = derived.RH_MEAN
+COLUMNS = [*derived.WET_BULB_INPUTS, COL_RAIN, COL_SOLAR]
+
+
+# ─────────────────────────── cover window ───────────────────────────
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (budburst -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return bbch >= APPLE_DORMANT_BELOW_BBCH
+
+
+# ─────────────────────────── window ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+def photo_time(run_day: date) -> datetime:
+    return datetime.combine(run_day, time(PHOTO_HOUR))
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return pd.to_numeric(df[name], errors="coerce") if name in df else pd.Series(np.nan, index=df.index)
+
+
+def day_hours(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's rows on the full hourly grid (the 24 h plus LOOKBACK_H hours before;
+    missing hour = empty row), with in_window, cold_c (wet-bulb, air fallback), cold_basis,
+    cold, rain_mm, rh_pct, solar_wm2 and the signals sig_a, sig_b, sig_c."""
+    start, end = day_window(run_day)
+    grid = pd.date_range(start + HOUR - timedelta(hours=LOOKBACK_H), end, freq="h", name="Timestamp")
+    rows = derived.add_wet_bulb(rows)
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()
+    df["in_window"] = df["Timestamp"] > start
+    wet = _col(df, derived.WET_BULB_MIN)
+    air = _col(df, COL_T_MIN).fillna(_col(df, COL_T_MEAN))
+    df["wet_bulb_c"] = wet
+    df["air_c"] = air
+    df["cold_c"] = wet.fillna(air)
+    df["cold_basis"] = np.where(wet.notna(), "wet_bulb", np.where(air.notna(), "air", None))
+    df["cold"] = df["cold_c"] <= SNOW["t_wet_snow_max_c"]                     # NaN -> False
+    df["rain_mm"] = _col(df, COL_RAIN)
+    df["rh_pct"] = _col(df, COL_RH).fillna(_col(df, COL_RH_MEAN))
+    df["solar_wm2"] = _col(df, COL_SOLAR)
+    wet_hour = df["rain_mm"] > 0                                              # NaN -> False
+    recent_cold = (df["cold"].astype(float).shift(1)
+                   .rolling(LOOKBACK_H, min_periods=1).max().fillna(0) > 0)
+    df["sig_a"] = df["cold"] & wet_hour
+    df["sig_b"] = df["cold_c"].notna() & ~df["cold"] & recent_cold & wet_hour
+    df["sig_c"] = df["cold"] & ~wet_hour & (df["rh_pct"] >= SNOW["saturated_rh_min_pct"])
+    return df
+
+
+def data_notes(w: pd.DataFrame, sensors: set[str] | None = None) -> list[str]:
+    """Missing data of one station's 24 h (w = the in-window rows of day_hours()) and how it
+    was handled. sensors: the station's 3A sensor names (None = unknown: only the data)."""
+    notes, n = [], len(w)
+    no_temp = int(w["cold_c"].isna().sum())
+    if no_temp == n:
+        return [f"No air temperature data in the {n} hours checked: snow cannot be judged."]
+    if no_temp:
+        notes.append(f"Air temperature missing for {no_temp} of {n} hours: those hours not checked.")
+    checked = w["cold_c"].notna()
+    t = SNOW["t_wet_snow_max_c"]
+    no_rh = int((checked & w["rh_pct"].isna()).sum())
+    if sensors is not None and HUMIDITY_SENSOR not in sensors:
+        notes.append(f"No humidity sensor ({HUMIDITY_SENSOR}) at this station: air temperature "
+                     f"≤ {t:g} °C used instead of the wet-bulb, and cold saturated air (signal C) "
+                     f"not checked.")
+    elif no_rh == int(checked.sum()):
+        notes.append(f"No humidity data in the 24 hours: air temperature ≤ {t:g} °C used instead "
+                     f"of the wet-bulb, and cold saturated air (signal C) not checked.")
+    elif no_rh:
+        notes.append(f"Humidity missing for {no_rh} of {n} hours: air temperature used instead of "
+                     f"the wet-bulb in those hours (signal C not checked there).")
+    no_rain = int((checked & w["rain_mm"].isna()).sum())
+    if sensors is not None and RAIN_SENSOR not in sensors:
+        notes.append(f"No rain gauge ({RAIN_SENSOR}) at this station: precipitation signals (A, B) "
+                     f"not checked; snow can only be suspected from cold saturated air (C).")
+    elif no_rain == int(checked.sum()):
+        notes.append("No rain data in the 24 hours: precipitation signals (A, B) not checked; snow "
+                     "can only be suspected from cold saturated air (C).")
+    elif no_rain:
+        notes.append(f"Rain missing for {no_rain} of {n} hours: precipitation signals (A, B) not "
+                     f"checked in those hours.")
+    if sensors is not None and SOLAR_SENSOR not in sensors:
+        notes.append(f"No solar radiation sensor ({SOLAR_SENSOR}) at this station: the covered-"
+                     f"sensor sign (signal D, supporting only) not checked.")
+    elif w.loc[checked, "solar_wm2"].isna().all():
+        notes.append("No solar radiation data in the 24 hours: the covered-sensor sign (signal D, "
+                     "supporting only) not checked.")
+    return notes
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def _iso(t):
+    return None if t is None or pd.isna(t) else pd.Timestamp(t).isoformat(timespec="minutes")
+
+
+def _midday(w: pd.DataFrame, after) -> pd.DataFrame:
+    """Midday records (ending MIDDAY_FIRST ... MIDDAY_LAST) after `after`, with a solar value."""
+    ts = w["Timestamp"]
+    mid = ts.dt.hour.between(MIDDAY_FIRST, MIDDAY_LAST) & (ts.dt.minute == 0)
+    return w[mid & (ts > after) & w["solar_wm2"].notna()]
+
+
+def analyse_station(df: pd.DataFrame, run_day: date, station_id: int,
+                    sensors: set[str] | None = None) -> dict:
+    """Station-level result of one day: status, signals, level, camera request, notes."""
+    w = df[df["in_window"]]
+    notes = data_notes(w, sensors)
+    checked = w[w["cold_c"].notna()]
+    if checked.empty:
+        return {"status": NO_DATA, "details": {"reason": "no air temperature records in the 24 h"},
+                "notes": notes}
+    coldest = checked.loc[checked["cold_c"].idxmin()]
+    basis = set(checked["cold_basis"])
+    a, b, c = checked[checked["sig_a"]], checked[checked["sig_b"]], checked[checked["sig_c"]]
+    suspect = checked[checked["sig_a"] | checked["sig_b"] | checked["sig_c"]]
+    ratio = SNOW["snow_liquid_ratio"] / 10.0          # mm of water -> cm of snow
+    snow_mm, melt_mm = float(a["rain_mm"].sum()), float(b["rain_mm"].sum())
+    solar = bool(checked["solar_wm2"].notna().any())
+    midday = _midday(checked, suspect["Timestamp"].min()) if solar and len(suspect) else None
+    covered = (None if midday is None or midday.empty
+               else bool((midday["solar_wm2"] <= SNOW["covered_solar_max_wm2"]).all()))
+    signals = [s for s, hit in (("A", len(a)), ("B", len(b)), ("C", len(c))) if hit]
+    if signals and covered:
+        signals.append("D")
+    if "A" in signals or "B" in signals or ("C" in signals and "D" in signals):
+        level = SUSPECT_LIKELY
+    elif signals:
+        level = SUSPECT_POSSIBLE
+    else:
+        level = None
+    details = {
+        "records": int(len(checked)),
+        "cold_basis": "wet_bulb" if basis == {"wet_bulb"} else ("air" if basis == {"air"} else "mixed"),
+        "air_fallback_hours": int((checked["cold_basis"] == "air").sum()),
+        "min_cold_c": _round(coldest["cold_c"]),
+        "min_cold_at": _iso(coldest["Timestamp"]),
+        "min_wet_bulb_c": _round(checked["wet_bulb_c"].min()),
+        "min_air_temp_c": _round(checked["air_c"].min()),
+        "cold_hours": int(checked["cold"].sum()),
+        "rain_checked": bool(checked["rain_mm"].notna().any()),
+        "humidity_checked": bool(checked["rh_pct"].notna().any()),
+        "solar_checked": solar,
+        "rain_day_mm": _round(checked["rain_mm"].sum()) if checked["rain_mm"].notna().any() else None,
+        "snow_hours": int(len(a)),
+        "snow_water_mm": _round(snow_mm),
+        "melt_hours": int(len(b)),
+        "melt_water_mm": _round(melt_mm),
+        "saturated_hours": int(len(c)),
+        "max_rh_cold_pct": _round(checked.loc[checked["cold"], "rh_pct"].max(), 0),
+        "midday_solar_max_wm2": None if midday is None or midday.empty else _round(midday["solar_wm2"].max(), 0),
+        "sensor_covered": covered,
+        "est_new_snow_cm": _round(snow_mm * ratio),
+        "est_new_snow_max_cm": _round((snow_mm + melt_mm) * ratio),
+        "signals": signals,
+        "suspicion": level,
+    }
+    if level is None:
+        return {"status": NO_EVENT, "details": details, "notes": notes}
+    reason = "; ".join(SIGNALS[s] for s in signals)
+    request = CameraRequest(phenomenon=PHENOMENON, station_id=station_id, photo_at=photo_time(run_day),
+                            level=level, signals=signals, reason=f"Snow {level}: {reason}.")
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": float(len(suspect)),
+        "start": suspect["Timestamp"].min().to_pydatetime(),
+        "end": suspect["Timestamp"].max().to_pydatetime(),
+        "details": {**details, "camera_request": request.to_dict()},
+    }
+
+
+def _summary(res) -> str:
+    if res["status"] == NO_DATA:
+        return "No air temperature data from the station for the last 24 hours."
+    d = res["details"]
+    t = SNOW["t_wet_snow_max_c"]
+    what = "wet-bulb" if d["cold_basis"] != "air" else "air temperature"
+    if res["status"] == NO_EVENT:
+        if not d["cold_hours"]:
+            return f"No snow possible: lowest {what} {d['min_cold_c']:g} °C (snow needs ≤ {t:g} °C)."
+        return (f"Cold enough for snow in {d['cold_hours']} hour(s) (lowest {what} "
+                f"{d['min_cold_c']:g} °C), but no precipitation or saturated air: no snow suspected.")
+    parts = []
+    if d["snow_hours"]:
+        parts.append(f"precipitation in {d['snow_hours']} cold hour(s) ({d['snow_water_mm']:g} mm)")
+    if d["melt_hours"]:
+        parts.append(f"{d['melt_water_mm']:g} mm of rain within {LOOKBACK_H} h after cold hours "
+                     f"(snow may have melted in the rain gauge)")
+    if d["saturated_hours"]:
+        parts.append(f"{d['saturated_hours']} cold hour(s) of saturated air without rain recorded")
+    if d["sensor_covered"]:
+        parts.append("the solar sensor stayed dark at midday (it may be under snow)")
+    photo = datetime.fromisoformat(d["camera_request"]["photo_at"])
+    text = f"Snow {d['suspicion']}: " + "; ".join(parts) + "."
+    if d["est_new_snow_cm"]:
+        text += f" About {d['est_new_snow_cm']:g} cm of new snow estimated from the rain gauge."
+    return text + f" Photo requested for {photo:%d %b %H:%M} to confirm."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None,
+           station_sensors: dict[int, set[str]] | None = None) -> list[Analysis]:
+    """One Analysis per field: the 24 h of its station; EVENT = snow suspected, photo requested.
+
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering the 24 h of day_window(run_day)
+    and the LOOKBACK_H hours before; rows outside are ignored. station_sensors: 3A sensor names
+    per station, so the data notes can tell "no sensor" from "no data".
+    """
+    station_names = station_names or {}
+    station_sensors = station_sensors or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(day_hours(rows, run_day), run_day, int(sid),
+                                                   station_sensors.get(int(sid)))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station for the last "
+                                                      "24 hours."]})
+        details = dict(res["details"])
+        if res["status"] == EVENT:
+            details["in_cover_window"] = in_cover_window(res["start"].date(), f.bbch)
+            if f.bbch is not None:
+                details["bbch"] = f.bbch
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"],                       # severity stays NONE: the CV model grades
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res), details=details,
+            data_notes=list(res.get("notes", [])),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the 24 h (plus LOOKBACK_H hours) of hourly rain, temperature, humidity and solar
+    radiation and flag possible snow."""
+    run_day = run_day or date.today()
+    start, end = day_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    first = start + HOUR - timedelta(hours=LOOKBACK_H)
+    df = fetch_hourly(ids, first, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()},
+                 {i: set(s.sensors) for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: snow cannot be judged." if known is None
+                    else "No air temperature sensor (TemperaturaAria) at this station: snow cannot "
+                         "be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no air temperature sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n = sum(a.detected for a in out)
+    log.info("Snow %s: snow suspected (photo requested) for %d of %d field(s), %d station(s)",
+             run_day, n, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Snow detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/detectors/strong_wind.py b/src/damage_engine/detectors/strong_wind.py
new file mode 100644
index 0000000..42a2ffa
--- /dev/null
+++ b/src/damage_engine/detectors/strong_wind.py
@@ -0,0 +1,310 @@
+"""
+Strong wind (storm) detector: wind of at least 50 km/h (13.9 m/s). Runs every
+24 h, on the 24 h ending at 07:00 of the run day.
+
+Contract (UnipolSai Model 1506, p.8): strong wind = "at least force 7 on the
+Beaufort scale - a speed of 50 km/h or more (14 m/s)", measured below 10 m height
+(the 3A anemometers are at 2 m, so every reading is valid). The contract does not
+say over what time the speed is measured. All values are read from
+damage_engine.thresholds; sources as tagged there:
+  [CONTRACT]    UnipolSai Model 1506, Ed. 01/2021
+  [SCALE]       Beaufort scale
+  [ENGINEERING] project default, the contract gives no number (to calibrate)
+  [PROJECT]     pipeline setting, not a physical threshold
+
+Input (Diagram.png): wind speed only (3A sensor VelocitaVento2m, 3A sends hourly values only:
+  - VelocitaVento2mMax   = highest wind of the hour, m/s
+  - VelocitaVento2mMedia = mean wind of the hour, m/s
+  STRONG_WIND["use_mean_or_gust"] = "hourly_max" [ENGINEERING, chosen by the user
+  2026-10-22] picks the value that is compared with the trigger: the hourly
+  maximum, so a storm shorter than one hour is still found. 3A does not say if
+  its maximum is a 10-min mean (= the contract's measure) or a gust (higher than
+  the contract's measure): see README, Known issues. "hourly_mean" would use the
+  hourly mean instead. The other value is reported only.
+
+Event day - STRONG_WIND_SCHEDULE["run_hour"] = 7 [PROJECT]: records after 07:00
+of the previous day up to 07:00 of the run day (24 hourly records; the hourly
+Timestamp is taken as the end of the hour, as in Flood).
+
+An hour is a strong-wind hour when (STRONG_WIND):
+  - wind (the value chosen above) >= wind_trigger_ms = 13.9 m/s, operator
+    wind_trigger_op = ">=" [CONTRACT p.8, "50 km/h or more"; 50 km/h and the
+    Beaufort 7 lower bound are both 13.9 m/s].
+Event when the strong-wind time reaches min_duration_min = 10 min [ENGINEERING]
+(one 10-min averaging period). One hourly record counts as 60 min, so one hour
+is enough.
+
+Severity, the same for every field and crop stage: the highest wind of the
+strong-wind hours graded with STRONG_WIND_BANDS_MS [SCALE, Beaufort 7/8/9/10]:
+>= 13.9 / 17.2 / 20.8 / 24.5 m/s -> light / moderate / severe / extreme.
+The bands only grade an event that already passed the trigger.
+
+Cover window: COVER_WINDOWS["strong_wind"] = fruit set -> 31 Oct [CONTRACT Special
+Art.5 p.31], checked on the day of the first strong-wind hour. Fruit set = BBCH
+above the last "post_bloom" code of APPLE_BBCH_TO_STAGE [ENGINEERING, approx.],
+the same rule as Flood (a copy: a detector may not import another detector).
+Not checked by the block: the contract's rule that the effects must also show on
+orchards within GENERAL["effect_radius_km"] = 3 km (no sensor can tell).
+
+Missing sensors / missing values - the detector still runs:
+  - wind is essential: a station without a wind sensor (VelocitaVento2m; 7 of the
+    14 stations) gives "no_data" ("station lacks required sensors"); hours with
+    no wind value at all (no record, or both values empty) are not checked; a day
+    without any usable hour gives "no_data".
+  - hourly maximum missing but hourly mean present (trigger "hourly_max"): the
+    hourly mean is used for that hour. It is never above the maximum, so a hit is
+    still a hit, but a short storm in that hour may be missed.
+  - hourly mean missing (trigger "hourly_max"): only not reported for that hour.
+  - trigger "hourly_mean" with the mean missing: that hour is not checked (the
+    maximum would overstate the mean).
+  Missing columns and missing hours are read as empty (_col, hourly grid), so no
+  sensor causes an error. Every case is written to Analysis.data_notes (sensor
+  missing at the station, no data, or N of M hours missing, plus what was done).
+"""
+
+import argparse
+import logging
+import operator
+from datetime import date, datetime, time, timedelta
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.sources.fields import Field, all_fields
+from damage_engine.sources.stations import station_by_id, stations_with
+from damage_engine.sources.threea import fetch_hourly
+from damage_engine.thresholds import (
+    APPLE_BBCH_TO_STAGE,
+    COVER_WINDOWS,
+    STRONG_WIND,
+    STRONG_WIND_BANDS_MS,
+    STRONG_WIND_SCHEDULE,
+)
+
+log = logging.getLogger(__name__)
+
+BLOCK = "strong_wind"
+PHENOMENON = "strong_wind"            # contract name, key in COVER_WINDOWS
+RUN_HOUR = STRONG_WIND_SCHEDULE["run_hour"]
+MINUTES_PER_RECORD = 60               # hourly data
+HOUR = timedelta(hours=1)
+MS_TO_KMH = 3.6
+
+OPS = {"<": operator.lt, "<=": operator.le, ">": operator.gt, ">=": operator.ge}
+
+# ─────────────────────────── inputs (3A api_parameters) ───────────────────────────
+
+REQUIRED_SENSORS = ("VelocitaVento2m",)
+COL_MAX = "VelocitaVento2mMax"        # highest wind of the hour, m/s
+COL_MEAN = "VelocitaVento2mMedia"     # mean wind of the hour, m/s
+COLUMNS = [COL_MAX, COL_MEAN]
+
+# STRONG_WIND["use_mean_or_gust"] -> (column compared with the trigger, fallback column)
+TRIGGER_COLUMNS = {
+    "hourly_max": (COL_MAX, COL_MEAN),    # the mean is never above the max: a safe lower bound
+    "hourly_mean": (COL_MEAN, None),      # the max would overstate the mean: no fallback
+}
+TRIGGER_LABEL = {"hourly_max": "hourly maximum", "hourly_mean": "hourly mean"}
+
+
+def trigger_columns() -> tuple[str, str | None]:
+    """(trigger column, fallback column) for STRONG_WIND["use_mean_or_gust"]."""
+    choice = STRONG_WIND["use_mean_or_gust"]
+    if choice not in TRIGGER_COLUMNS:
+        raise ValueError(f"STRONG_WIND['use_mean_or_gust'] = {choice!r}: "
+                         f"expected one of {sorted(TRIGGER_COLUMNS)}")
+    return TRIGGER_COLUMNS[choice]
+
+
+# ─────────────────────────── cover window (same rule as Flood) ───────────────────────────
+
+def fruit_set(bbch: int) -> bool:
+    """Fruit set (start of the strong wind cover): BBCH above the last post-bloom code."""
+    return bbch > max(c for c, s in APPLE_BBCH_TO_STAGE.items() if s == "post_bloom")
+
+
+def in_cover_window(day: date, bbch: int | None):
+    """Contract cover window (fruit set -> end date). None when the stage is unknown."""
+    _, end_mmdd = COVER_WINDOWS[PHENOMENON]
+    if day.strftime("%m-%d") > end_mmdd:
+        return False
+    if bbch is None:
+        return None
+    return fruit_set(bbch)
+
+
+# ─────────────────────────── window ───────────────────────────
+
+def day_window(run_day: date):
+    """(start, end): the 24 h checked are the records after start up to and including end."""
+    end = datetime.combine(run_day, time(RUN_HOUR))
+    return end - timedelta(days=1), end
+
+
+# ─────────────────────────── detection ───────────────────────────
+
+def _col(df, name):
+    return df[name] if name in df else pd.Series(np.nan, index=df.index)
+
+
+def day_hours(rows: pd.DataFrame, run_day: date) -> pd.DataFrame:
+    """One station's rows on the full hourly grid of the 24 h (missing hour = empty row), with
+    wind_ms (the value compared with the trigger, fallback filled in), fallback_used and strong."""
+    start, end = day_window(run_day)
+    grid = pd.date_range(start + HOUR, end, freq="h", name="Timestamp")
+    rows = rows.assign(Timestamp=pd.to_datetime(rows["Timestamp"])).drop_duplicates("Timestamp")
+    df = rows.set_index("Timestamp").reindex(grid).reset_index()   # rows outside the 24 h dropped
+    main_col, fallback_col = trigger_columns()
+    wind = _col(df, main_col)
+    df["fallback_used"] = False
+    if fallback_col is not None:
+        df["fallback_used"] = wind.isna() & _col(df, fallback_col).notna()
+        wind = wind.fillna(_col(df, fallback_col))
+    df["wind_ms"] = wind
+    df["strong"] = OPS[STRONG_WIND["wind_trigger_op"]](wind, STRONG_WIND["wind_trigger_ms"])  # NaN -> False
+    return df
+
+
+def data_notes(df: pd.DataFrame) -> list[str]:
+    """Missing data of one station day (df = day_hours()) and how it was handled."""
+    notes, n = [], len(df)
+    main_col, fallback_col = trigger_columns()
+    label = TRIGGER_LABEL[STRONG_WIND["use_mean_or_gust"]]
+    no_wind = int(df["wind_ms"].isna().sum())
+    if no_wind == n:
+        return [f"No wind data in the {n} hours checked: strong wind cannot be judged."]
+    if no_wind:
+        notes.append(f"Wind missing for {no_wind} of {n} hours: those hours not checked.")
+    fallback = int(df["fallback_used"].sum())
+    if fallback:
+        notes.append(f"Hourly maximum wind missing for {fallback} of {n} hours: hourly mean used "
+                     f"(a short storm in those hours may be missed).")
+    if fallback_col is not None:                  # trigger = hourly max: the mean is reported only
+        no_mean = int((_col(df, main_col).notna() & _col(df, COL_MEAN).isna()).sum())
+        if no_mean:
+            notes.append(f"Hourly mean wind missing for {no_mean} of {n} hours: not reported for "
+                         f"those hours (the check uses the {label}).")
+    return notes
+
+
+def _round(x, digits=1):
+    return None if x is None or pd.isna(x) else round(float(x), digits)
+
+
+def _iso(t):
+    return None if t is None or pd.isna(t) else pd.Timestamp(t).isoformat(timespec="minutes")
+
+
+def analyse_station(df: pd.DataFrame) -> dict:
+    """Station-level result of one day: status, duration, times, highest wind, data notes."""
+    notes = data_notes(df)
+    checked = df[df["wind_ms"].notna()]
+    if checked.empty:
+        return {"status": NO_DATA, "details": {"reason": "no wind records in the 24 h"}, "notes": notes}
+    top = checked.loc[checked["wind_ms"].idxmax()]
+    base = {"records": int(len(checked)),
+            "trigger": STRONG_WIND["use_mean_or_gust"],
+            "max_wind_ms": _round(top["wind_ms"]),
+            "max_wind_kmh": _round(top["wind_ms"] * MS_TO_KMH),
+            "max_wind_at": _iso(top["Timestamp"]),
+            "max_mean_wind_ms": _round(_col(checked, COL_MEAN).max()),
+            "fallback_hours": int(checked["fallback_used"].sum())}
+    hits = checked[checked["strong"]]
+    minutes = len(hits) * MINUTES_PER_RECORD
+    if minutes < STRONG_WIND["min_duration_min"]:
+        return {"status": NO_EVENT, "details": base, "notes": notes}
+    return {
+        "status": EVENT,
+        "notes": notes,
+        "duration_h": minutes / 60,
+        "start": hits["Timestamp"].min().to_pydatetime(),
+        "end": hits["Timestamp"].max().to_pydatetime(),
+        "max_wind_ms": float(hits["wind_ms"].max()),
+        "details": {**base, "strong_wind_hours": int(len(hits))},
+    }
+
+
+def _summary(res, sev):
+    if res["status"] == NO_DATA:
+        return "No wind data from the station for the last 24 hours."
+    d = res["details"]
+    wind = f"{d['max_wind_ms']:g} m/s ({d['max_wind_kmh']:g} km/h)"
+    if res["status"] == NO_EVENT:
+        return f"No strong wind: highest wind {wind}."
+    return f"Strong wind for {res['duration_h']:g} h, highest wind {wind}: severity {sev.value}."
+
+
+def detect(df: pd.DataFrame, fields: list[Field], run_day: date,
+           station_names: dict[int, str] | None = None) -> list[Analysis]:
+    """One Analysis per field: the 24 h of its station, graded with the Beaufort bands.
+
+    df: hourly rows (CodiceStazione, Timestamp, COLUMNS) covering day_window(run_day);
+    rows outside it are ignored.
+    """
+    station_names = station_names or {}
+    by_station = {}
+    if not df.empty:
+        for sid, rows in df.groupby("CodiceStazione"):
+            by_station[int(sid)] = analyse_station(day_hours(rows, run_day))
+    out = []
+    for f in fields:
+        res = by_station.get(f.station_id, {"status": NO_DATA, "details": {"reason": "no records"},
+                                            "notes": ["No records from the station for the last "
+                                                      "24 hours."]})
+        sev, score = Severity.NONE, 0.0
+        details = dict(res["details"])
+        if res["status"] == EVENT:
+            sev, score = grade(res["max_wind_ms"], STRONG_WIND_BANDS_MS)
+            details["in_cover_window"] = in_cover_window(res["start"].date(), f.bbch)
+            if f.bbch is not None:
+                details["bbch"] = f.bbch
+        out.append(Analysis(
+            block=BLOCK, run_day=run_day,
+            field_id=f.id, field_name=f.name,
+            station_id=f.station_id,
+            station_name=station_names.get(f.station_id, str(f.station_id)),
+            status=res["status"], severity=sev, severity_score=score,
+            duration_h=res.get("duration_h", 0.0),
+            start=res.get("start"), end=res.get("end"),
+            summary=_summary(res, sev), details=details,
+            data_notes=res.get("notes", []),
+        ))
+    return out
+
+
+def run(run_day: date | None = None) -> list[Analysis]:
+    """Fetch the last 24 h of hourly wind for every field's station and detect strong wind."""
+    run_day = run_day or date.today()
+    start, end = day_window(run_day)
+    stations = {s.id: s for s in stations_with(*REQUIRED_SENSORS)}
+    fields = all_fields()
+    usable = [f for f in fields if f.station_id in stations]
+    ids = sorted({f.station_id for f in usable})
+    df = fetch_hourly(ids, start + HOUR, end, COLUMNS) if ids else pd.DataFrame()
+    out = detect(df, usable, run_day, {i: s.name for i, s in stations.items()})
+    for f in fields:
+        if f.station_id not in stations:
+            known = station_by_id(f.station_id)
+            note = ("Station not in the 3A catalogue: strong wind cannot be judged." if known is None
+                    else "No wind sensor (VelocitaVento2m) at this station: strong wind cannot be judged.")
+            out.append(Analysis(block=BLOCK, run_day=run_day, field_id=f.id, field_name=f.name,
+                                station_id=f.station_id,
+                                station_name=known.name if known else str(f.station_id),
+                                status=NO_DATA, summary="The station has no wind sensor.",
+                                details={"reason": "station lacks required sensors"},
+                                data_notes=[note]))
+    n_events = sum(a.detected for a in out)
+    log.info("Strong wind %s: %d event(s) over %d field(s), %d station(s)",
+             run_day, n_events, len(out), len(ids))
+    return out
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+    p = argparse.ArgumentParser(description="Run the Strong wind detector once (prints, does not save).")
+    p.add_argument("--date", type=date.fromisoformat, help="run day to evaluate (default: today)")
+    args = p.parse_args()
+    for a in run(args.date):
+        print(a.to_dict())
diff --git a/src/damage_engine/event_merge.py b/src/damage_engine/event_merge.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/hail-snow/__init__.py b/src/damage_engine/hail-snow/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/hail-snow/hail_snow.py b/src/damage_engine/hail-snow/hail_snow.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/main.py b/src/damage_engine/main.py
new file mode 100644
index 0000000..6c917c7
--- /dev/null
+++ b/src/damage_engine/main.py
@@ -0,0 +1,258 @@
+"""
+Entry point: runs the whole pipeline.
+
+    python -m damage_engine.main                    # every block now, then every station report, exit
+    python -m damage_engine.main --date 2026-10-18  # the same for a past day
+    python -m damage_engine.main --once brina       # one block only, then every station report
+    python -m damage_engine.main --serve            # keep running: the whole pipeline every day
+                                                    # (+ Hail's daytime checks on hail-risk days)
+    python -m damage_engine.main --hail-daytime 13  # Hail daytime check now (hail-risk stations)
+
+The blocks are the events of Diagram.png (station_report.EVENTS): for each one
+the pipeline loads detectors/<name>.py and calls its run(run_day). A block that
+has no code yet (no run() function), crashes or cannot reach the 3A API does
+not stop the others: its error is stored (report.submit_error) and shown in
+every station report under "Checks that could not run".
+
+Blocks that do not run every day (Drought: monthly) define due(run_day); on the
+other days the pipeline skips them and stores nothing, so the station reports keep
+showing their latest run. --once <block> runs a block on any day.
+
+After the blocks, one client report (JSON + PDF) is written for every station
+(station_report.write), in outputs/station_reports/<station>/<day>.
+
+Hail daytime checks (hail melts fast): the morning Hail run marks each station's hail-risk
+day (details["hail_risk"]). At 13:05 and 19:05 (HAIL_SCHEDULE["daytime_run_hours"]) --serve
+runs hail.run_daytime for the stations whose risk is not False, stores it as block
+"hail_daytime" with slot "13h" / "19h" (never replacing the morning run) and rewrites those
+stations' report of the day. No hail-risk station -> nothing is fetched or stored. No night
+check: the camera cannot take a photo in the dark.
+
+Run day: the blocks look at windows ending at their RUN_HOUR (Brina 06:00,
+Flood 07:00). Without --date, the run day is today once the latest RUN_HOUR has
+passed, otherwise yesterday, so a run never reads an unfinished window.
+"""
+
+import argparse
+import importlib
+import logging
+import time
+import traceback
+from datetime import date, datetime, timedelta
+
+from damage_engine.report import report, station_report
+from damage_engine.sources.fields import all_fields
+from damage_engine.sources.stations import all_stations
+
+log = logging.getLogger("damage_engine")
+
+BLOCKS = list(station_report.EVENTS)      # every event of the diagram = detectors/<name>.py
+DEFAULT_RUN_HOUR = 7                      # used only if no block defines RUN_HOUR
+
+
+class BlockNotReady(Exception):
+    """The block's file has no code to run yet."""
+
+
+def load_block(name):
+    """(module, run function) of detectors/<name>.py; BlockNotReady if it has no code yet."""
+    module_name = f"damage_engine.detectors.{name}"
+    try:
+        module = importlib.import_module(module_name)
+    except ModuleNotFoundError as e:
+        if e.name != module_name:
+            raise
+        raise BlockNotReady(f"Not available yet: the file detectors/{name}.py does not exist.")
+    func = getattr(module, "run", None)
+    if not callable(func):
+        raise BlockNotReady(f"Not available yet: the {name} check has no code "
+                            f"(detectors/{name}.py has no run() function).")
+    return module, func
+
+
+def pipeline_hour() -> int:
+    """Latest RUN_HOUR of the blocks that have code: the whole pipeline runs after it."""
+    hours = []
+    for name in BLOCKS:
+        try:
+            module, _ = load_block(name)
+        except Exception:
+            continue
+        hours.append(getattr(module, "RUN_HOUR", DEFAULT_RUN_HOUR))
+    return max(hours, default=DEFAULT_RUN_HOUR)
+
+
+def default_run_day(now: datetime | None = None) -> date:
+    now = now or datetime.now()
+    return now.date() if now.hour >= pipeline_hour() else now.date() - timedelta(days=1)
+
+
+def run_block(name: str, run_day: date) -> str | None:
+    """Run one block and store its result; return None when it worked, else the error message."""
+    try:
+        _, func = load_block(name)
+        analyses = func(run_day)
+        if not isinstance(analyses, list):
+            raise TypeError(f"run() returned {type(analyses).__name__}, not a list of Analysis")
+    except BlockNotReady as e:
+        log.warning("Block %s: %s", name, e)
+        report.submit_error(name, run_day, str(e))
+        return str(e)
+    except Exception as e:
+        log.exception("Block %s failed for %s", name, run_day)
+        message = f"The check stopped with an error: {type(e).__name__}: {e}"
+        report.submit_error(name, run_day, message, traceback.format_exc())
+        return message
+    report.submit(name, run_day, analyses)
+    log.info("Block %s: %d analysis(es) stored", name, len(analyses))
+    return None
+
+
+def station_ids() -> list[int]:
+    """Every client station: the 3A catalogue plus any station named in data/fields.json."""
+    return sorted({s.id for s in all_stations()} | {f.station_id for f in all_fields()})
+
+
+def write_station_reports(run_day: date, ids: list[int] | None = None) -> dict[int, str | None]:
+    """One client report (JSON + PDF) per station (ids: only these); station -> None, or the
+    error message."""
+    out = {}
+    for sid in (station_ids() if ids is None else ids):
+        try:
+            station_report.write(sid, run_day)
+            out[sid] = None
+        except Exception as e:
+            log.exception("Station report %s failed", sid)
+            out[sid] = f"{type(e).__name__}: {e}"
+    return out
+
+
+def not_due(name: str, run_day: date) -> str | None:
+    """Why a block is skipped on run_day (its due() says no), or None when it runs."""
+    try:
+        module, _ = load_block(name)
+    except Exception:
+        return None                                    # run_block stores the problem
+    due = getattr(module, "due", None)
+    if callable(due) and not due(run_day):
+        return f"not due on {run_day} (see its due())"
+    return None
+
+
+def run_pipeline(run_day: date, blocks: list[str] | None = None) -> dict:
+    """Run the blocks (default: all), then write every station report. Returns a summary.
+
+    Without `blocks`, a block whose due(run_day) is False is skipped (summary "skipped");
+    blocks named in `blocks` (--once) always run.
+    """
+    log.info("Pipeline for run day %s", run_day)
+    skipped = {} if blocks else {n: why for n in BLOCKS if (why := not_due(n, run_day))}
+    block_errors = {name: run_block(name, run_day) for name in (blocks or BLOCKS)
+                    if name not in skipped}
+    station_errors = write_station_reports(run_day)
+    _print_summary(run_day, block_errors, station_errors, skipped)
+    return {"blocks": block_errors, "skipped": skipped, "stations": station_errors}
+
+
+def _print_summary(run_day, block_errors, station_errors, skipped=None):
+    lines = [f"\nPIPELINE - run day {run_day}", "Blocks:"]
+    for name, err in block_errors.items():
+        lines.append(f"  {name:<17} {'OK' if err is None else 'ERROR - ' + err}")
+    for name, why in (skipped or {}).items():
+        lines.append(f"  {name:<17} skipped - {why}")
+    ok = sum(e is None for e in station_errors.values())
+    lines.append(f"Station reports: {ok} of {len(station_errors)} written to "
+                 f"{station_report.STATION_REPORTS_DIR}")
+    lines += [f"  station {sid}: ERROR - {err}" for sid, err in station_errors.items() if err]
+    print("\n".join(lines))
+
+
+def hail_risk_stations(day: date, root=None) -> set[int] | None:
+    """Stations whose Hail daytime checks run on `day`: hail_risk not False in that day's morning
+    Hail run. None = every station (no usable morning run, inside the hail season)."""
+    hail, _ = load_block("hail")
+    morning = next((r for r in report.day_runs("hail", day, root) if not r.get("slot")), None)
+    if morning is None or morning.get("error"):
+        return None if hail.in_season(day) else set()
+    return {a.station_id for a in morning["analyses"] if a.details.get("hail_risk") is not False}
+
+
+def run_hail_daytime(day: date, hour: int) -> dict:
+    """One Hail daytime check: hail-risk stations only, stored as block "hail_daytime" with slot
+    "<hour>h", then those stations' report of the day is rewritten. Returns a summary."""
+    hail, _ = load_block("hail")
+    ids = hail_risk_stations(day)
+    slot = f"{hour:02d}h"
+    if ids is not None and not ids:
+        log.info("Hail daytime check %s %s: no hail-risk station, nothing to check", day, slot)
+        print(f"\nHAIL DAYTIME CHECK - {day} {hour:02d}:00\nNo hail-risk station today: skipped.")
+        return {"error": None, "stations": {}, "checked": []}
+    try:
+        analyses = hail.run_daytime(day, hour, ids)
+    except Exception as e:
+        log.exception("Hail daytime check %s %s failed", day, slot)
+        message = f"The check stopped with an error: {type(e).__name__}: {e}"
+        report.submit_error(hail.DAYTIME_BLOCK, day, message, traceback.format_exc(), slot=slot)
+        error = message
+    else:
+        report.submit(hail.DAYTIME_BLOCK, day, analyses, slot=slot)
+        error = None
+    checked = sorted(ids) if ids is not None else station_ids()
+    stations = write_station_reports(day, checked)
+    print(f"\nHAIL DAYTIME CHECK - {day} {hour:02d}:00 ({len(checked)} station(s))\n  "
+          f"{'OK' if error is None else 'ERROR - ' + error}")
+    return {"error": error, "stations": stations, "checked": checked}
+
+
+def next_job(now: datetime) -> tuple[datetime, int | None]:
+    """(time, hour) of the next --serve job: hour None = the whole pipeline (after the latest block
+    RUN_HOUR), an hour = Hail's daytime check at that hour. All at minute 5."""
+    try:
+        hail, _ = load_block("hail")
+        daytime = list(hail.DAYTIME_HOURS)
+    except Exception:
+        daytime = []
+    jobs = []
+    for hour, kind in [(pipeline_hour(), None), *[(h, h) for h in daytime]]:
+        at = now.replace(hour=hour, minute=5, second=0, microsecond=0)
+        if at <= now:
+            at += timedelta(days=1)
+        jobs.append((at, kind))
+    return min(jobs, key=lambda j: j[0])
+
+
+def serve():
+    """Keep running: the whole pipeline once a day after the latest block RUN_HOUR, and Hail's
+    daytime checks (13:05, 19:05) on hail-risk days."""
+    while True:
+        at, hour = next_job(datetime.now())
+        log.info("Next run: %s (%s)", at, "pipeline" if hour is None else "hail daytime check")
+        while (left := (at - datetime.now()).total_seconds()) > 0:
+            time.sleep(min(left, 60))
+        if hour is None:
+            run_pipeline(at.date())
+        else:
+            run_hail_daytime(at.date(), hour)
+
+
+def main():
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    logging.getLogger("fontTools").setLevel(logging.WARNING)   # PDF font subsetting is very chatty
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--once", choices=BLOCKS, help="run only this block (then every station report)")
+    p.add_argument("--date", type=date.fromisoformat, help="run day (default: see Run day above)")
+    p.add_argument("--serve", action="store_true", help="keep running, whole pipeline every day")
+    p.add_argument("--hail-daytime", type=int, metavar="HOUR",
+                   help="Hail daytime check at HOUR (13 or 19) for the hail-risk stations; with "
+                        "--date for a past day (default: today)")
+    args = p.parse_args()
+    if args.serve:
+        serve()
+    elif args.hail_daytime is not None:
+        run_hail_daytime(args.date or date.today(), args.hail_daytime)
+    else:
+        run_pipeline(args.date or default_run_day(), [args.once] if args.once else None)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/damage_engine/report/__init__.py b/src/damage_engine/report/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/report/assets/3a_logo.png b/src/damage_engine/report/assets/3a_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..a60f7dc8898965d83dfd7870493eb37a93ef429d
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diff --git a/src/damage_engine/report/assets/fonts/DejaVu-LICENSE.txt b/src/damage_engine/report/assets/fonts/DejaVu-LICENSE.txt
new file mode 100644
index 0000000..df52c17
--- /dev/null
+++ b/src/damage_engine/report/assets/fonts/DejaVu-LICENSE.txt
@@ -0,0 +1,187 @@
+Fonts are (c) Bitstream (see below). DejaVu changes are in public domain.
+Glyphs imported from Arev fonts are (c) Tavmjong Bah (see below)
+
+
+Bitstream Vera Fonts Copyright
+------------------------------
+
+Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Bitstream Vera is
+a trademark of Bitstream, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of the fonts accompanying this license ("Fonts") and associated
+documentation files (the "Font Software"), to reproduce and distribute the
+Font Software, including without limitation the rights to use, copy, merge,
+publish, distribute, and/or sell copies of the Font Software, and to permit
+persons to whom the Font Software is furnished to do so, subject to the
+following conditions:
+
+The above copyright and trademark notices and this permission notice shall
+be included in all copies of one or more of the Font Software typefaces.
+
+The Font Software may be modified, altered, or added to, and in particular
+the designs of glyphs or characters in the Fonts may be modified and
+additional glyphs or characters may be added to the Fonts, only if the fonts
+are renamed to names not containing either the words "Bitstream" or the word
+"Vera".
+
+This License becomes null and void to the extent applicable to Fonts or Font
+Software that has been modified and is distributed under the "Bitstream
+Vera" names.
+
+The Font Software may be sold as part of a larger software package but no
+copy of one or more of the Font Software typefaces may be sold by itself.
+
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT,
+TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL BITSTREAM OR THE GNOME
+FOUNDATION BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING
+ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF
+THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE
+FONT SOFTWARE.
+
+Except as contained in this notice, the names of Gnome, the Gnome
+Foundation, and Bitstream Inc., shall not be used in advertising or
+otherwise to promote the sale, use or other dealings in this Font Software
+without prior written authorization from the Gnome Foundation or Bitstream
+Inc., respectively. For further information, contact: fonts at gnome dot
+org.
+
+Arev Fonts Copyright
+------------------------------
+
+Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the fonts accompanying this license ("Fonts") and
+associated documentation files (the "Font Software"), to reproduce
+and distribute the modifications to the Bitstream Vera Font Software,
+including without limitation the rights to use, copy, merge, publish,
+distribute, and/or sell copies of the Font Software, and to permit
+persons to whom the Font Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright and trademark notices and this permission notice
+shall be included in all copies of one or more of the Font Software
+typefaces.
+
+The Font Software may be modified, altered, or added to, and in
+particular the designs of glyphs or characters in the Fonts may be
+modified and additional glyphs or characters may be added to the
+Fonts, only if the fonts are renamed to names not containing either
+the words "Tavmjong Bah" or the word "Arev".
+
+This License becomes null and void to the extent applicable to Fonts
+or Font Software that has been modified and is distributed under the 
+"Tavmjong Bah Arev" names.
+
+The Font Software may be sold as part of a larger software package but
+no copy of one or more of the Font Software typefaces may be sold by
+itself.
+
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL
+TAVMJONG BAH BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
+
+Except as contained in this notice, the name of Tavmjong Bah shall not
+be used in advertising or otherwise to promote the sale, use or other
+dealings in this Font Software without prior written authorization
+from Tavmjong Bah. For further information, contact: tavmjong @ free
+. fr.
+
+TeX Gyre DJV Math
+-----------------
+Fonts are (c) Bitstream (see below). DejaVu changes are in public domain.
+
+Math extensions done by B. Jackowski, P. Strzelczyk and P. Pianowski
+(on behalf of TeX users groups) are in public domain.
+
+Letters imported from Euler Fraktur from AMSfonts are (c) American
+Mathematical Society (see below).
+Bitstream Vera Fonts Copyright
+Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Bitstream Vera
+is a trademark of Bitstream, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of the fonts accompanying this license (“Fonts”) and associated
+documentation
+files (the “Font Software”), to reproduce and distribute the Font Software,
+including without limitation the rights to use, copy, merge, publish,
+distribute,
+and/or sell copies of the Font Software, and to permit persons  to whom
+the Font Software is furnished to do so, subject to the following
+conditions:
+
+The above copyright and trademark notices and this permission notice
+shall be
+included in all copies of one or more of the Font Software typefaces.
+
+The Font Software may be modified, altered, or added to, and in particular
+the designs of glyphs or characters in the Fonts may be modified and
+additional
+glyphs or characters may be added to the Fonts, only if the fonts are
+renamed
+to names not containing either the words “Bitstream” or the word “Vera”.
+
+This License becomes null and void to the extent applicable to Fonts or
+Font Software
+that has been modified and is distributed under the “Bitstream Vera”
+names.
+
+The Font Software may be sold as part of a larger software package but
+no copy
+of one or more of the Font Software typefaces may be sold by itself.
+
+THE FONT SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT,
+TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL BITSTREAM OR THE GNOME
+FOUNDATION
+BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL,
+SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN
+ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR
+INABILITY TO USE
+THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
+Except as contained in this notice, the names of GNOME, the GNOME
+Foundation,
+and Bitstream Inc., shall not be used in advertising or otherwise to promote
+the sale, use or other dealings in this Font Software without prior written
+authorization from the GNOME Foundation or Bitstream Inc., respectively.
+For further information, contact: fonts at gnome dot org.
+
+AMSFonts (v. 2.2) copyright
+
+The PostScript Type 1 implementation of the AMSFonts produced by and
+previously distributed by Blue Sky Research and Y&Y, Inc. are now freely
+available for general use. This has been accomplished through the
+cooperation
+of a consortium of scientific publishers with Blue Sky Research and Y&Y.
+Members of this consortium include:
+
+Elsevier Science IBM Corporation Society for Industrial and Applied
+Mathematics (SIAM) Springer-Verlag American Mathematical Society (AMS)
+
+In order to assure the authenticity of these fonts, copyright will be
+held by
+the American Mathematical Society. This is not meant to restrict in any way
+the legitimate use of the fonts, such as (but not limited to) electronic
+distribution of documents containing these fonts, inclusion of these fonts
+into other public domain or commercial font collections or computer
+applications, use of the outline data to create derivative fonts and/or
+faces, etc. However, the AMS does require that the AMS copyright notice be
+removed from any derivative versions of the fonts which have been altered in
+any way. In addition, to ensure the fidelity of TeX documents using Computer
+Modern fonts, Professor Donald Knuth, creator of the Computer Modern faces,
+has requested that any alterations which yield different font metrics be
+given a different name.
+
+$Id$
diff --git a/src/damage_engine/report/assets/fonts/DejaVuSans.ttf b/src/damage_engine/report/assets/fonts/DejaVuSans.ttf
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diff --git a/src/damage_engine/report/assets/fonts/NotoSans-OFL.txt b/src/damage_engine/report/assets/fonts/NotoSans-OFL.txt
new file mode 100644
index 0000000..6843f31
--- /dev/null
+++ b/src/damage_engine/report/assets/fonts/NotoSans-OFL.txt
@@ -0,0 +1,93 @@
+Copyright 2022 The Noto Project Authors (https://github.com/notofonts/latin-greek-cyrillic)
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+https://scripts.sil.org/OFL
+
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded, 
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting -- in part or in whole -- any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
diff --git a/src/damage_engine/report/assets/fonts/NotoSans-Regular.ttf b/src/damage_engine/report/assets/fonts/NotoSans-Regular.ttf
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diff --git a/src/damage_engine/report/report.py b/src/damage_engine/report/report.py
new file mode 100644
index 0000000..c9e845a
--- /dev/null
+++ b/src/damage_engine/report/report.py
@@ -0,0 +1,301 @@
+"""
+Report block: collects the Analysis list of every block run, writes a simple
+report per block, keeps the last REPORT["history_runs"] runs of each block,
+and builds the report by field: each field (sorted by name), then every block
+that analysed it (brina, flood, ...), then the next field.
+
+A block run is sent here by main.py:
+
+    from damage_engine.report import report
+    report.submit("brina", run_day, analyses)
+    report.submit_error("freezing", run_day, "No code yet: ...")   # the block could not run
+
+A failed run is stored like a normal one, with an "error" message and no
+analyses, so the block report, the field report and the station reports show it.
+
+Storage (one run = one block + one run day; running the same day again replaces it):
+
+    outputs/reports/<block>/<run_day>.json   all Analysis of the run
+    outputs/reports/<block>/<run_day>.txt    readable block report
+
+A block that runs several times a day (Hail's daytime checks, block "hail_daytime") passes a
+slot ("13h", "19h"): each check is its own run, <run_day>_<slot>.json / .txt, and only the
+same slot of the same day replaces it. The last HISTORY_RUNS run DAYS are kept (all slots).
+    outputs/reports/fields_report.txt        report by field, newest run of every block
+
+For the farmer:
+    python -m damage_engine.report.report                         # choose a field (or all) interactively
+    python -m damage_engine.report.report --all [--date 2026-10-18]   # every field, block by block
+    python -m damage_engine.report.report --fields                # list fields
+    python -m damage_engine.report.report --field ST3             # latest report of a field
+    python -m damage_engine.report.report --field ST3 --history   # dates of past reports
+    python -m damage_engine.report.report --field ST3 --date 2026-10-18
+    python -m damage_engine.report.report --block brina [--date 2026-10-18]
+"""
+
+import argparse
+import json
+from datetime import date, datetime
+from pathlib import Path
+
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+from damage_engine.thresholds import REPORT
+
+REPORTS_DIR = Path(__file__).resolve().parents[3] / "outputs" / "reports"
+HISTORY_RUNS = REPORT["history_runs"]
+FIELDS_REPORT_FILE = "fields_report.txt"
+
+STATUS_TEXT = {EVENT: "EVENT", NO_EVENT: "no event", NO_DATA: "no data"}
+
+
+# ─────────────────────────── storage ───────────────────────────
+
+def submit(block: str, run_day: date, analyses: list[Analysis], root: Path | None = None,
+           slot: str | None = None) -> str:
+    """Store one block run, write its block report, keep the last HISTORY_RUNS run days.
+    slot: for blocks that run several times a day (e.g. "13h"); None = one run per day."""
+    return _store(block, run_day, {"analyses": [a.to_dict() for a in analyses]}, root, slot)
+
+
+def submit_error(block: str, run_day: date, message: str, detail: str = "",
+                 root: Path | None = None, slot: str | None = None) -> str:
+    """Store a block run that failed (no code yet, crash, API error).
+
+    message: one sentence shown in every report; detail: e.g. the traceback, kept
+    only in outputs/reports/<block>/<run_day>.json.
+    """
+    return _store(block, run_day, {"error": message, "error_detail": detail, "analyses": []}, root,
+                  slot)
+
+
+def _store(block: str, run_day: date, body: dict, root: Path | None, slot: str | None = None) -> str:
+    folder = (root or REPORTS_DIR) / block
+    folder.mkdir(parents=True, exist_ok=True)
+    run = {"block": block, "run_day": run_day.isoformat(),
+           "created_at": datetime.now().isoformat(timespec="seconds"), **body}
+    name = run_day.isoformat()
+    if slot:
+        run["slot"] = slot
+        name += f"_{slot}"
+    (folder / f"{name}.json").write_text(json.dumps(run, indent=2, ensure_ascii=False), encoding="utf-8")
+    text = block_report(_parse(run))
+    (folder / f"{name}.txt").write_text(text, encoding="utf-8")
+    _prune(folder)
+    (root or REPORTS_DIR).joinpath(FIELDS_REPORT_FILE).write_text(fields_report(root=root),
+                                                                  encoding="utf-8")
+    return text
+
+
+def _prune(folder: Path):
+    """Keep the runs of the last HISTORY_RUNS run days (a file name starts with its day)."""
+    files = sorted(folder.glob("*.json"), reverse=True)
+    keep = sorted({f.name[:10] for f in files}, reverse=True)[:HISTORY_RUNS]
+    for old in files:
+        if old.name[:10] not in keep:
+            old.unlink()
+            old.with_suffix(".txt").unlink(missing_ok=True)
+
+
+def _parse(run: dict) -> dict:
+    return {**run, "run_day": date.fromisoformat(run["run_day"]),
+            "analyses": [Analysis.from_dict(a) for a in run["analyses"]]}
+
+
+def blocks(root: Path | None = None) -> list[str]:
+    root = root or REPORTS_DIR
+    return sorted(p.name for p in root.iterdir() if p.is_dir()) if root.exists() else []
+
+
+def runs(block: str, root: Path | None = None) -> list[dict]:
+    """Stored runs of one block, newest first."""
+    folder = (root or REPORTS_DIR) / block
+    files = sorted(folder.glob("*.json"), reverse=True) if folder.exists() else []
+    return [_parse(json.loads(f.read_text(encoding="utf-8"))) for f in files]
+
+
+def latest_run(block: str, as_of: date | None = None, root: Path | None = None):
+    """Newest run of a block with run_day <= as_of (None = newest of all)."""
+    return next((r for r in runs(block, root) if as_of is None or r["run_day"] <= as_of), None)
+
+
+def day_runs(block: str, day: date, root: Path | None = None) -> list[dict]:
+    """Every run of a block on one run day (all slots), oldest first."""
+    return sorted((r for r in runs(block, root) if r["run_day"] == day), key=lambda r: r.get("slot") or "")
+
+
+def fields(root: Path | None = None) -> dict[str, str]:
+    """field_id -> field name, for every field in any stored run."""
+    out = {}
+    for b in blocks(root):
+        for r in runs(b, root):
+            for a in r["analyses"]:
+                out.setdefault(a.field_id, a.field_name)
+    return dict(sorted(out.items()))
+
+
+def field_report_dates(field_id: str, root: Path | None = None) -> list[date]:
+    """Run days (newest first) on which any block analysed this field."""
+    days = {r["run_day"] for b in blocks(root) for r in runs(b, root)
+            if any(a.field_id == field_id for a in r["analyses"])}
+    return sorted(days, reverse=True)
+
+
+# ─────────────────────────── text reports ───────────────────────────
+
+def _fmt_time(t):
+    return t.strftime("%Y-%m-%d %H:%M") if t else "-"
+
+
+def block_report(run: dict) -> str:
+    """Simple report of one block run: counts and one line per field."""
+    when = f"run day {run['run_day']}" + (f", check {run['slot']}" if run.get("slot") else "")
+    if run.get("error"):
+        return (f"{run['block'].upper()} report - {when}\n"
+                f"Created {run.get('created_at', '-')}\n\nERROR: {run['error']}\n")
+    items = run["analyses"]
+    events = [a for a in items if a.status == EVENT]
+    by_sev = {s: sum(a.severity == s for a in events) for s in Severity}
+    sev_text = ", ".join(f"{s.value} {n}" for s, n in by_sev.items() if n) or "-"
+    lines = [
+        f"{run['block'].upper()} report - {when}",
+        f"Created {run.get('created_at', '-')} | {len(items)} field(s), "
+        f"{len({a.station_id for a in items})} station(s)",
+        f"Events: {len(events)} ({sev_text}) | no event: "
+        f"{sum(a.status == NO_EVENT for a in items)} | no data: {sum(a.status == NO_DATA for a in items)}",
+        "",
+        f"{'FIELD':<28} {'STATION':<22} {'STATUS':<9} {'SEVERITY':<9} {'SCORE':>5} {'HOURS':>5}",
+    ]
+    order = sorted(items, key=lambda a: (-a.severity.level, a.status != EVENT, a.field_id))
+    for a in order:
+        lines.append(f"{(a.field_id + ' ' + a.field_name)[:28]:<28} "
+                     f"{(str(a.station_id) + ' ' + a.station_name)[:22]:<22} "
+                     f"{STATUS_TEXT[a.status]:<9} {a.severity.value:<9} "
+                     f"{a.severity_score:>5.2f} {a.duration_h:>5g}")
+    noted = [a for a in order if a.data_notes]
+    if noted:
+        lines += ["", "Missing sensors / data:"]
+        for a in noted:
+            lines += [f"  {a.field_id} (station {a.station_id} {a.station_name}): {n}" for n in a.data_notes]
+    return "\n".join(lines) + "\n"
+
+
+def _field_section(block: str, run: dict | None, field_id: str) -> list[str]:
+    head = f"[{block.upper()}]"
+    if run is None:
+        return [f"{head} no run yet", ""]
+    if run.get("error"):
+        return [f"{head} run {run['run_day']}: ERROR - {run['error']}", ""]
+    mine = [a for a in run["analyses"] if a.field_id == field_id]
+    if not mine:
+        return [f"{head} run {run['run_day']}: this field was not analysed", ""]
+    lines = []
+    for a in mine:
+        result = STATUS_TEXT[a.status]
+        if a.status == EVENT:
+            result += f" - severity {a.severity.value.upper()} (score {a.severity_score:.2f})"
+        lines += [f"{head} run {a.run_day}",
+                  f"  Station:  {a.station_id} {a.station_name}",
+                  f"  Result:   {result}"]
+        if a.status == EVENT:
+            lines.append(f"  Time:     {_fmt_time(a.start)} -> {_fmt_time(a.end)} ({a.duration_h:g} h)")
+        if a.summary:
+            lines.append(f"  Summary:  {a.summary}")
+        lines += [f"  Data:     {n}" for n in a.data_notes]
+        lines += [f"    {k}: {v}" for k, v in a.details.items()]
+        lines.append("")
+    return lines
+
+
+def field_report(field_id: str, as_of: date | None = None, root: Path | None = None) -> str:
+    """All blocks for one field together: for each block its newest run up to as_of."""
+    name = fields(root).get(field_id, "?")
+    lines = [f"Field report: {field_id} - {name}",
+             f"As of: {as_of or 'latest'}", ""]
+    return "\n".join(lines + _field_sections(field_id, _latest_runs(as_of, root)))
+
+
+def fields_report(as_of: date | None = None, root: Path | None = None) -> str:
+    """Report by field: each field (sorted by name), then all its block results, then the next field."""
+    latest = _latest_runs(as_of, root)
+    known = sorted(fields(root).items(), key=lambda kv: (kv[1], kv[0]))
+    lines = ["REPORT BY FIELD",
+             f"As of: {as_of or 'latest'} | created {datetime.now():%Y-%m-%d %H:%M} | "
+             f"{len(known)} field(s) | blocks: {', '.join(latest) or '-'}", ""]
+    if not known:
+        lines.append("No block has run yet.")
+    rule = "=" * 72
+    for fid, name in known:
+        lines += [rule, f"FIELD {name} [{fid}]", rule]
+        lines += _field_sections(fid, latest)
+    return "\n".join(lines) + "\n"
+
+
+def _latest_runs(as_of: date | None, root: Path | None) -> dict[str, dict | None]:
+    """block -> its newest run up to as_of (blocks in alphabetical order)."""
+    return {b: latest_run(b, as_of, root) for b in blocks(root)}
+
+
+def _field_sections(field_id: str, latest: dict[str, dict | None]) -> list[str]:
+    """Every block's section for one field."""
+    lines = []
+    for b, run in latest.items():
+        lines += _field_section(b, run, field_id)
+    return lines or ["No block has run yet."]
+
+
+# ─────────────────────────── command line ───────────────────────────
+
+def _interactive():
+    known = list(fields().items())
+    if not known:
+        print("No reports yet.")
+        return
+    print(f"{0:>3}. all fields")
+    for i, (fid, name) in enumerate(known, 1):
+        print(f"{i:>3}. {fid}  {name}")
+    pick = input("Field number: ").strip()
+    if pick == "0":
+        print("\n" + fields_report())
+        return
+    if not pick.isdigit() or not 1 <= int(pick) <= len(known):
+        print("Unknown field.")
+        return
+    fid = known[int(pick) - 1][0]
+    print("\n" + field_report(fid))
+    days = field_report_dates(fid)[1:]
+    if days and input("Show a past report? [y/N] ").strip().lower() == "y":
+        for i, d in enumerate(days, 1):
+            print(f"{i:>3}. {d}")
+        pick = input("Report number: ").strip()
+        if pick.isdigit() and 1 <= int(pick) <= len(days):
+            print("\n" + field_report(fid, days[int(pick) - 1]))
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--fields", action="store_true", help="list the fields that have reports")
+    p.add_argument("--all", action="store_true", help="report by field: every field, all its blocks")
+    p.add_argument("--field", help="field id")
+    p.add_argument("--history", action="store_true", help="with --field: list past report dates")
+    p.add_argument("--block", help="show a block report")
+    p.add_argument("--date", type=date.fromisoformat, help="report as of this run day")
+    args = p.parse_args()
+    if args.all:
+        print(fields_report(args.date))
+    elif args.fields:
+        for fid, name in fields().items():
+            print(f"{fid}  {name}")
+    elif args.field and args.history:
+        for d in field_report_dates(args.field):
+            print(d)
+    elif args.field:
+        print(field_report(args.field, args.date))
+    elif args.block:
+        run = latest_run(args.block, args.date)
+        print(block_report(run) if run else f"No {args.block} run stored.")
+    else:
+        _interactive()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/damage_engine/report/station_pdf.py b/src/damage_engine/report/station_pdf.py
new file mode 100644
index 0000000..c5c8af4
--- /dev/null
+++ b/src/damage_engine/report/station_pdf.py
@@ -0,0 +1,478 @@
+"""
+Draws the station report (the dict built by station_report.build) as a PDF.
+Pure Python (fpdf2), no LaTeX: it runs anywhere the pipeline runs.
+
+Assets in report/assets/: 3a_logo.png (from www.3acloud.it/favicon.ico),
+Noto Sans (text) and DejaVu Sans (fallback for ≤ ≥ →), both free licences
+(the licence files are next to the fonts).
+
+    from damage_engine.report import station_pdf
+    station_pdf.render(content, Path("566.pdf"))
+"""
+
+from datetime import date, datetime
+from pathlib import Path
+
+from fpdf import FPDF
+from fpdf.enums import XPos, YPos
+from fpdf.fonts import FontFace
+
+ASSETS = Path(__file__).resolve().parent / "assets"
+LOGO = ASSETS / "3a_logo.png"
+FONTS = ASSETS / "fonts"
+
+# colours (RGB): text, 3A brand (from the logo), severity scale
+INK, MUTED, LINE, PANEL = (33, 37, 41), (108, 117, 125), (218, 222, 226), (245, 247, 249)
+ZEBRA = (250, 251, 252)
+BLUE, GREEN, ORANGE = (88, 140, 201), (118, 146, 60), (227, 109, 38)
+SEVERITY_COLOR = {"none": (118, 146, 60), "light": (214, 170, 20), "moderate": (227, 109, 38),
+                  "severe": (200, 45, 45), "extreme": (120, 20, 30)}
+NO_DATA_COLOR = (140, 147, 155)
+ERROR_COLOR = (125, 60, 152)     # a block that could not run (not a severity)
+
+STATUS_TEXT = {"crossed": "Crossed", "not_crossed": "Not crossed", "not_applied": "Not applied",
+               "not_recorded": "Not recorded", "info": "-"}
+STATUS_COLOR = {"crossed": (200, 45, 45), "not_crossed": GREEN, "not_applied": MUTED,
+                "not_recorded": MUTED, "info": MUTED}
+RESULT_TEXT = {"event": "Event", "no_event": "No event", "no_data": "No data",
+               "not_run": "Not available yet", "error": "Error"}
+
+LEFT, WIDTH = 16, 178          # page margin and content width (A4 = 210 mm)
+
+
+def _dt(iso, with_year=False):
+    if not iso:
+        return "-"
+    t = datetime.fromisoformat(iso)
+    return t.strftime("%d %b %Y %H:%M" if with_year else "%d %b %H:%M")
+
+
+def _day(iso):
+    return date.fromisoformat(iso).strftime("%d %B %Y")
+
+
+def _val(x, unit):
+    return "-" if x is None else f"{x:g} {unit}".strip()
+
+
+def _period(b):
+    """Period a block checked: its label for monthly blocks ("Jul–Sep 2026"), else start → end."""
+    w = b.get("window")
+    if not w:
+        return "-"
+    return w.get("label") or f"{_dt(w['start'])} → {_dt(w['end'])}"
+
+
+def _missing(b, sep="\n"):
+    """Missing sensors of one event, e.g. 'wind'. No counts of other stations."""
+    return sep.join(m["name"] for m in b["missing_sensors"]) or "none"
+
+
+def _suspected(field):
+    """Snow (later hail): possible from the weather data, a photo is asked for to confirm."""
+    return bool(field and field.get("suspected"))
+
+
+def _sev_color(field):
+    if _suspected(field):
+        return ORANGE
+    if field["status"] == "event":
+        return SEVERITY_COLOR[field["severity"]]
+    return SEVERITY_COLOR["none"] if field["status"] == "no_event" else NO_DATA_COLOR
+
+
+class _Doc(FPDF):
+    def __init__(self, content):
+        super().__init__(orientation="P", unit="mm", format="A4")
+        self.c = content
+        self.set_margins(LEFT, 16, LEFT)
+        self.set_auto_page_break(True, margin=20)
+        self.add_font("Noto", "", FONTS / "NotoSans-Regular.ttf")
+        self.add_font("Noto", "B", FONTS / "NotoSans-Bold.ttf")
+        self.add_font("Noto", "I", FONTS / "NotoSans-Italic.ttf")
+        self.add_font("DejaVu", "", FONTS / "DejaVuSans.ttf")
+        self.set_fallback_fonts(["DejaVu"], exact_match=False)
+        st = content["station"]
+        self.set_title(f"Weather damage report - station {st['id']} {st['name']} - {content['report_day']}")
+        self.set_author("Damage Engine")
+        self.set_creator("Damage Engine (fpdf2)")
+
+    # every page after the first gets a small header
+    def header(self):
+        if self.page_no() == 1:
+            return
+        st = self.c["station"]
+        self.image(LOGO, LEFT, 8, h=8)
+        self.font(8, color=MUTED)
+        self.set_xy(LEFT + 10, 9.5)
+        self.cell(WIDTH - 10, 5, f"Weather damage report  ·  {st['name']} (station {st['id']})  ·  "
+                                 f"{_day(self.c['report_day'])}", align="R")
+        self._brand_bar(18, 0.6)
+        self.set_y(24)
+
+    def footer(self):
+        self.set_y(-14)
+        self.set_draw_color(*LINE)
+        self.line(LEFT, self.get_y(), LEFT + WIDTH, self.get_y())
+        self.font(7.5, color=MUTED)
+        self.set_y(-12)
+        st = self.c["station"]
+        self.cell(WIDTH / 2, 5, f"Data: 3A weather station {st['id']} {st['name']}  ·  "
+                                f"generated {_dt(self.c['created_at'], True)}")
+        self.cell(WIDTH / 2, 5, f"Page {self.page_no()} of {{nb}}", align="R")
+
+    # ── helpers ──
+    def font(self, size, style="", color=INK):
+        self.set_font("Noto", style, size)
+        self.set_text_color(*color)
+
+    def _brand_bar(self, y, h):
+        for i, col in enumerate((BLUE, GREEN, ORANGE)):
+            self.set_fill_color(*col)
+            self.rect(LEFT + i * WIDTH / 3, y, WIDTH / 3, h, style="F")
+
+    def _label(self, x, y, w, label, value, size=11):
+        self.set_xy(x, y)
+        self.font(7, "B", MUTED)
+        self.cell(w, 4, label.upper(), new_x=XPos.LEFT, new_y=YPos.NEXT)
+        self.font(size, "B")
+        self.multi_cell(w, 5.2, value)
+
+    def _chip(self, x, y, text, color, w=None):
+        self.font(8, "B", (255, 255, 255))
+        w = w or self.get_string_width(text) + 6
+        self.set_fill_color(*color)
+        self.rect(x, y, w, 5.6, style="F", round_corners=True, corner_radius=1.4)
+        self.set_xy(x, y + 0.2)
+        self.cell(w, 5.2, text, align="C")
+        return w
+
+    def multi_cell(self, *args, **kw):
+        kw.setdefault("align", "L")            # fpdf2 justifies by default: wide gaps
+        return super().multi_cell(*args, **kw)
+
+    def table(self, *args, **kw):
+        self.set_fill_color(255, 255, 255)
+        return super().table(*args, **kw)
+
+    def _ensure(self, h):
+        if self.will_page_break(h):
+            self.add_page()
+
+    def _heading(self, text, color=BLUE):
+        self._ensure(30)
+        self.ln(3)
+        y = self.get_y()
+        self.set_fill_color(*color)
+        self.rect(LEFT, y + 1, 1.6, 6, style="F")
+        self.set_xy(LEFT + 4, y)
+        self.font(13, "B")
+        self.cell(WIDTH - 4, 8, text, new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(1)
+
+    # ── page 1 top ──
+    def title_block(self):
+        c, st = self.c, self.c["station"]
+        self.image(LOGO, LEFT, 13, h=17)
+        self.set_xy(LEFT + 21, 14)
+        self.font(18, "B")
+        self.cell(100, 8, "Weather Damage Report", new_x=XPos.LEFT, new_y=YPos.NEXT)
+        self.font(9, color=MUTED)
+        self.cell(100, 5, "Automatic assessment from 3A weather-station data")
+        self.set_xy(LEFT + WIDTH - 70, 14)
+        self.font(7, "B", MUTED)
+        self.cell(70, 4, "REPORT DAY", align="R", new_x=XPos.LEFT, new_y=YPos.NEXT)
+        self.font(12, "B")
+        self.cell(70, 6, _day(c["report_day"]), align="R", new_x=XPos.LEFT, new_y=YPos.NEXT)
+        self.font(8, color=MUTED)
+        self.cell(70, 5, f"Generated {_dt(c['created_at'], True)}", align="R")
+        self._brand_bar(34, 1.1)
+
+        # station card
+        y = 40
+        self.set_fill_color(*PANEL)
+        self.rect(LEFT, y, WIDTH, 22, style="F", round_corners=True, corner_radius=2)
+        windows = [b["window"] for b in c["blocks"]          # monthly blocks (drought): own period
+                   if b.get("window") and not b.get("monthly")]
+        months = [_period(b) for b in c["blocks"] if b.get("window") and b.get("monthly")]
+        period = (f"{_dt(min(w['start'] for w in windows))} → {_dt(max(w['end'] for w in windows))}"
+                  if windows else (months[0] if months else "-"))
+        col = (WIDTH - 10) / 2
+        self._label(LEFT + 5, y + 4, col, "Station", f"{st['name']}  ·  ID {st['id']}")
+        self._label(LEFT + 5 + col, y + 4, col, "Period checked", period)
+        self.set_y(y + 26)
+
+        # headline
+        worst = c["overall"]["worst_severity"]
+        notices = c["overall"].get("notices", [])
+        suspected = c["overall"].get("suspected", [])
+        if c["overall"]["events"]:
+            color = SEVERITY_COLOR[worst]
+        elif notices or suspected:
+            color = ORANGE
+        else:
+            color = SEVERITY_COLOR["none"] if "No damaging" in c["overall"]["headline"] else NO_DATA_COLOR
+        y = self.get_y()
+        self.set_xy(LEFT + 6, y + 1)
+        self.font(7, "B", MUTED)
+        self.cell(100, 4, "RESULT", new_x=XPos.LEFT, new_y=YPos.NEXT)
+        self.font(12, "B", color)
+        self.multi_cell(WIDTH - 6, 6.5, c["overall"]["headline"], new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        lines = [f"{n['title']}: {n['text']}" for n in notices]    # a past event to claim (rain row 3)
+        lines += [s["text"] for s in suspected]                     # a photo asked for ("Snow likely: ...")
+        for line in lines:
+            self.set_x(LEFT + 6)
+            self.font(9, "B", ORANGE)
+            self.multi_cell(WIDTH - 6, 4.8, line, new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.set_fill_color(*color)
+        self.rect(LEFT, y, 2.2, max(self.get_y() - y, 12), style="F")
+        errors = c["overall"].get("errors", [])
+        if errors:
+            self.set_x(LEFT + 6)
+            self.font(8.5, "B", ERROR_COLOR)
+            self.multi_cell(WIDTH - 6, 4.6, f"{len(errors)} check(s) could not run: "
+                            + ", ".join(e["title"] for e in errors)
+                            + "  (see \"Checks that could not run\").",
+                            new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(4)
+
+    def overview(self):
+        self._heading("Overview")
+        self.set_draw_color(*LINE)
+        self.font(8)
+        head = FontFace(emphasis="BOLD", color=MUTED, fill_color=PANEL, size_pt=7)
+        several = len(self.c["fields"]) > 1          # one field per station: no field column
+        widths = (30, 28, 40, 26, 16, 12, 26) if several else (32, 44, 28, 16, 12, 46)
+        heads = ("PHENOMENON", "FIELD", "PERIOD CHECKED", "RESULT", "SEVERITY", "HOURS",
+                 "MISSING SENSORS *") if several else (
+                 "PHENOMENON", "PERIOD CHECKED", "RESULT", "SEVERITY", "HOURS", "MISSING SENSORS *")
+        align = ["LEFT"] * len(heads)
+        align[-2] = "RIGHT"
+        with self.table(width=WIDTH, col_widths=widths, line_height=4.8, text_align=align,
+                        headings_style=head, borders_layout="HORIZONTAL_LINES", padding=1.4,
+                        cell_fill_color=ZEBRA, cell_fill_mode="ROWS") as t:
+            row = t.row()
+            for h in heads:
+                row.cell(h)
+            for b in self.c["blocks"]:
+                period = _period(b)
+                if b.get("watch_window"):              # Excessive rain: also the last 4 days
+                    w = b["watch_window"]
+                    period += f"\n{w['label']}:\n{_dt(w['start'])} → {_dt(w['end'])}"
+                for f in b["fields"] or [None]:
+                    row = t.row()
+                    row.cell(b["title"], style=FontFace(emphasis="BOLD"))
+                    if several:
+                        row.cell(f["field_name"] if f else "-")
+                    row.cell(period)
+                    status = f["status"] if f else b["status"]
+                    color = _sev_color(f) if f else (
+                        ERROR_COLOR if b["status"] == "error" else NO_DATA_COLOR)
+                    result = RESULT_TEXT[status]
+                    if b.get("notice") and status == "no_event":     # past event found (rain row 3)
+                        result, color = "No new event\n" + b["notice"]["short"], ORANGE
+                    if _suspected(f):                                # snow: photo asked for
+                        result = "Suspected\nphoto requested"
+                    row.cell(result, style=FontFace(emphasis="BOLD", color=color))
+                    sev = (f["severity"].capitalize() if f and f["status"] == "event"
+                           and not _suspected(f) else "-")
+                    row.cell(sev, style=FontFace(emphasis="BOLD", color=color))
+                    row.cell((f.get("duration_text") or f"{f['duration_h']:g} h")
+                             if f and f["status"] == "event" else "-")
+                    row.cell(_missing(b), style=FontFace(color=ORANGE if b["missing_sensors"] else MUTED,
+                                                        size_pt=7.5))
+        self.font(7, "I", MUTED)
+        self.multi_cell(WIDTH, 3.8, "* Sensors the check uses that this station does not have. "
+                                    "\"Not available yet\": this check has not produced results yet. "
+                                    "\"Error\": the check could not run (see \"Checks that could not "
+                                    "run\").",
+                        new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(2)
+
+    # ── one section per block ──
+    def block(self, b):
+        color = BLUE
+        if any(f["status"] == "event" for f in b["fields"]):
+            color = _sev_color(max((f for f in b["fields"] if f["status"] == "event"),
+                                   key=lambda f: f["severity_score"]))
+        self._ensure(60)                         # keep the heading with its result and table
+        self._heading(b["title"], color)
+        self.font(8.5, color=MUTED)
+        when = _period(b)
+        stale = "  (latest run available)" if b.get("stale") else ""
+        self.multi_cell(WIDTH, 4.6, f"Period checked: {when}  ·  run of {_day(b['run_day'])}{stale}",
+                        new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        if b["missing_sensors"]:
+            self.set_text_color(*ORANGE)
+            self.multi_cell(WIDTH, 4.6, "Sensors missing at this station: " + _missing(b, ", "),
+                            new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(1)
+        for f in b["fields"]:
+            self._field_result(f, len(b["fields"]) > 1)
+        if b.get("notice"):
+            self.font(9.5, "B", ORANGE)
+            self.multi_cell(WIDTH, 5, b["notice"]["text"], new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+            self.ln(2)
+        if b["checks"]:
+            self._checks(b["checks"])
+        if b["data_notes"]:
+            self._notes(b["data_notes"])
+
+    def _field_result(self, f, show_name):
+        self._ensure(20)
+        x, y = LEFT, self.get_y()
+        suspected = _suspected(f)
+        w = self._chip(x, y, "SUSPECTED" if suspected else RESULT_TEXT[f["status"]].upper(), _sev_color(f))
+        if suspected:
+            w += 2 + self._chip(x + w + 2, y, "PHOTO REQUESTED", _sev_color(f))
+        elif f["status"] == "event":
+            w += 2 + self._chip(x + w + 2, y, f"SEVERITY {f['severity'].upper()}", _sev_color(f))
+        self.set_xy(x + w + 3, y)
+        self.font(9.5, "B")
+        self.cell(WIDTH - w - 3, 5.6, f["field_name"] if show_name else "",
+                  new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(1.5)
+        self.font(10)
+        self.multi_cell(WIDTH, 5.2, f["summary"], new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        lines = []
+        if suspected:
+            req = f["camera_request"]
+            lines.append(("Suspect hours", f"first {_dt(f['start'])}, last {_dt(f['end'])}  ·  "
+                                           f"{f['duration_h']:g} h with a sign"))
+            lines.append(("Photo", f"requested for {_dt(req['photo_at'])}  ·  {req['level']} "
+                                   f"(signs {', '.join(req['signals'])})"))
+            lines.append(("Severity", f["severity_basis"]))
+        elif f["status"] == "event" and f.get("period_text"):      # monthly block (drought)
+            lines.append(("Period", f["period_text"]))
+            lines.append(("Severity", f"{f['severity'].capitalize()} (score {f['severity_score']:.2f} "
+                                      f"of 1)  ·  {f['severity_basis']}"))
+            if f["severity_scale"]:
+                lines.append(("Scale", f["severity_scale"]))
+        elif f["status"] == "event":
+            lines.append(("Event hours", f"first {_dt(f['start'])}, last {_dt(f['end'])}  ·  "
+                                         f"{f['duration_h']:g} h with event conditions"))
+            lines.append(("Severity", f"{f['severity'].capitalize()} (score {f['severity_score']:.2f} "
+                                      f"of 1)  ·  {f['severity_basis']}"))
+            if f["severity_scale"]:
+                lines.append(("Scale", f["severity_scale"]))
+        if f["status"] == "event":
+            cover = {True: "inside the contract cover period", False: "outside the contract cover period",
+                     None: "unknown (crop stage not recorded for this field)"}[f["in_cover_window"]]
+            cover = f.get("cover_text") or cover
+            lines.append(("Cover", cover))
+        for k, v in lines:
+            self.font(8.5, "B", MUTED)
+            self.cell(22, 4.8, k)
+            self.font(8.5)
+            self.multi_cell(WIDTH - 22, 4.8, v, new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(2)
+
+    def _checks(self, checks):
+        self._ensure(12 + 7 * len(checks))
+        self.font(7.5, "B", MUTED)
+        self.cell(WIDTH, 5, "MEASURED VALUES AGAINST THRESHOLDS", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.set_draw_color(*LINE)
+        self.font(8.5)
+        head = FontFace(emphasis="BOLD", color=MUTED, fill_color=PANEL, size_pt=7.5)
+        with self.table(width=WIDTH, col_widths=(50, 44, 22, 24, 38), line_height=5,
+                        text_align=("LEFT", "LEFT", "RIGHT", "LEFT", "LEFT"),
+                        headings_style=head, borders_layout="HORIZONTAL_LINES", padding=1.4,
+                        cell_fill_color=ZEBRA, cell_fill_mode="ROWS") as t:
+            row = t.row()
+            for h in ("CHECK", "THRESHOLD", "MEASURED", "RESULT", "MARGIN"):
+                row.cell(h)
+            for ch in checks:
+                row = t.row()
+                name = ch["check"] + (f"\n{ch['note']}" if ch["note"] else "")
+                row.cell(name)
+                row.cell(ch["threshold_text"])
+                row.cell(_val(ch["measured"], ch["unit"]), style=FontFace(emphasis="BOLD"))
+                row.cell(STATUS_TEXT[ch["status"]],
+                         style=FontFace(emphasis="BOLD", color=STATUS_COLOR[ch["status"]]))
+                margin = "-"
+                if ch["margin"] is not None:
+                    margin = (f"past by {ch['margin']:g} {ch['unit']}" if ch["status"] == "crossed"
+                              else f"{ch['margin']:g} {ch['unit']} away")
+                row.cell(margin)
+        self.font(7, "I", MUTED)
+        self.multi_cell(WIDTH, 3.8, "Measured = the extreme of the period (lowest / highest). "
+                                    "\"Not recorded\": the value is kept only for event hours. "
+                                    "\"Not applied\": the station has no sensor or data for this check.",
+                        new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(2)
+
+    def _notes(self, notes):
+        self._ensure(10 + 5 * len(notes))
+        y = self.get_y()
+        self.set_x(LEFT + 4)
+        self.font(7.5, "B", MUTED)
+        self.cell(WIDTH - 4, 5, "DATA BASIS", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.font(8, color=MUTED)
+        for n in notes:
+            self.set_x(LEFT + 4)
+            self.multi_cell(WIDTH - 6, 4.3, f"•  {n}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.set_fill_color(*LINE)
+        self.rect(LEFT, y + 1, 0.8, self.get_y() - y - 1, style="F")
+        self.ln(3)
+
+    def errors(self):
+        """One table with every block that could not run and why."""
+        failed = [b for b in self.c["blocks"] if b["status"] == "error"]
+        if not failed:
+            return
+        self._ensure(30 + 9 * len(failed))
+        self._heading("Checks that could not run", ERROR_COLOR)
+        self.font(8.5, color=MUTED)
+        self.multi_cell(WIDTH, 4.6, "These checks gave no result for this report. The other "
+                                    "checks are not affected.", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.ln(1)
+        self.set_draw_color(*LINE)
+        self.font(8.5)
+        head = FontFace(emphasis="BOLD", color=MUTED, fill_color=PANEL, size_pt=7.5)
+        with self.table(width=WIDTH, col_widths=(40, 26, 112), line_height=4.8,
+                        text_align=("LEFT", "LEFT", "LEFT"), headings_style=head,
+                        borders_layout="HORIZONTAL_LINES", padding=1.4,
+                        cell_fill_color=ZEBRA, cell_fill_mode="ROWS") as t:
+            row = t.row()
+            for h in ("CHECK", "RUN OF", "PROBLEM"):
+                row.cell(h)
+            for b in failed:
+                row = t.row()
+                row.cell(b["title"], style=FontFace(emphasis="BOLD"))
+                row.cell(_day(b["run_day"]))
+                row.cell(b["error"], style=FontFace(color=ERROR_COLOR))
+        self.ln(2)
+
+    def closing(self):
+        self._ensure(30)
+        self.ln(4)
+        self.set_draw_color(*LINE)
+        self.line(LEFT, self.get_y(), LEFT + WIDTH, self.get_y())
+        self.ln(2)
+        self.font(7.5, "B", MUTED)
+        self.cell(WIDTH, 5, "ABOUT THIS REPORT", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+        self.font(7.5, color=MUTED)
+        self.multi_cell(WIDTH, 4, (
+            "This report is produced automatically from the hourly data of your 3A weather station. "
+            "Thresholds come from the crop insurance contract (UnipolSai Model 1506) and from "
+            "agronomic literature; where neither gives a number, a project default is used. "
+            "The severity estimates the likely damage from the weather alone: it is not a "
+            "field inspection or a loss assessment. All times are local station time."),
+            new_x=XPos.LMARGIN, new_y=YPos.NEXT)
+
+
+def render(content: dict, path: Path) -> Path:
+    """Write the PDF of one station report and return its path."""
+    pdf = _Doc(content)
+    pdf.add_page()
+    pdf.title_block()
+    pdf.overview()
+    for b in content["blocks"]:
+        if b["status"] not in ("not_run", "error"):    # no results: overview (+ errors) only
+            pdf.block(b)
+    pdf.errors()
+    pdf.closing()
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    pdf.output(str(path))
+    return path
diff --git a/src/damage_engine/report/station_report.py b/src/damage_engine/report/station_report.py
new file mode 100644
index 0000000..3d14dff
--- /dev/null
+++ b/src/damage_engine/report/station_report.py
@@ -0,0 +1,900 @@
+"""
+Station report: one report per station and day, for the client of that station
+(each station = one client; a client never sees another station's report).
+It lists EVERY event of Diagram.png (EVENTS, also blocks not written yet) in ONE
+report and, for each check, shows the measured value, the threshold, whether it
+was crossed and by how much. A block appears with its own section as soon as it
+stores a run (report.submit); a block without a check function here gets a
+generic table of its numeric details. For every event the report also says which
+of its sensors this station lacks (nothing about other stations).
+
+Two files per station and day (a new run replaces that day's report only; the last
+REPORT["history_runs"] = 10 days are kept):
+
+    outputs/station_reports/<station_id>/<day>.json   content (the same data as the PDF)
+    outputs/station_reports/<station_id>/<day>.pdf    the client's report (station_pdf.py)
+
+The content is built here from the stored block runs (report.py) and the
+thresholds (thresholds.py, read only). station_pdf.py only draws it, so the PDF
+engine can change without touching this file.
+
+Limits: the blocks store only extremes and totals, not hour-by-hour values, so a
+check shows the night/day extreme (e.g. lowest air temperature, wettest hour) and
+the event's first and last hour. Values a block does not store are "not recorded".
+
+    python -m damage_engine.report.station_report --station 566              # latest blocks
+    python -m damage_engine.report.station_report --station 566 --date 2026-10-20
+"""
+
+import argparse
+import json
+import operator
+from datetime import date, datetime, time, timedelta
+from pathlib import Path
+
+from damage_engine.report import report
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity, camera_request
+from damage_engine.sources.stations import all_stations, station_by_id
+from damage_engine.thresholds import (
+    BRINA,
+    BRINA_SCHEDULE,
+    DROUGHT,
+    DROUGHT_BANDS_PCT,
+    EXCESSIVE_RAIN,
+    EXCESSIVE_RAIN_BANDS_RATIO,
+    FLOOD_SCHEDULE,
+    FROST_FALLBACK_BANDS_C,
+    FROST_KILL_BANDS,
+    GELO,
+    HAIL,
+    HAIL_SCHEDULE,
+    HIGH_TEMPERATURE_SCHEDULE,
+    HOT_TEMPERATURE,
+    HOT_TEMPERATURE_FST_BANDS_C,
+    HOT_WIND,
+    HOT_WIND_BANDS_C,
+    HOT_WIND_SCHEDULE,
+    REPORT,
+    SNOW,
+    SNOW_SCHEDULE,
+    STRONG_WIND,
+    STRONG_WIND_BANDS_MS,
+    STRONG_WIND_SCHEDULE,
+)
+
+STATION_REPORTS_DIR = report.REPORTS_DIR.parent / "station_reports"
+
+# check status
+CROSSED = "crossed"            # the measured value reached the threshold
+NOT_CROSSED = "not_crossed"
+NOT_APPLIED = "not_applied"    # the station has no sensor / no data for this check
+NOT_RECORDED = "not_recorded"  # the block does not store this value (nights/days without event)
+INFO = "info"                  # value shown for context, no threshold
+
+ERROR = "error"                # block status: the block could not run (report.submit_error)
+
+OPS = {"<=": operator.le, "<": operator.lt, ">=": operator.ge, ">": operator.gt}
+SYMBOL = {"<=": "≤", "<": "<", ">=": "≥", ">": ">"}
+
+# Every event of Diagram.png, in diagram order: block name (as given to report.submit)
+# -> title and the 3A sensors it reads (Diagram.png inputs; Brina also uses solar
+# radiation, see its header). Wet-bulb is not a sensor here: it is calculated from
+# temperature and humidity. Hail also lists wind (its gust sign, see detectors/hail.py).
+# sensors=None would mean "All sensors" (the diagram's drought
+# input); Drought lists the sensors it reads: rain, and soil moisture for information
+# (its ET0, EvapoPot, is a daily value calculated by 3A at every station, not a sensor).
+_T, _H, _W, _S, _R = "TemperaturaAria", "UmiditaRelativa", "VelocitaVento2m", "RadiazioneSolare", "Pioggia"
+EVENTS = {
+    "brina":            {"title": "Brina (hoar frost)",  "sensors": (_T, _H, _W, _S)},
+    "strong_wind":      {"title": "Strong wind (storm)", "sensors": (_W,)},
+    "freezing":         {"title": "Freezing (gelo)",     "sensors": (_T, _S, _H, _W)},
+    "flood":            {"title": "Excessive rain",      "sensors": (_R,)},
+    "snow":             {"title": "Snow",                "sensors": (_R, _T, _S, _H)},
+    "hail":             {"title": "Hail",                "sensors": (_T, _R, _W)},
+    "hot_wind":         {"title": "Hot wind",            "sensors": (_W, _T, _H)},
+    "high_temperature": {"title": "High temperature",    "sensors": (_H, _S, _T)},
+    "drought":          {"title": "Drought",             "sensors": (_R, "Suolo1", "Suolo2")},
+}
+# Extra runs of a block stored as their own block, shown inside it in the client report: Hail's
+# daytime checks on hail-risk days (detectors/hail.py DAYTIME_BLOCK, slots "13h", "19h").
+DAYTIME_BLOCK_OF = {"hail": "hail_daytime"}
+CALCULATED_SENSORS = {"TBulb"}      # 3A's wet-bulb, calculated by 3A: never "missing"
+SENSOR_NAMES = {
+    "TemperaturaAria": "air temperature", "UmiditaRelativa": "humidity", "VelocitaVento2m": "wind",
+    "RadiazioneSolare": "solar radiation", "Pioggia": "rain gauge", "BagnaturaFogliare": "leaf wetness",
+    "Pressione": "air pressure", "Suolo1": "soil moisture 1", "Suolo2": "soil moisture 2",
+    "TemperaturaTerreno": "soil temperature", "TemperaturaTerreno2": "soil temperature 2",
+}
+HIDDEN_NOTES = ("5-year reference average",)    # data notes not shown to the client
+CRITERIA = {"downpour_1h": "rain in 1 hour", "intense_72h": "rain in 72 hours",
+            "prolonged_10d": "rain in 10 days"}
+
+
+def _num(x):
+    return f"{x:g}" if isinstance(x, (int, float)) else str(x)
+
+
+def _check(name, measured, unit, op=None, threshold=None, *, status=None,
+           threshold_text=None, note=""):
+    """One row: measured value against a threshold; status computed when not given."""
+    if status is None:
+        if op is None:
+            status = INFO
+        elif measured is None:
+            status = NOT_RECORDED
+        else:
+            status = CROSSED if OPS[op](measured, threshold) else NOT_CROSSED
+    margin = None
+    if status in (CROSSED, NOT_CROSSED) and measured is not None and threshold is not None:
+        margin = round(abs(measured - threshold), 2)
+    if threshold_text is None:
+        threshold_text = f"{SYMBOL[op]} {_num(threshold)} {unit}" if op else "-"
+    return {"check": name, "measured": measured, "unit": unit, "operator": op,
+            "threshold": threshold, "threshold_text": threshold_text,
+            "status": status, "margin": margin, "note": note}
+
+
+# ─────────────────────────── per-block checks ───────────────────────────
+
+def _brina_checks(a: Analysis, station) -> list[dict]:
+    d, event = a.details, a.status == EVENT
+    has_solar = station is not None and station.has("RadiazioneSolare")
+    wind_ok = bool(d.get("wind_checked"))
+    seen = CROSSED if event else NOT_RECORDED       # night held in every brina hour
+    max_wind = d.get("max_wind_ms")
+    offset = BRINA["radiative_offset_c"]
+    wet_note = (f"3A's own wet-bulb (from the hourly mean): {d['min_wet_bulb_3a_c']} °C"
+                if "min_wet_bulb_3a_c" in d else "")
+    return [
+        _check("Darkness (solar radiation)", None, "W/m²", "<=", BRINA["night_solar_max_wm2"],
+               status=seen if has_solar else NOT_APPLIED,
+               note="" if has_solar else "No pyranometer: every hour of the night counts as dark."),
+        _check("Highest wind in brina hours", max_wind, "m/s",
+               status=INFO if max_wind is not None else (NOT_RECORDED if wind_ok else NOT_APPLIED),
+               threshold_text=f"≤ {BRINA['calm_wind_max_ms']:g} m/s = calm",
+               note=(f"Calm hours: bud {offset:g} °C below the air and ice test. Windier hours: "
+                     f"bud = air, air below 0 °C instead of the ice test." if wind_ok
+                     else f"No wind sensor or data: calm assumed, bud {offset:g} °C below the air "
+                          f"(worst case).")),
+        _check("Lowest air temperature", d.get("night_min_temp_c"), "°C", "<=", BRINA["t_air_max_c"]),
+        _check("Lowest wet-bulb temperature", d.get("min_wet_bulb_c"), "°C", "<=",
+               BRINA["t_wet_max_c"], note=wet_note),
+        _check("Lowest bud temperature", d.get("min_bud_temp_c"), "°C", "<=", 0.0,
+               threshold_text="calm: ≤ 0 °C and ≤ frost point; windy: < 0 °C",
+               note=f"Bud = air − {offset:g} °C on calm hours, = air on windier hours."),
+        _check("Hours with brina conditions", a.duration_h, "h", ">=", BRINA["min_duration_h"]),
+    ]
+
+
+def _freezing_checks(a: Analysis, station) -> list[dict]:
+    d, event, g = a.details, a.status == EVENT, GELO
+    run_h, night_h = BRINA_SCHEDULE["run_hour"], BRINA_SCHEDULE["night_start_hour"]
+    fixed = f"{run_h + 1:02d}:00-{night_h - 1:02d}:00"
+    solar_ok = bool(d.get("solar_checked"))
+    trigger = f"{g['t_air_trigger_c']:g} °C"
+    wind_status = None if d.get("mean_wind_ms") is not None else (NOT_APPLIED if event else NOT_RECORDED)
+    return [
+        _check("Sunlit hours of the night (solar radiation)", d.get("sunlit_hours_in_night_window"),
+               "h", status=INFO if solar_ok else NOT_APPLIED,
+               threshold_text=f"> {BRINA['night_solar_max_wm2']:g} W/m²",
+               note=(f"Night hours with sun, checked here as daylight (day hours {fixed} always are)."
+                     if solar_ok else f"No pyranometer: daylight = the day hours {fixed} only.")),
+        _check("Lowest daylight air temperature", d.get("day_min_temp_c"), "°C",
+               g["t_air_trigger_op"], g["t_air_trigger_c"]),
+        _check(f"Daylight hours below {trigger}", a.duration_h, "h", ">=", g["min_duration_h"]),
+        _check(f"Mean wind while below {trigger}", d.get("mean_wind_ms"), "m/s", ">=",
+               g["advective_wind_min_ms"], status=wind_status,
+               note=("For information: wind points to a cold air mass; not needed in daylight."
+                     if wind_status is None or not event else "No wind sensor or data.")),
+        _check(f"Lowest wet-bulb while below {trigger}", d.get("min_wet_bulb_c"), "°C",
+               status=INFO if d.get("min_wet_bulb_c") is not None else NOT_RECORDED,
+               note="For information: how cold wet buds (rain, dew) could get."),
+    ]
+
+
+def _flood_checks(a: Analysis, station) -> list[dict]:
+    d, met = a.details, set(a.details.get("criteria_met", []))
+    r = EXCESSIVE_RAIN
+    rows = [_check("Rain in 1 hour (wettest hour)", d.get("max_1h_mm"), "mm",
+                   r["downpour_1h_op"], r["downpour_1h_mm"])]
+    rows.append(_last_72h_check(d, met))                 # row 2: the 72 h ending at the run hour
+    rows.append(_last_heavy_72h_check(d, a.run_day))     # row 3: for claims
+    text_10d =(f"{SYMBOL[r['prolonged_10d_ratio_op']]} {r['prolonged_10d_ratio']:g} × 5-year "
+                f"average and {SYMBOL[r['prolonged_10d_min_op']]} {r['prolonged_10d_min_mm']:g} mm")
+    ref = d.get("reference_10d_mm")
+    if ref is None:                            # criterion not applied: shown as a plain total
+        rows.append(_check("Rain in the last 10 days", d.get("rain_10d_mm"), "mm"))
+    else:
+        trigger = max(r["prolonged_10d_ratio"] * ref, r["prolonged_10d_min_mm"])
+        rows.append(_check("Rain in 10 days", d.get("rain_10d_mm"), "mm", ">", trigger,
+                           status=CROSSED if "prolonged_10d" in met else NOT_CROSSED,
+                           threshold_text=f"{text_10d} (= {trigger:.1f} mm)"))
+    rows.append(_check("Rain in the event day (24 h)", d.get("rain_day_mm"), "mm"))
+    return rows
+
+
+WIND_VALUE = {"hourly_max": "highest wind of the hour", "hourly_mean": "mean wind of the hour"}
+
+
+def _strong_wind_checks(a: Analysis, station) -> list[dict]:
+    d, w = a.details, STRONG_WIND
+    op, ms = w["wind_trigger_op"], w["wind_trigger_ms"]
+    kmh = f"{ms * 3.6:.0f} km/h"
+    value = WIND_VALUE.get(d.get("trigger"), "wind")
+    top = d.get("max_wind_ms")
+    note = f"{SYMBOL[op]} {kmh}, Beaufort 7 (contract). Value used: {value}."
+    if d.get("max_wind_at") and top is not None:
+        note = f"At {_hour(d['max_wind_at'])}, {d.get('max_wind_kmh'):g} km/h. " + note
+    if d.get("fallback_hours"):
+        note += f" Hourly mean used for {d['fallback_hours']} hour(s) without a maximum."
+    minutes = a.duration_h * 60
+    held = CROSSED if minutes >= w["min_duration_min"] else NOT_CROSSED
+    mean = d.get("max_mean_wind_ms")
+    return [
+        _check("Highest wind in the 24 h", top, "m/s", op, ms, note=note),
+        _check(f"Time with wind {SYMBOL[op]} {ms:g} m/s", a.duration_h, "h", status=held,
+               threshold_text=f"≥ {w['min_duration_min']:g} min",
+               note="One hourly record counts as 60 min."),
+        _check("Highest hourly mean wind", mean, "m/s", status=INFO if mean is not None else NOT_RECORDED,
+               note="For information: the mean of the hour, not used to detect."
+               if d.get("trigger") != "hourly_mean" else "The value used to detect."),
+    ]
+
+
+def _hot_wind_checks(a: Analysis, station) -> list[dict]:
+    d, w = a.details, HOT_WIND
+    op, deg = w["t_max_trigger_op"], w["t_max_trigger_c"]
+    wind_min = w["wind_min_ms"]
+    note = f"{SYMBOL[op]} {deg:g} °C at 2 m (contract). Value used: highest temperature of the hour."
+    if d.get("max_temp_at") and d.get("max_temp_c") is not None:
+        note = f"At {_hour(d['max_temp_at'])}. " + note
+    if d.get("fallback_hours"):
+        note += f" Hourly mean used for {d['fallback_hours']} hour(s) without a maximum."
+    hours = d.get("hot_wind_hours", 0)
+    hours_note = f"Moving air = mean wind ≥ {wind_min:g} m/s (project value; the contract gives no speed)."
+    if d.get("still_hot_hours"):
+        hours_note += f" {d['still_hot_hours']} hour(s) at ≥ {deg:g} °C had still air."
+    if d.get("wind_assumed_hours"):
+        hours_note += f" Air taken as moving in {d['wind_assumed_hours']} hour(s) without wind data."
+    wind = d.get("wind_at_max_temp_ms")
+    if wind is None:
+        wind_row = _check("Wind in the hottest hour", None, "m/s", ">=", wind_min, status=NOT_APPLIED,
+                          note="No wind data: air taken as moving (worst case).")
+    else:
+        wind_row = _check("Wind in the hottest hour", wind, "m/s", ">=", wind_min,
+                          note="Mean wind of the hour (project value; the contract gives no speed).")
+    rh = d.get("humidity_at_max_temp_pct")
+    lo, hi = w["dir_from_min_deg"], w["dir_from_max_deg"]
+    return [
+        _check("Highest air temperature in the 24 h", d.get("max_temp_c"), "°C", op, deg, note=note),
+        _check(f"Hours at {SYMBOL[op]} {deg:g} °C with moving air", hours, "h",
+               status=CROSSED if a.status == EVENT else NOT_CROSSED, threshold_text="≥ 1 h",
+               note=hours_note),
+        wind_row,
+        _check("Wind from south-east to south-west", None, "", status=NOT_APPLIED,
+               threshold_text=f"{lo:g}°–{hi:g}°",
+               note="No 3A station measures wind direction: not checked."),
+        _check("Humidity in the hottest hour", rh, "%", status=INFO if rh is not None else NOT_RECORDED,
+               note="For information: the lowest humidity of the hour; dry air speeds up withering."),
+    ]
+
+
+def _high_temperature_checks(a: Analysis, station) -> list[dict]:
+    d, p = a.details, HOT_TEMPERATURE
+    op, deg = p["contract_t_max_op"], p["contract_t_max_c"]
+    note = f"{SYMBOL[op]} {deg:g} °C at 2 m (contract). Value used: highest temperature of the hour."
+    if d.get("max_temp_at") and d.get("max_temp_c") is not None:
+        note = f"At {_hour(d['max_temp_at'])}. " + note
+    if d.get("fallback_hours"):
+        note += f" Hourly mean used for {d['fallback_hours']} hour(s) without a maximum."
+    sunburn = HOT_TEMPERATURE_FST_BANDS_C["edges"][0]
+    formula = (f"Estimated: air + {p['fst_gain_max_c']:g} °C × sun / 1000 W/m² ÷ "
+               f"(1 + {p['fst_wind_coeff']:g} × wind); sun counted from "
+               f"{p['daytime_solar_min_wm2']:g} W/m².")
+    risk_name = f"Sunburn risk hours (air ≥ {p['risk_t_air_c']:g} °C, fruit ≥ {sunburn:g} °C)"
+    if not d.get("solar_checked"):
+        no_sun = "No solar radiation data at this station: not estimated."
+        fst_row = _check("Highest fruit surface temperature", None, "°C", ">=", sunburn,
+                         status=NOT_APPLIED, note=no_sun)
+        risk_row = _check(risk_name, None, "h", status=NOT_APPLIED, threshold_text="≥ 1 h", note=no_sun)
+    else:
+        fst_note = f"Sunburn (browning) starts at {sunburn:g} °C. {formula}"
+        if d.get("max_fst_at"):
+            wind = d.get("wind_at_max_fst_ms")
+            wind_text = f"wind {wind:g} m/s" if wind is not None else "no wind data, calm air taken"
+            fst_note = (f"At {_hour(d['max_fst_at'])}: sun {d.get('solar_at_max_fst_wm2'):g} W/m², "
+                        f"{wind_text}. " + fst_note)
+        fst_row = _check("Highest fruit surface temperature", d.get("max_fst_c"), "°C", ">=", sunburn,
+                         note=fst_note)
+        hours = d.get("sunburn_risk_hours")
+        risk_row = _check(risk_name, hours, "h", status=CROSSED if hours else NOT_CROSSED,
+                          threshold_text="≥ 1 h",
+                          note=f"For information: below {deg:g} °C air the contract does not cover "
+                               f"sunburn.")
+    rh, wet = d.get("humidity_at_max_temp_pct"), d.get("wet_bulb_at_max_temp_c")
+    return [
+        _check("Highest air temperature in the 24 h", d.get("max_temp_c"), "°C", op, deg, note=note),
+        _check(f"Hours at {SYMBOL[op]} {deg:g} °C", d.get("hot_hours", 0), "h",
+               status=CROSSED if a.status == EVENT else NOT_CROSSED, threshold_text="≥ 1 h"),
+        fst_row,
+        risk_row,
+        _check("Humidity in the hottest hour", rh, "%", status=INFO if rh is not None else NOT_RECORDED,
+               note="For information: the lowest humidity of the hour."),
+        _check("Wet-bulb in the hottest hour", wet, "°C", status=INFO if wet is not None else NOT_RECORDED,
+               note="For information: calculated from temperature and humidity (3A formula)."),
+    ]
+
+
+def _snow_checks(a: Analysis, station) -> list[dict]:
+    """Snow: the signs that decide whether a photo is asked for (no station measures snow)."""
+    d, p = a.details, SNOW
+    t, hours = p["t_wet_snow_max_c"], p["melt_lookahead_h"]
+    what = "air temperature" if d.get("cold_basis") == "air" else "wet-bulb"
+    note = f"Snow is possible at ≤ {t:g} °C (coldest moment of the hour)."
+    if d.get("min_cold_at"):
+        note = f"At {_hour(d['min_cold_at'])}. " + note
+    if d.get("air_fallback_hours"):
+        note += f" Air temperature used for {d['air_fallback_hours']} hour(s) without humidity."
+    cold = d.get("cold_hours", 0)
+    rain_ok, rh_ok, solar_ok = d.get("rain_checked"), d.get("humidity_checked"), d.get("solar_checked")
+    no_rain = "No rain data at this station: not checked."
+    if not rain_ok:
+        a_row = _check("A · Precipitation in cold hours", None, "mm", status=NOT_APPLIED,
+                       threshold_text="> 0 mm", note=no_rain)
+        b_row = _check(f"B · Rain within {hours:g} h after a cold hour", None, "mm",
+                       status=NOT_APPLIED, threshold_text="> 0 mm", note=no_rain)
+    else:
+        a_row = _check("A · Precipitation in cold hours", d.get("snow_water_mm"), "mm", ">", 0.0,
+                       note=f"{d.get('snow_hours', 0)} hour(s). Strong sign: snow probably fell.")
+        b_row = _check(f"B · Rain within {hours:g} h after a cold hour", d.get("melt_water_mm"), "mm",
+                       ">", 0.0, note=f"{d.get('melt_hours', 0)} hour(s). Medium sign: an unheated "
+                                      f"rain gauge records snow only when it melts.")
+    rh = p["saturated_rh_min_pct"]
+    c_name = f"C · Cold hours with humidity ≥ {rh:g} % and no rain"
+    if rh_ok:
+        c_row = _check(c_name, d.get("saturated_hours"), "h", ">=", 1, threshold_text="≥ 1 h",
+                       note="Weak sign: snow the rain gauge may have missed (or fog, frost).")
+    else:
+        c_row = _check(c_name, None, "h", status=NOT_APPLIED, threshold_text="≥ 1 h",
+                       note="No humidity data at this station: not checked.")
+    d_name = "D · Highest midday sun after the first suspect hour"
+    lo, hi = p["midday_hours"]
+    covered = p["covered_solar_max_wm2"]
+    if not solar_ok:
+        d_row = _check(d_name, None, "W/m²", status=NOT_APPLIED, threshold_text=f"≤ {covered:g} W/m²",
+                       note="No solar radiation data at this station: not checked.")
+    else:
+        val = d.get("midday_solar_max_wm2")
+        d_row = _check(d_name, val, "W/m²", "<=", covered,
+                       note=(f"Supporting sign only (records {lo:02d}:00-{hi:02d}:00): the solar "
+                             f"sensor may be under snow; an overcast midday is about 50-200 W/m²."
+                             if val is not None else
+                             "No midday hour after a suspect hour in the 24 h (or no suspect hour)."))
+    est, est_max = d.get("est_new_snow_cm"), d.get("est_new_snow_max_cm")
+    more = f"; up to {est_max:g} cm counting the rain of sign B" if est_max and est_max != est else ""
+    est_note = (f"Water of the cold hours × {p['snow_liquid_ratio']:g}{more}. "
+                f"Information only: the photo decides.")
+    return [
+        _check(f"Lowest {what} in the 24 h", d.get("min_cold_c"), "°C", "<=", t, note=note),
+        _check(f"Hours at ≤ {t:g} °C (cold enough for snow)", cold, "h",
+               status=CROSSED if cold else NOT_CROSSED, threshold_text="≥ 1 h"),
+        a_row, b_row, c_row, d_row,
+        _check("Estimated new snow", est, "cm", status=INFO if rain_ok else NOT_APPLIED,
+               note=est_note if rain_ok else no_rain),
+    ]
+
+
+def _hail_checks(a: Analysis, station) -> list[dict]:
+    """Hail: the signs of a hailstorm that decide whether a photo is asked for (no station
+    measures hail). B and C count only within storm_window_h of rain."""
+    d, p = a.details, HAIL
+    near = f"Counts only within {p['storm_window_h']:g} h of rain (hail melts in the rain gauge)."
+
+    def at(key):
+        return f"At {_hour(d[key])}. " if d.get(key) else ""
+
+    def dry(key):
+        n = d.get(key, 0)
+        return f" {n} hour(s) without rain nearby: not counted." if n else ""
+
+    rain_ok = d.get("rain_checked")
+    if rain_ok:
+        burst = _check("A · Rain in the wettest hour", d.get("max_rain_1h_mm"), "mm", ">=",
+                       p["rain_1h_mm"], note=at("max_rain_1h_at") + "Burst rain of a storm.")
+    else:
+        burst = _check("A · Rain in the wettest hour", None, "mm", status=NOT_APPLIED,
+                       threshold_text=f"≥ {p['rain_1h_mm']:g} mm",
+                       note="No rain data at this station: not checked.")
+    rate_mmh = p["rain_10min_mm"] * 6
+    if d.get("rate_checked"):
+        rate = _check("A · Highest rain rate", d.get("max_rain_rate_mmh"), "mm/h", ">=", rate_mmh,
+                      note=at("max_rain_rate_at") + f"The rate of {p['rain_10min_mm']:g} mm in "
+                                                    f"10 minutes.")
+    else:
+        rate = _check("A · Highest rain rate", None, "mm/h", status=NOT_APPLIED,
+                      threshold_text=f"≥ {rate_mmh:g} mm/h",
+                      note="This station's rain gauge sends no rain rate: not checked.")
+    if d.get("temp_checked"):
+        drop_val = d.get("max_t_drop_c")
+        drop_val = None if drop_val is None else max(drop_val, 0.0)
+        note = at("max_t_drop_at") + ("Mean of the hour before minus the lowest of the hour (cold "
+                                      "outflow of a storm). " + near + dry("drop_dry_hours"))
+        if d.get("temp_fallback_hours"):
+            note += f" Hourly mean used for {d['temp_fallback_hours']} hour(s) without a minimum."
+        drop = _check("B · Largest temperature drop", drop_val, "°C", ">=", p["t_drop_30min_c"],
+                      note=note)
+    else:
+        drop = _check("B · Largest temperature drop", None, "°C", status=NOT_APPLIED,
+                      threshold_text=f"≥ {p['t_drop_30min_c']:g} °C",
+                      note="No air temperature data at this station: not checked.")
+    if d.get("wind_checked"):
+        note = at("max_gust_at") + "Highest wind of the hour. " + near + dry("gust_dry_hours")
+        if d.get("wind_fallback_hours"):
+            note += f" Hourly mean used for {d['wind_fallback_hours']} hour(s) without a maximum."
+        gust = _check("C · Highest wind (gust)", d.get("max_gust_ms"), "m/s", ">=", p["gust_ms"],
+                      note=note)
+    else:
+        gust = _check("C · Highest wind (gust)", None, "m/s", status=NOT_APPLIED,
+                      threshold_text=f"≥ {p['gust_ms']:g} m/s",
+                      note="No wind data at this station: not checked.")
+    k = p["min_indicators"]
+    storm = _check("Signs in one storm", d.get("storm_signs", 0), "", status=INFO,
+                   threshold_text=f"1 = possible, ≥ {k} = likely",
+                   note=f"Signs within {p['storm_window_h']:g} h of each other. Any sign asks for a "
+                        f"photo; the photo decides.")
+    rain = d.get("rain_day_mm")
+    total = _check("Rain in the 24 h", rain, "mm", status=INFO if rain_ok else NOT_APPLIED,
+                   note=(f"{d.get('rain_hours', 0)} hour(s) with rain. For information."
+                         if rain_ok else "No rain data at this station."))
+    return [burst, rate, drop, gust, storm, total]
+
+
+def _drought_checks(a: Analysis, station) -> list[dict]:
+    """Drought: rain of the last complete months against the climate average (thresholds.py
+    RAIN_NORMALS_MM), month by month, plus the water balance and soil moisture (information)."""
+    d, p = a.details, DROUGHT
+    op, trigger = p["rain_pct_of_normal_op"], p["rain_pct_of_normal_max"]
+    label, normal, rain = d.get("window_label", ""), d.get("normal_mm"), d.get("rain_mm")
+    gap = None if rain is None or normal is None else round(normal - rain, 1)
+    gap_text = "" if gap is None else (f"{gap:g} mm below the average." if gap > 0
+                                       else f"{-gap:g} mm above the average.")
+    missing = f" {d['missing_days']} day(s) without data counted as 0 mm." if d.get("missing_days") else ""
+    rows = [
+        _check(f"Rain in the last {d.get('window_months', len(d.get('months', [])))} months ({label})",
+               rain, "mm", status=INFO, threshold_text=f"average {_num(normal)} mm",
+               note=f"Average 1991-2020 of these months. {gap_text}{missing}".strip()),
+        _check("Rain as % of the average", d.get("rain_pct_of_normal"), "%", op, trigger,
+               threshold_text=(f"{SYMBOL[op]} {trigger:g} % of the average"
+                               + (f" (= {normal * trigger / 100:.1f} mm)" if normal else "")),
+               note="Drought = much less rain than usual for these months (project value)."),
+    ]
+    for m in d.get("months", []):
+        note = f"{_num(m['rain_pct_of_normal'])} % of the average."
+        if m.get("missing_days"):
+            note += f" {m['missing_days']} day(s) without data."
+        rows.append(_check(f"Rain in {m['label']}", m["rain_mm"], "mm", status=INFO,
+                           threshold_text=f"average {_num(m['normal_mm'])} mm", note=note))
+    balance = d.get("water_balance_mm")
+    if balance is None:
+        rows.append(_check(f"Water balance: rain − ET0 ({label})", None, "mm", status=NOT_APPLIED,
+                           note="No evapotranspiration data (3A EvapoPot) for these months."))
+    else:
+        some = (f" Only {d['water_balance_months']} month(s) with data."
+                if d.get("water_balance_months", 0) < len(d.get("months", [])) else "")
+        rows.append(_check(f"Water balance: rain − ET0 ({label})", balance, "mm", status=INFO,
+                           note=f"For information. ET0 (3A EvapoPot) {_num(d.get('et0_mm'))} mm. Below "
+                                f"0: the crop needed more water than the rain gave (without "
+                                f"irrigation).{some}"))
+    soil = d.get("soil_moisture") or {}
+    last = d.get("last_month_label", "the last month")
+    if soil:
+        for sensor, v in soil.items():
+            rows.append(_check(f"{SENSOR_NAMES.get(sensor, sensor).capitalize()}, mean of {last}",
+                               v["mean_pct"], "%", status=INFO,
+                               note=f"For information. First 7 days {_num(v['first_week_pct'])} %, "
+                                    f"last 7 days {_num(v['last_week_pct'])} %."))
+    else:
+        has = station is not None and any(station.has(s) for s in ("Suolo1", "Suolo2"))
+        rows.append(_check(f"Soil moisture, mean of {last}", None, "%", status=NOT_APPLIED,
+                           note=("No soil moisture data for this month." if has
+                                 else "No soil moisture sensor at this station.")))
+    return rows
+
+
+def _hour(iso: str) -> str:
+    return datetime.fromisoformat(iso).strftime("%d %b %H:%M")
+
+
+def _last_72h_check(d: dict, met: set) -> dict:
+    """Rain of the last 72 h (ending at the run hour) against the contract's 72 h threshold.
+
+    Flood's event still uses the contract rule (any 72 h window ending on rain of the event
+    day), so a note explains the two cases where this row and the event result differ.
+    """
+    op, mm = EXCESSIVE_RAIN["intense_72h_op"], EXCESSIVE_RAIN["intense_72h_mm"]
+    rain = d.get("rain_72h_to_run_mm")
+    crossed = rain is not None and OPS[op](rain, mm)
+    note = ""
+    if crossed and "intense_72h" not in met:             # the last 24 h were dry
+        note = "No rain in the last 24 h: not a new event (see the Heavy Rain Watch)."
+    elif not crossed and "intense_72h" in met:           # an earlier 72 h window crossed it
+        note = "The event comes from an earlier 72 h window (see the Heavy Rain Watch)."
+    return _check("Rain in the last 72 hours", rain, "mm", op, mm, note=note)
+
+
+def _last_heavy_72h_check(d: dict, run_day: date) -> dict:
+    """72-Hour Heavy Rain Watch (flood details), for claims: the latest 72 h window of the last
+    claim_search_days that reaches the 72 h threshold, else the wettest one; only the rain of
+    those days counts. The name carries those days (they follow the run day); the note gives
+    the first → last rainy hour of the window."""
+    r, days = EXCESSIVE_RAIN, FLOOD_SCHEDULE["claim_search_days"]
+    op, mm = r["intense_72h_op"], r["intense_72h_mm"]
+    watch = _watch_window("flood", run_day)
+    name = f"72-Hour Heavy Rain Watch ({_hour(watch['start'])} → {_hour(watch['end'])})"
+    if "last_heavy_72h" not in d:                        # run stored before this check existed
+        return _check(name, None, "mm", op, mm)
+    heavy, wettest = d["last_heavy_72h"], d.get("wettest_72h")
+    window = heavy or wettest
+    if not window:
+        return _check(name, 0.0, "mm", op, mm, note=f"No rain in the last {days} days.")
+    first, last = _hour(window["first_rain"]), _hour(window["last_rain"])
+    rain = f"Rain {first} → {last}" if first != last else f"Rain at {first}"
+    earlier = " Rain also fell just before these days (not counted)." if window.get("partial") else ""
+    if heavy:
+        return _check(name, heavy["mm"], "mm", op, mm,
+                      note=f"{rain}; {mm:g} mm reached on {_hour(heavy['reached_at'])}.{earlier}")
+    return _check(name, wettest["mm"], "mm", op, mm, note=f"{rain}.{earlier}")
+
+
+def _generic_checks(a: Analysis, station) -> list[dict]:
+    """Blocks without a check function yet: their numeric details, without thresholds."""
+    return [_check(k.replace("_", " ").capitalize(), v, "") for k, v in a.details.items()
+            if isinstance(v, (int, float)) and not isinstance(v, bool)]
+
+
+CHECKS = {"brina": _brina_checks, "flood": _flood_checks, "freezing": _freezing_checks,
+          "strong_wind": _strong_wind_checks, "hot_wind": _hot_wind_checks,
+          "high_temperature": _high_temperature_checks, "snow": _snow_checks,
+          "hail": _hail_checks, "drought": _drought_checks}
+# Blocks that check whole months, not a day: their period is the label stored by the block
+# ("Jul–Sep 2026"), it is left out of the top card's "Period checked", and an event shows its
+# period and days instead of hours.
+MONTHLY_BLOCKS = {"drought"}
+
+
+def missing_sensors(block: str, station) -> list[dict]:
+    """Sensors the event reads that this station lacks (only sensors that exist in the 3A
+    network). The report of a station gives no information about other stations, so no
+    counts of who has them."""
+    known = {sensor for st in all_stations() for sensor in st.sensors}
+    wanted = EVENTS[block]["sensors"] if block in EVENTS else ()
+    if wanted is None:                                   # "All sensors"
+        wanted = sorted((x for x in known if x not in CALCULATED_SENSORS),
+                        key=lambda x: SENSOR_NAMES.get(x, x))
+    mine = set(station.sensors) if station else set()
+    return [{"sensor": x, "name": SENSOR_NAMES.get(x, x)}
+            for x in wanted if x not in mine and x in known]
+
+
+def _window(block: str, run_day: date):
+    """(start, end) of the period a block looked at."""
+    if block == "brina":
+        return (datetime.combine(run_day - timedelta(days=1), time(BRINA_SCHEDULE["night_start_hour"])),
+                datetime.combine(run_day, time(BRINA_SCHEDULE["run_hour"])))
+    if block == "flood":
+        end = datetime.combine(run_day, time(FLOOD_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "freezing":                    # the 24 h shared with Brina, ending where its night ends
+        end = datetime.combine(run_day, time(BRINA_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "strong_wind":
+        end = datetime.combine(run_day, time(STRONG_WIND_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "hot_wind":
+        end = datetime.combine(run_day, time(HOT_WIND_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "high_temperature":
+        end = datetime.combine(run_day, time(HIGH_TEMPERATURE_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "snow":
+        end = datetime.combine(run_day, time(SNOW_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    if block == "hail":
+        end = datetime.combine(run_day, time(HAIL_SCHEDULE["run_hour"]))
+        return end - timedelta(days=1), end
+    return None
+
+
+def _watch_window(block: str, run_day: date) -> dict | None:
+    """Extra period shown in the overview and in rain row 3: Flood's Heavy Rain Watch counts
+    the rain of the last claim_search_days, not only the event day."""
+    if block != "flood":
+        return None
+    days = FLOOD_SCHEDULE["claim_search_days"]
+    end = datetime.combine(run_day, time(FLOOD_SCHEDULE["run_hour"]))
+    return {"label": f"Last {days} days", "start": _iso(end - timedelta(days=days)), "end": _iso(end)}
+
+
+# ─────────────────────────── severity text ───────────────────────────
+
+def _scale(bands: dict, fmt) -> str:
+    higher, strict = bands["higher_is_worse"], bands.get("strict", False)
+    op = (">" if strict else "≥") if higher else ("<" if strict else "≤")
+    names = [s.value for s in Severity][1:]
+    return ", ".join(f"{n} {op} {fmt(e)}" for n, e in zip(names, bands["edges"]))
+
+
+def _severity_basis(a: Analysis) -> tuple[str, str]:
+    """(how the severity was found, the severity scale) for one event."""
+    d = a.details
+    if a.block in ("brina", "freezing"):       # same frost tables; Freezing: bud = air in daylight
+        what = "lowest bud temperature" if a.block == "brina" else "lowest daylight air temperature"
+        method = d.get("severity_method")
+        if method == "kill_fraction":
+            return (f"Stage {str(d.get('stage')).replace('_', ' ')} (BBCH {d.get('bbch')}): "
+                    f"about {d['kill_fraction']:.0%} "
+                    f"of buds killed at the {what}.",
+                    "Buds killed: " + _scale(FROST_KILL_BANDS, lambda e: f"{e * 100:g} %"))
+        if method == "dormant":
+            return f"Buds dormant (BBCH {d.get('bbch')}): no damage expected.", ""
+        scale = "Bud temperature: " if a.block == "brina" else "Air temperature: "
+        return (f"Crop stage not set: graded on the {what}.",
+                scale + _scale(FROST_FALLBACK_BANDS_C, lambda e: f"{e:g} °C"))
+    if a.block == "flood":
+        worst = d.get("worst_criterion")
+        return (f"Rain = {d.get('rain_trigger_ratio', 0):.2f} × its threshold for "
+                f"{CRITERIA.get(worst, worst)}, the check crossed the most.",
+                "Rain / threshold: " + _scale(EXCESSIVE_RAIN_BANDS_RATIO, lambda e: f"{e:g}×"))
+    if a.block == "strong_wind":                # same for every crop stage
+        return ("Graded on the highest wind of the strong-wind hours (Beaufort scale), "
+                "the same for every crop stage.",
+                "Wind: " + _scale(STRONG_WIND_BANDS_MS, lambda e: f"{e:g} m/s")
+                + " (Beaufort 7, 8, 9, 10)")
+    if a.block == "hot_wind":                   # same for every crop stage
+        return ("Graded on the highest air temperature of the hot-wind hours, "
+                "the same for every crop stage.",
+                "Air temperature: " + _scale(HOT_WIND_BANDS_C, lambda e: f"{e:g} °C"))
+    if a.block == "high_temperature":           # same for every crop stage
+        if d.get("severity_method") == "fst":
+            return (f"Graded on the highest estimated fruit surface temperature "
+                    f"({d.get('severity_value_c'):g} °C), the same for every crop stage.",
+                    "Fruit surface: " + _scale(HOT_TEMPERATURE_FST_BANDS_C, lambda e: f"{e:g} °C"))
+        return ("No solar radiation data: graded on the highest air temperature, "
+                "the same for every crop stage.",
+                "Air temperature: " + _scale(HOT_WIND_BANDS_C, lambda e: f"{e:g} °C"))
+    if a.block == "drought":                    # same for every crop stage
+        return (f"Graded on the rain of {d.get('window_label')} as % of its 1991-2020 average "
+                f"({d.get('rain_pct_of_normal'):g} %), the same for every crop stage.",
+                "Rain / average: " + _scale(DROUGHT_BANDS_PCT, lambda e: f"{e:g} %"))
+    if camera_request(d):                       # snow, hail: suspected, the photo grades it
+        return ("Not graded: no sensor measures it; the photo (CV model) will confirm and grade it.",
+                "")
+    return "", ""
+
+
+# ─────────────────────────── content ───────────────────────────
+
+def _iso(t):
+    return t.isoformat(timespec="minutes") if t else None
+
+
+def _block_entry(block: str, run: dict | None, station_id: int, station, day: date) -> dict:
+    title = EVENTS[block]["title"] if block in EVENTS else block.replace("_", " ").capitalize()
+    entry = {"block": block, "title": title, "missing_sensors": missing_sensors(block, station)}
+    empty = {"fields": [], "checks": [], "data_notes": [], "details": {}}
+    if run and run.get("error"):                  # the block could not run (no code, crash, API)
+        window = _window(block, run["run_day"])
+        return {**entry, **empty, "status": ERROR, "run_day": run["run_day"].isoformat(),
+                "stale": run["run_day"] != day, "error": run["error"][:400],
+                "window": {"start": _iso(window[0]), "end": _iso(window[1])} if window else None,
+                "watch_window": _watch_window(block, run["run_day"])}
+    mine = [a for a in (run or {}).get("analyses", []) if a.station_id == station_id]
+    if not mine:
+        return {**entry, "status": "not_run", "run_day": None, "fields": [], "checks": [],
+                "data_notes": [], "details": {}}
+    run_day = run["run_day"]
+    window = _window(block, run_day)
+    ref = next((a for a in mine if a.status != NO_DATA), mine[0])     # station-level values
+    label = None
+    if block in MONTHLY_BLOCKS and ref.details.get("window_start"):   # the months the block checked
+        window = (datetime.fromisoformat(ref.details["window_start"]),
+                  datetime.fromisoformat(ref.details["window_end"]))
+        label = ref.details.get("window_label")
+    checks = CHECKS.get(block, _generic_checks)(ref, station) if ref.status != NO_DATA else []
+    notes = [n for n in dict.fromkeys(n for a in mine for n in a.data_notes)
+             if not any(h in n for h in HIDDEN_NOTES)]
+    fields = [_field_entry(block, a) for a in mine]
+    worst = max(mine, key=lambda a: a.severity.level)
+    status = worst.status if worst.status == EVENT else ref.status
+    return {**entry, "status": status,
+            "run_day": run_day.isoformat(), "stale": run_day != day,
+            "window": ({"start": _iso(window[0]), "end": _iso(window[1]),
+                        **({"label": label} if label else {})} if window else None),
+            "monthly": block in MONTHLY_BLOCKS,
+            "watch_window": _watch_window(block, run_day),
+            "checks": checks, "fields": fields, "data_notes": notes, "details": ref.details,
+            "notice": _past_event_notice(block, status, ref.details)}
+
+
+def _field_entry(block: str, a: Analysis) -> dict:
+    """One field's result in a block entry."""
+    basis, scale = _severity_basis(a) if a.status == EVENT else ("", "")
+    return {"field_id": a.field_id, "field_name": a.field_name, "status": a.status,
+            "severity": a.severity.value, "severity_score": a.severity_score,
+            "duration_h": a.duration_h, "start": _iso(a.start), "end": _iso(a.end),
+            "summary": a.summary, "severity_basis": basis, "severity_scale": scale,
+            "in_cover_window": a.details.get("in_cover_window"),
+            "bbch": a.details.get("bbch"),
+            # suspected (snow, hail): no sensor, a photo is asked for to confirm
+            "suspected": a.status == EVENT and camera_request(a.details) is not None,
+            "camera_request": camera_request(a.details) if a.status == EVENT else None,
+            **_monthly_field(block, a)}
+
+
+def _merge_daytime(entry: dict, runs: list[dict], station_id: int, day: date) -> dict:
+    """Hail: add the daytime checks of the report day (block DAYTIME_BLOCK, slots 13h / 19h) to
+    the Hail entry. The checks table stays the morning 24 h; a suspicion found by a daytime check
+    makes the field "suspected" with that check's photo request, and its summary is appended."""
+    if not runs:
+        return entry
+    entry = {**entry, "fields": [dict(f) for f in entry["fields"]], "data_notes": list(entry["data_notes"])}
+    by_id = {f["field_id"]: f for f in entry["fields"]}
+    windows = []
+    for run in runs:
+        slot = run.get("slot", "")
+        if run.get("error"):
+            entry["data_notes"].append(f"Daytime check {slot[:2]}:00 could not run: {run['error'][:300]}")
+            continue
+        for a in (x for x in run["analyses"] if x.station_id == station_id):
+            start, end = a.details.get("window_start"), a.details.get("window_end")
+            if start and end:
+                windows.append((start, end))
+            f = by_id.get(a.field_id)
+            if f is None:                               # no morning result for this field
+                f = by_id[a.field_id] = _field_entry("hail", a)
+                f.update(status=NO_EVENT if a.status == EVENT else a.status, suspected=False,
+                         camera_request=None, summary="", duration_h=0.0, start=None, end=None)
+                entry["fields"].append(f)
+            label = f"Daytime check {_hour(start)[-5:]} → {_hour(end)[-5:]}" if start and end else "Daytime check"
+            f["summary"] = (f["summary"] + " " if f["summary"] else "") + f"{label}: {a.summary}"
+            f.setdefault("daytime_checks", []).append(
+                {"slot": slot, "window_start": start, "window_end": end, "status": a.status,
+                 "summary": a.summary, "camera_request": camera_request(a.details)})
+            if a.status == EVENT:
+                if f["status"] != EVENT or not f.get("suspected"):
+                    f.update(status=EVENT, start=_iso(a.start), end=_iso(a.end), duration_h=0.0,
+                             severity_basis=_severity_basis(a)[0], severity_scale="",
+                             in_cover_window=a.details.get("in_cover_window"))
+                f["suspected"] = True
+                f["camera_request"] = camera_request(a.details)        # the newest photo request
+                f["start"] = min(filter(None, [f["start"], _iso(a.start)]))
+                f["end"] = max(filter(None, [f["end"], _iso(a.end)]))
+                f["duration_h"] = f["duration_h"] + a.duration_h
+            for n in a.data_notes:
+                note = f"{label}: {n}"
+                if note not in entry["data_notes"] and not any(h in n for h in HIDDEN_NOTES):
+                    entry["data_notes"].append(note)
+    if windows:
+        entry["watch_window"] = {"label": "Daytime checks", "start": min(w[0] for w in windows),
+                                 "end": max(w[1] for w in windows)}
+    if any(f["status"] == EVENT for f in entry["fields"]):
+        entry["status"] = EVENT
+    elif entry["status"] == "not_run" and entry["fields"]:
+        entry["status"] = NO_EVENT if any(f["status"] == NO_EVENT for f in entry["fields"]) else NO_DATA
+    if entry.get("run_day") is None:
+        entry.update(run_day=day.isoformat(), stale=False)
+    return entry
+
+
+def _monthly_field(block: str, a: Analysis) -> dict:
+    """Monthly blocks (drought): the event's period as text, and its cover line (drought has
+    no contract cover period in thresholds.COVER_WINDOWS)."""
+    if block not in MONTHLY_BLOCKS or a.status != EVENT:
+        return {}
+    days = a.details.get("days")
+    return {"period_text": f"{a.details.get('window_label')} ({days} days)" if days else
+            a.details.get("window_label"),
+            "duration_text": f"{days} d" if days else "-",
+            "cover_text": "no contract cover period for drought (not in thresholds.py)"}
+
+
+def _past_event_notice(block: str, status: str, details: dict) -> dict | None:
+    """No new event, but a past one to claim (Flood's last_heavy_72h, rain row 3)."""
+    heavy = details.get("last_heavy_72h") if block == "flood" else None
+    if status != NO_EVENT or not heavy:
+        return None
+    first, last = datetime.fromisoformat(heavy["first_rain"]), datetime.fromisoformat(heavy["last_rain"])
+    mm = EXCESSIVE_RAIN["intense_72h_mm"]
+    return {"short": f"Past: {heavy['mm']:g} mm in 72 h ({first:%d %b} → {last:%d %b})",
+            "text": (f"No new event, but {heavy['mm']:g} mm of rain fell within 72 h in the past "
+                     f"days ({_hour(heavy['first_rain'])} → {_hour(heavy['last_rain'])}; "
+                     f"{mm:g} mm reached on {_hour(heavy['reached_at'])}).")}
+
+
+def _photo_text(request: dict) -> str:
+    """One line for the client: why a photo is asked for, and when."""
+    when = datetime.fromisoformat(request["photo_at"]).strftime("%d %b %H:%M")
+    return f"{request['reason']} Photo requested for {when} to confirm."
+
+
+def build(station_id: int, day: date | None = None, root: Path | None = None) -> dict:
+    """Report content of one station: every event, with its block's newest run up to `day`."""
+    station = station_by_id(station_id)
+    stored = report.blocks(root)
+    merged = set(DAYTIME_BLOCK_OF.values())           # stored blocks shown inside another block
+    order = list(EVENTS) + [b for b in stored if b not in EVENTS and b not in merged]
+    latest = {b: report.latest_run(b, day, root) if b in stored else None for b in order}
+    day = day or max((r["run_day"] for r in latest.values() if r), default=date.today())
+    blocks = [_block_entry(b, run, station_id, station, day) for b, run in latest.items()]
+    for i, b in enumerate(blocks):
+        extra = DAYTIME_BLOCK_OF.get(b["block"])
+        if extra and extra in stored:
+            blocks[i] = _merge_daytime(b, report.day_runs(extra, day, root), station_id, day)
+    names = {a.station_name for r in latest.values() if r for a in r["analyses"]
+             if a.station_id == station_id}
+    name = station.name if station else (names.pop() if names else str(station_id))
+    found = [(b, f) for b in blocks for f in b["fields"] if f["status"] == EVENT]
+    events = [(b, f) for b, f in found if not f.get("suspected")]
+    suspects = [(b, f) for b, f in found if f.get("suspected")]      # photo asked for (snow, hail)
+    worst = max((Severity(f["severity"]) for _, f in events), key=lambda s: s.level, default=Severity.NONE)
+    suspected_titles = list(dict.fromkeys(b["title"] for b, _ in suspects))
+    if events:
+        titles = list(dict.fromkeys(b["title"] for b, _ in events))
+        headline = f"{' and '.join(titles)} detected  ·  worst severity: {worst.value.upper()}"
+        if suspects:
+            headline += f"  ·  {' and '.join(suspected_titles)} suspected"
+    elif suspects:
+        headline = f"{' and '.join(suspected_titles)} suspected  ·  photo requested to confirm"
+    elif any(b.get("notice") for b in blocks):
+        headline = "No new damaging event  ·  a past one was found (see below)."
+    elif any(b["status"] == NO_EVENT for b in blocks):
+        headline = "No damaging weather event detected."
+    else:
+        headline = "No usable data for this station."
+    fields = {f["field_id"]: f["field_name"] for b in blocks for f in b["fields"]}
+    return {
+        "station": {"id": station_id, "name": name,
+                    "sensors": sorted(station.sensors) if station else []},
+        "report_day": day.isoformat(),
+        "created_at": datetime.now().isoformat(timespec="minutes"),
+        "overall": {"events": len(events), "worst_severity": worst.value, "headline": headline,
+                    "suspected": [{"block": b["block"], "title": b["title"],
+                                   "text": _photo_text(f["camera_request"])} for b, f in suspects],
+                    "notices": [{"block": b["block"], "title": b["title"], "text": b["notice"]["text"]}
+                                for b in blocks if b.get("notice")],
+                    "errors": [{"block": b["block"], "title": b["title"], "error": b["error"]}
+                               for b in blocks if b["status"] == ERROR]},
+        "fields": [{"field_id": k, "field_name": v} for k, v in fields.items()],
+        "blocks": blocks,
+    }
+
+
+def write(station_id: int, day: date | None = None, root: Path | None = None,
+          out_dir: Path | None = None) -> tuple[Path, Path]:
+    """Write <day>.json and <day>.pdf of one station. A report of the same day is replaced;
+    the other days stay, and only the last REPORT["history_runs"] (10) days are kept."""
+    from damage_engine.report import station_pdf          # needs fpdf2 only when writing
+
+    content = build(station_id, day, root)
+    folder = (out_dir or STATION_REPORTS_DIR) / str(station_id)
+    folder.mkdir(parents=True, exist_ok=True)
+    json_path = folder / f"{content['report_day']}.json"
+    json_path.write_text(json.dumps(content, indent=2, ensure_ascii=False), encoding="utf-8")
+    pdf_path = station_pdf.render(content, json_path.with_suffix(".pdf"))
+    for old in sorted(folder.glob("*.json"), reverse=True)[REPORT["history_runs"]:]:
+        old.unlink()
+        old.with_suffix(".pdf").unlink(missing_ok=True)
+    return json_path, pdf_path
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--station", type=int, required=True, help="3A station id, e.g. 566")
+    p.add_argument("--date", type=date.fromisoformat, help="report day (default: newest block run)")
+    args = p.parse_args()
+    for path in write(args.station, args.date):
+        print(path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/damage_engine/schemas.py b/src/damage_engine/schemas.py
new file mode 100644
index 0000000..cc3e5c6
--- /dev/null
+++ b/src/damage_engine/schemas.py
@@ -0,0 +1,107 @@
+"""
+Shared data shapes. Every block may import this file.
+
+A block returns a list of Analysis: one per field and station it looked at,
+also when nothing happened, so the report can say "no brina last night".
+It carries the (phenomenon, severity, duration) message of the diagram plus
+the metadata the report needs (field, station, run day, ...).
+"""
+
+from dataclasses import asdict, dataclass, field
+from datetime import date, datetime
+from enum import Enum
+
+from damage_engine.thresholds import SEVERITY_LEVELS
+
+
+class Severity(str, Enum):
+    """Severity levels; the names and their order come from thresholds.SEVERITY_LEVELS."""
+    NONE = "none"
+    LIGHT = "light"
+    MODERATE = "moderate"
+    SEVERE = "severe"
+    EXTREME = "extreme"
+
+    @classmethod
+    def from_level(cls, level: int) -> "Severity":
+        return cls[SEVERITY_LEVELS[level]]
+
+    @property
+    def level(self) -> int:
+        return next(k for k, name in SEVERITY_LEVELS.items() if name == self.name)
+
+
+# Analysis.status
+EVENT = "event"            # the block detected its phenomenon
+NO_EVENT = "no_event"      # data was fine, nothing detected
+NO_DATA = "no_data"        # the station could not be judged (no sensors / no records)
+
+
+# Camera request: blocks without a sensor for their phenomenon (snow, hail) only flag that it
+# was POSSIBLE and ask the camera for a photo; the Hail/Snow CV model confirms and grades it.
+# The request is stored as a dict in Analysis.details["camera_request"] (CameraRequest.to_dict),
+# so the camera / CV side reads it without importing the detector.
+SUSPECT_POSSIBLE = "possible"      # weak signals only
+SUSPECT_LIKELY = "likely"          # strong signals
+
+
+@dataclass
+class CameraRequest:
+    phenomenon: str            # "snow" or "hail"
+    station_id: int
+    photo_at: datetime         # when to take the photo (local time)
+    level: str                 # SUSPECT_POSSIBLE or SUSPECT_LIKELY
+    signals: list[str]         # the signals that fired, e.g. ["A", "D"]
+    reason: str                # one sentence: why the photo is asked for
+
+    def to_dict(self):
+        d = asdict(self)
+        d["photo_at"] = self.photo_at.isoformat(timespec="minutes")
+        return d
+
+
+def camera_request(details: dict) -> dict | None:
+    """The camera request stored in an Analysis' details, or None."""
+    return details.get("camera_request") if details else None
+
+
+@dataclass
+class Analysis:
+    block: str                 # e.g. "brina"
+    run_day: date              # day the block ran for
+    field_id: str
+    field_name: str
+    station_id: int
+    station_name: str
+    status: str                # EVENT, NO_EVENT or NO_DATA
+    severity: Severity = Severity.NONE
+    severity_score: float = 0.0    # 0-1, see thresholds.SEVERITY_SCORE_AT_LEVEL_START
+    duration_h: float = 0.0        # hours the event conditions held
+    start: datetime | None = None
+    end: datetime | None = None
+    summary: str = ""              # one sentence for the farmer
+    details: dict = field(default_factory=dict)  # block-specific values (min temp, ...)
+    # missing sensors / missing data and how the block handled them, one sentence
+    # each; every block fills it and the report always shows it
+    data_notes: list[str] = field(default_factory=list)
+
+    @property
+    def detected(self) -> bool:
+        return self.status == EVENT
+
+    def to_dict(self):
+        d = asdict(self)
+        d["severity"] = self.severity.value
+        d["run_day"] = self.run_day.isoformat()
+        d["start"] = self.start.isoformat() if self.start else None
+        d["end"] = self.end.isoformat() if self.end else None
+        return d
+
+    @classmethod
+    def from_dict(cls, d):
+        d = dict(d)
+        d["severity"] = Severity(d["severity"])
+        d["run_day"] = date.fromisoformat(d["run_day"])
+        d["start"] = datetime.fromisoformat(d["start"]) if d.get("start") else None
+        d["end"] = datetime.fromisoformat(d["end"]) if d.get("end") else None
+        return cls(**d)
diff --git a/src/damage_engine/severity.py b/src/damage_engine/severity.py
new file mode 100644
index 0000000..3ca7f8f
--- /dev/null
+++ b/src/damage_engine/severity.py
@@ -0,0 +1,43 @@
+"""
+Severity grading shared by every block.
+
+Turns a value into a Severity level and a 0-1 score using a band dict from
+thresholds.py ({"edges": [...4 values...], "higher_is_worse": bool, "strict": bool}):
+
+    from damage_engine.severity import grade
+    from damage_engine.thresholds import FROST_FALLBACK_BANDS_C
+
+    grade(-3.1, FROST_FALLBACK_BANDS_C)    # -> (Severity.MODERATE, 0.638)
+"""
+
+from damage_engine.schemas import Severity
+from damage_engine.thresholds import SEVERITY_SCORE_AT_LEVEL_START
+
+
+def level_for(value, bands) -> int:
+    """0 (NONE) to 4 (EXTREME): number of band edges the value reaches."""
+    higher, strict = bands["higher_is_worse"], bands.get("strict", False)
+
+    def reached(edge):
+        if higher:
+            return value > edge if strict else value >= edge
+        return value < edge if strict else value <= edge
+
+    return sum(1 for edge in bands["edges"] if reached(edge))
+
+
+def score_for(value, bands, level) -> float:
+    """s_sev: the level's start score, interpolated linearly up to the next level's start."""
+    if level == 0:
+        return 0.0
+    scores, edges = SEVERITY_SCORE_AT_LEVEL_START, bands["edges"]
+    if level >= len(edges):
+        return scores[len(edges)]
+    lo, hi = edges[level - 1], edges[level]
+    frac = min(max((value - lo) / (hi - lo), 0.0), 1.0)
+    return scores[level] + frac * (scores[level + 1] - scores[level])
+
+
+def grade(value, bands) -> tuple[Severity, float]:
+    level = level_for(value, bands)
+    return Severity.from_level(level), round(score_for(value, bands, level), 3)
diff --git a/src/damage_engine/sources/__init__.py b/src/damage_engine/sources/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/damage_engine/sources/derived.py b/src/damage_engine/sources/derived.py
new file mode 100644
index 0000000..10a8dcb
--- /dev/null
+++ b/src/damage_engine/sources/derived.py
@@ -0,0 +1,108 @@
+"""
+Quantities CALCULATED from 3A sensor data, one implementation for the whole
+pipeline. Every block imports them from here and never keeps its own formula,
+so a value (e.g. the wet-bulb temperature of an hour) is the same in every
+block and in every report.
+
+Reference [3A-DOC]: 3A, "Algoritmi di stima di alcune grandezze
+agrometeorologiche" (docs/ImplementazioneAlgoritmiSensori.pdf), section 4
+"Temperatura bulbo umido (Tw)". It is the formula 3A uses for its own TBulbMed:
+on (hourly mean temperature, mean humidity) it gives 3A's TBulbMed in 49 of 49
+hours at station 566 (to 3A's 0.1 °C rounding, checked 2026-10-21).
+
+Wet-bulb of one hour, from the 3A hourly columns (add_wet_bulb):
+    WetBulbMin = Tw(TemperaturaAriaMin, UmiditaRelativaMax)   lowest of the hour
+    WetBulbMax = Tw(TemperaturaAriaMax, UmiditaRelativaMin)   highest of the hour
+Why these pairs: within an hour the water vapour in the air changes little, so
+the coldest moment is also the most humid one (and the warmest the driest).
+The same pairing (Tmin with RHmax, Tmax with RHmin) is used by the 3A document
+for the vapour pressure, section 3 eq. (2) (FAO-56). Checked on 566: the vapour
+pressure of (Tmin, RHmax), (Tmean, RHmean) and (Tmax, RHmin) differs by < 1 hPa.
+
+Missing values: no hourly minimum / maximum temperature -> hourly mean
+temperature; no hourly maximum / minimum humidity -> hourly mean humidity; no
+temperature or no humidity at all -> NaN (the block decides what to do).
+
+    from damage_engine.sources import derived
+    df = fetch_hourly(ids, start, end, [*derived.WET_BULB_INPUTS, ...])
+    df = derived.add_wet_bulb(df)          # adds WetBulbMin, WetBulbMax (°C)
+"""
+
+import numpy as np
+import pandas as pd
+
+K0 = 273.15                 # 0 °C in K
+
+# constants of the 3A document, section 4 [3A-DOC]
+RV = 0.11                   # cal/(Kg K)  gas constant of moist air
+CP = 0.24                   # cal/(Kg K)  specific heat at constant pressure
+L_EVAPORATION = 597.0       # cal/Kg      latent heat, T > 0 °C (water)
+L_SUBLIMATION = 677.0       # cal/Kg      latent heat, T <= 0 °C (ice)
+PRESSURE_HPA = 1000.0       # mbar        atmospheric pressure ("generalmente si può porre 1000 mbar")
+# saturation vapour pressure e*(T) = 6.1 * 10^(a*T / (T + b)), T in °C
+MAGNUS_WATER = (7.5, 237.3)     # T > 0 °C
+MAGNUS_ICE = (9.5, 265.5)       # T <= 0 °C
+
+# 3A hourly columns (api_parameters) used, and the columns added by add_wet_bulb
+T_MIN, T_MEAN, T_MAX = "TemperaturaAriaMin", "TemperaturaAriaMedia", "TemperaturaAriaMax"
+RH_MIN, RH_MEAN, RH_MAX = "UmiditaRelativaMin", "UmiditaRelativaMedia", "UmiditaRelativaMax"
+WET_BULB_INPUTS = [T_MIN, T_MEAN, T_MAX, RH_MIN, RH_MEAN, RH_MAX]
+WET_BULB_MIN = "WetBulbMin"     # °C, lowest wet-bulb of the hour
+WET_BULB_MAX = "WetBulbMax"     # °C, highest wet-bulb of the hour
+
+
+def _branch(t_c):
+    """(a, b, L) per value: ice constants for T <= 0 °C, water constants above."""
+    ice = t_c <= 0
+    a = np.where(ice, MAGNUS_ICE[0], MAGNUS_WATER[0])
+    b = np.where(ice, MAGNUS_ICE[1], MAGNUS_WATER[1])
+    latent = np.where(ice, L_SUBLIMATION, L_EVAPORATION)
+    return a, b, latent
+
+
+def vapour_pressure_hpa(temp_c, rh_pct):
+    """Vapour pressure e = RH * e*(T) / 100 (hPa) [3A-DOC sec. 4]."""
+    t = np.asarray(temp_c, dtype=float)
+    rh = np.asarray(rh_pct, dtype=float)
+    a, b, _ = _branch(t)
+    return rh / 100.0 * 6.1 * 10.0 ** (a * t / (t + b))
+
+
+def dew_point_c(temp_c, rh_pct):
+    """Dew point (frost point for T <= 0 °C), °C [3A-DOC sec. 4 eq. 2-3, sec. 5]."""
+    t = np.asarray(temp_c, dtype=float)
+    a, b, _ = _branch(t)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        x = np.log10(vapour_pressure_hpa(t, rh_pct) / 6.1)
+        return b * x / (a - x)
+
+
+def wet_bulb_c(temp_c, rh_pct, pressure_hpa=PRESSURE_HPA):
+    """Wet-bulb temperature (°C) [3A-DOC sec. 4 eq. 1]:
+
+        Tw = Td + (T - Td) / (1 + L² s / (Rv Cp T²)) - 273.15     (T, Td in K)
+        s  = 0.622 e / (P - 0.378 e)                              (specific humidity)
+    Water constants for T > 0 °C, ice constants for T <= 0 °C. RH <= 0 gives NaN.
+    """
+    t = np.asarray(temp_c, dtype=float)
+    _, _, latent = _branch(t)
+    e = vapour_pressure_hpa(t, rh_pct)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        s = 0.622 * e / (pressure_hpa - 0.378 * e)
+        t_k, td_k = t + K0, dew_point_c(t, rh_pct) + K0
+        return td_k + (t_k - td_k) / (1.0 + latent ** 2 * s / (RV * CP * t_k ** 2)) - K0
+
+
+def _col(df, name):
+    return pd.to_numeric(df[name], errors="coerce") if name in df else pd.Series(np.nan, index=df.index)
+
+
+def add_wet_bulb(df: pd.DataFrame) -> pd.DataFrame:
+    """Copy of the hourly rows with WetBulbMin and WetBulbMax (°C) added (see module doc)."""
+    df = df.copy()
+    t_mean, rh_mean = _col(df, T_MEAN), _col(df, RH_MEAN)
+    t_lo, rh_hi = _col(df, T_MIN).fillna(t_mean), _col(df, RH_MAX).fillna(rh_mean)
+    t_hi, rh_lo = _col(df, T_MAX).fillna(t_mean), _col(df, RH_MIN).fillna(rh_mean)
+    df[WET_BULB_MIN] = np.round(wet_bulb_c(t_lo, rh_hi), 2)
+    df[WET_BULB_MAX] = np.round(wet_bulb_c(t_hi, rh_lo), 2)
+    return df
diff --git a/src/damage_engine/sources/fields.py b/src/damage_engine/sources/fields.py
new file mode 100644
index 0000000..823a887
--- /dev/null
+++ b/src/damage_engine/sources/fields.py
@@ -0,0 +1,53 @@
+"""
+Field registry: which farmer fields exist and which station watches each one.
+
+Read from data/fields.json:
+
+    {"fields": [
+        {"field_id": "F001", "name": "Melo nord", "station_id": 3, "crop": "apple", "bbch": 65}
+    ]}
+
+"bbch" is the current phenological stage of the field (optional; update it
+during the season). Without it, frost blocks use the fallback severity bands.
+If data/fields.json does not exist, every station counts as one field with
+id "ST<station id>", so the pipeline works before real fields are entered.
+"""
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+from damage_engine.sources.stations import all_stations
+
+FIELDS_FILE = Path(__file__).resolve().parents[3] / "data" / "fields.json"
+
+
+@dataclass(frozen=True)
+class Field:
+    id: str
+    name: str
+    station_id: int
+    crop: str = "apple"
+    bbch: int | None = None
+
+
+def all_fields(path: Path | None = None) -> list[Field]:
+    path = path or FIELDS_FILE
+    if not path.exists():
+        return [Field(id=f"ST{s.id}", name=f"{s.name} (station {s.id})", station_id=s.id)
+                for s in all_stations()]
+    data = json.loads(path.read_text(encoding="utf-8"))
+    return [
+        Field(
+            id=str(f["field_id"]),
+            name=f.get("name", str(f["field_id"])),
+            station_id=int(f["station_id"]),
+            crop=f.get("crop", "apple"),
+            bbch=int(f["bbch"]) if f.get("bbch") is not None else None,
+        )
+        for f in data["fields"]
+    ]
+
+
+def field_by_id(field_id, path: Path | None = None):
+    return next((f for f in all_fields(path) if f.id == str(field_id)), None)
diff --git a/src/damage_engine/sources/stations.py b/src/damage_engine/sources/stations.py
new file mode 100644
index 0000000..39085de
--- /dev/null
+++ b/src/damage_engine/sources/stations.py
@@ -0,0 +1,62 @@
+"""
+Station catalogue: which 3A stations exist and which sensors each one has.
+
+Read from API/3a_stations_sensors.json (built from the 3A metadata calls), so
+blocks can pick their stations without spending rate-limited API requests:
+
+    from damage_engine.sources.stations import stations_with
+
+    stations_with("TemperaturaAria", "UmiditaRelativa")   # -> [Station, ...]
+"""
+
+import json
+from dataclasses import dataclass, field
+from functools import lru_cache
+from pathlib import Path
+
+CATALOGUE_FILE = Path(__file__).resolve().parents[3] / "API" / "3a_stations_sensors.json"
+
+
+@dataclass(frozen=True)
+class Station:
+    id: int
+    name: str
+    latitude: float | None
+    longitude: float | None
+    elevation_m: float | None
+    sensors: dict[str, list[str]] = field(default_factory=dict)  # sensor -> api_parameters
+
+    def has(self, *sensors):
+        return all(s in self.sensors for s in sensors)
+
+
+def _num(value):
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+@lru_cache(maxsize=1)
+def all_stations():
+    data = json.loads(CATALOGUE_FILE.read_text(encoding="utf-8"))
+    return [
+        Station(
+            id=int(s["StationId"]),
+            name=s["StationName"],
+            latitude=_num(s.get("Latitude_WGS84")),
+            longitude=_num(s.get("Longitude_WGS84")),
+            elevation_m=_num(s.get("Elevation_m")),
+            sensors={x["sensor"]: list(x["api_parameters"]) for x in s.get("Sensors", [])},
+        )
+        for s in data["Stations"]
+    ]
+
+
+def stations_with(*sensors):
+    """Stations that have every one of the given sensors (3A sensor names)."""
+    return [s for s in all_stations() if s.has(*sensors)]
+
+
+def station_by_id(station_id):
+    return next((s for s in all_stations() if s.id == int(station_id)), None)
diff --git a/src/damage_engine/sources/threea.py b/src/damage_engine/sources/threea.py
new file mode 100644
index 0000000..0d114a7
--- /dev/null
+++ b/src/damage_engine/sources/threea.py
@@ -0,0 +1,242 @@
+"""
+Shared client for the 3A weather API (https://api.3acloud.it).
+
+Every block that needs sensor data uses this file, so login, token caching,
+the rate limit and the response parsing live in one place:
+
+    from damage_engine.sources.threea import fetch_hourly
+
+    df = fetch_hourly(station_ids=[3, 6], start=..., end=...,
+                      columns=["TemperaturaAriaMin", "UmiditaRelativaMedia"])
+
+Auth (from the API spec):
+    POST /login with headers name, password, totp  -> token
+    every other call sends the header  token: <token>
+
+Credentials are read, in this order, from
+    1. environment variables  THREEA_USER, THREEA_PASSWORD, THREEA_TOTP_SECRET
+    2. a .env file in the project folder (same names, KEY=value lines)
+    3. API/3a_credentials.json (keys user, password, totp_secret)
+The TOTP secret is needed for unattended runs (e.g. Brina at 06:00): with it,
+the 6-digit codes are generated automatically.
+
+The server allows 5 requests per 15 minutes; requests wait for the window.
+"""
+
+import json
+import logging
+import os
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd
+import requests
+
+log = logging.getLogger(__name__)
+
+BASE_URL = "https://api.3acloud.it"
+TIMEOUT_S = 120
+DATE_FMT = "%Y-%m-%d %H:%M"
+
+PROJECT_DIR = Path(__file__).resolve().parents[3]
+CRED_FILE = PROJECT_DIR / "API" / "3a_credentials.json"
+ENV_FILE = PROJECT_DIR / ".env"
+CACHE_DIR = PROJECT_DIR / "data" / "3a_cache"
+TOKEN_FILE = CACHE_DIR / ".token"
+
+
+class ThreeAError(RuntimeError):
+    pass
+
+
+# ─────────────────────────── credentials ───────────────────────────
+
+def _read_env_file():
+    if not ENV_FILE.exists():
+        return {}
+    values = {}
+    for line in ENV_FILE.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if line and not line.startswith("#") and "=" in line:
+            key, value = line.split("=", 1)
+            values[key.strip()] = value.strip().strip('"').strip("'")
+    return values
+
+
+def _credential(env_name, json_key):
+    if os.environ.get(env_name):
+        return os.environ[env_name]
+    value = _read_env_file().get(env_name)
+    if value:
+        return value
+    if CRED_FILE.exists():
+        value = json.loads(CRED_FILE.read_text(encoding="utf-8")).get(json_key, "")
+        if value and not value.startswith("PUT_"):
+            return value
+    return None
+
+
+def _totp_code():
+    secret = _credential("THREEA_TOTP_SECRET", "totp_secret")
+    if secret:
+        import pyotp
+        return pyotp.TOTP(secret.replace(" ", "")).now()
+    if not sys.stdin.isatty():
+        raise ThreeAError("No TOTP secret configured and no terminal to ask for a code; "
+                          "set THREEA_TOTP_SECRET for unattended runs.")
+    return input("Paste a fresh 6-digit TOTP code: ").strip().replace(" ", "")
+
+
+# ─────────────────────────── client ───────────────────────────
+
+class ThreeAClient:
+    """One authenticated session. Use the module-level `client()` to share it."""
+
+    def __init__(self):
+        self.session = requests.Session()
+        self.session.headers["output-fmt"] = "json"
+        self._rate_remaining = None
+        self._rate_reset = None
+
+    # rate limit
+    def _wait_for_rate_limit(self):
+        if self._rate_remaining == 0 and self._rate_reset and self._rate_reset > time.time():
+            wait = self._rate_reset - time.time() + 2
+            log.info("3A rate limit reached, waiting %.0f s", wait)
+            time.sleep(wait)
+
+    def _send(self, method, path, **kw):
+        while True:
+            self._wait_for_rate_limit()
+            r = self.session.request(method, BASE_URL + path, timeout=TIMEOUT_S, **kw)
+            if "X-RateLimit-Remaining" in r.headers:
+                self._rate_remaining = int(r.headers["X-RateLimit-Remaining"])
+            if "X-RateLimit-Reset" in r.headers:
+                self._rate_reset = int(r.headers["X-RateLimit-Reset"])
+            if r.status_code != 429:
+                return r
+            self._rate_remaining = 0
+            self._rate_reset = self._rate_reset or time.time() + 15 * 60
+
+    # auth
+    def login(self):
+        user = _credential("THREEA_USER", "user")
+        password = _credential("THREEA_PASSWORD", "password")
+        if not (user and password):
+            raise ThreeAError("3A user/password not configured (see damage_engine/sources/threea.py).")
+        r = self._send("POST", "/login",
+                       headers={"name": user, "password": password, "totp": _totp_code()})
+        token = _extract_token(r) if r.ok else None
+        if not token:
+            raise ThreeAError(f"3A login failed ({r.status_code}): {r.text[:200]}")
+        self.session.headers["token"] = token
+        CACHE_DIR.mkdir(parents=True, exist_ok=True)
+        TOKEN_FILE.write_text(token)
+        log.info("3A login ok, token cached")
+
+    def _ensure_token(self):
+        if "token" in self.session.headers:
+            return
+        if TOKEN_FILE.exists():
+            self.session.headers["token"] = TOKEN_FILE.read_text().strip()
+        else:
+            self.login()
+
+    def call(self, method, path, body=None):
+        """Authenticated call; logs in again once if the token was rejected."""
+        self._ensure_token()
+        r = self._send(method, path, json=body)
+        if r.status_code in (401, 403):
+            log.info("3A token rejected (%s), logging in again", r.status_code)
+            TOKEN_FILE.unlink(missing_ok=True)
+            self.session.headers.pop("token", None)
+            self.login()
+            r = self._send(method, path, json=body)
+        if not r.ok:
+            raise ThreeAError(f"{method} {path} failed ({r.status_code}): {r.text[:500]}")
+        try:
+            return r.json()
+        except ValueError:
+            return r.text
+
+
+def _extract_token(resp):
+    try:
+        data = resp.json()
+    except ValueError:
+        return resp.text.strip().strip('"') or None
+    if isinstance(data, str):
+        return data
+    if isinstance(data, dict):
+        for k in ("token", "access_token", "Token"):
+            if k in data:
+                return data[k]
+    return None
+
+
+_client = None
+
+
+def client():
+    global _client
+    if _client is None:
+        _client = ThreeAClient()
+    return _client
+
+
+# ─────────────────────────── data ───────────────────────────
+
+def to_frame(data):
+    """Turn a 3A JSON response (list of rows, or {key: [rows]}) into a DataFrame."""
+    if isinstance(data, list):
+        if not data:
+            return pd.DataFrame()
+        if all(isinstance(x, dict) for x in data):
+            return pd.json_normalize(data)
+    if isinstance(data, dict):
+        lists = [v for v in data.values() if isinstance(v, list)]
+        if len(lists) == 1:
+            return to_frame(lists[0])
+        if lists and all(isinstance(x, dict) for v in lists for x in v):
+            return to_frame([x for v in lists for x in v])
+    raise ThreeAError(f"Unexpected 3A response shape: {str(data)[:200]}")
+
+
+# time column of each kind: daily rows have only "Data" (the API rejects "Timestamp")
+TIME_COLUMN = {"Hourly": "Timestamp", "Daily": "Data"}
+
+
+def _fetch_weather(kind, station_ids, start, end, columns):
+    time_col = TIME_COLUMN[kind]
+    cols = list(dict.fromkeys(["CodiceStazione", time_col, *columns]))
+    body = {"stationsIds": [int(s) for s in station_ids],
+            "dateFrom": start.strftime(DATE_FMT), "dateTo": end.strftime(DATE_FMT),
+            "columns": cols}
+    log.info("3A %s data %s … %s for %d station(s)", kind, body["dateFrom"], body["dateTo"],
+             len(body["stationsIds"]))
+    df = to_frame(client().call("POST", f"/getWeather{kind}", body))
+    if df.empty:
+        return pd.DataFrame(columns=cols)
+    df[time_col] = pd.to_datetime(df[time_col])
+    for c in columns:
+        if c in df.columns:
+            df[c] = pd.to_numeric(df[c], errors="coerce")
+    # the API pads the series with empty 10-minute rows; keep only rows with values
+    df = df.dropna(subset=[c for c in columns if c in df.columns], how="all")
+    return df.sort_values(["CodiceStazione", time_col]).reset_index(drop=True)
+
+
+def fetch_hourly(station_ids, start: datetime, end: datetime, columns):
+    """Hourly rows for the stations in [start, end]; columns are 3A api_parameters."""
+    return _fetch_weather("Hourly", station_ids, start, end, columns)
+
+
+def fetch_daily(station_ids, start: datetime, end: datetime, columns):
+    """Daily rows for the stations in [start, end]; columns are 3A api_parameters.
+
+    The date column is "Data" (not "Timestamp"): CodiceStazione, Data, <columns>.
+    Daily-only columns (e.g. EvapoPot) are in DailyOnlyParameters of 3a_stations_sensors.json.
+    """
+    return _fetch_weather("Daily", station_ids, start, end, columns)
diff --git a/src/damage_engine/thresholds.py b/src/damage_engine/thresholds.py
new file mode 100644
index 0000000..19c00fb
--- /dev/null
+++ b/src/damage_engine/thresholds.py
@@ -0,0 +1,574 @@
+# -*- coding: utf-8 -*-
+"""
+thresholds.py - THRESHOLD REFERENCE FOR THE DAMAGE PIPELINE (APPLE)
+===================================================================
+
+This file contains VALUES ONLY: no functions, no processing, no imports.
+It is the ONLY place where thresholds live: every pipeline block reads them from here
+and no block keeps its own copy:
+
+    from damage_engine.thresholds import BRINA, SEVERITY_LEVELS
+    if t_air <= BRINA["t_air_max_c"]: ...
+
+To change pipeline behaviour, edit a number here; every block that reads it follows.
+The numbers come from the sources tagged below - do not change them without a source.
+
+CROP:      apple (melo) only.
+PHENOMENA: brina, gelo (Freezing), strong wind, excessive rain (eccesso di pioggia,
+           the "Flood" box of the diagram), snow, hail, hot wind, hot temperature
+           (High temperature box), drought (detector only: a rain deficit against the
+           climate average; no contract trigger, see DROUGHT).
+OUT OF SCOPE: drought claims and damage (future work), flooding by rivers (alluvione, not in
+              Model 1506 for this crop), heatwave, sunscald, sudden temperature change.
+
+CONTRACT: UnipolSai Assicurazioni S.p.A., "Agricultural Risks - Adverse Weather Conditions",
+          Model 1506, Ed. 01/2021 (English version). Page numbers refer to that document.
+
+SOURCE TAG in each comment:
+  [CONTRACT]     written in Model 1506 - change only if the insurer's wording changes
+  [SCALE]        standard meteorological scale (Beaufort, TORRO-based hail classes)
+  [LITERATURE]   published agronomic data (WSU / MSU)
+  [ENGINEERING]  project default, the contract gives no number -> CALIBRATE on own data
+  [PROJECT]      pipeline setting (schedule, history size), not a physical threshold
+  [EXTERNAL]     reference data from an outside service, copied here once by hand (the
+                 pipeline never calls it: only the 3A API is used at run time); edit freely
+
+UNITS are in the key names: _c = deg C, _ms = m/s, _mm = mm, _cm = cm, _wm2 = W/m2,
+_h = hours, _min = minutes, _days = days, _pct = percent, _deg = degrees (wind FROM).
+
+CONTRACT COMPARISONS: every contract trigger has a matching "_op" key giving the exact
+operator from the wording ("<" for "below", ">" for "more than", ">=" for "at least").
+Detection must use these operators.
+
+SEVERITY BANDS: a list of 4 edges = value where LIGHT, MODERATE, SEVERE, EXTREME start.
+  "higher_is_worse": True  -> value >= edge reaches that level (wind, rain, temperature)
+  "higher_is_worse": False -> value <= edge reaches that level (frost temperatures)
+  "strict": True           -> use > / < instead of >= / <=
+Bands only grade events that already passed detection (the "_op" check above).
+The contract only defines the yes/no trigger; levels above it are never contractual.
+"""
+
+CONTRACT_REF = ("UnipolSai Assicurazioni S.p.A., Model 1506 'Agricultural Risks - "
+                "Adverse Weather Conditions', Ed. 01/2021")
+CROP = "apple"
+
+
+# =============================================================================
+# SEVERITY SCALE (shared by all phenomena)
+# =============================================================================
+
+SEVERITY_LEVELS = {0: "NONE", 1: "LIGHT", 2: "MODERATE", 3: "SEVERE", 4: "EXTREME"}
+
+# s_sev (0-1) at the start of each level; interpolate linearly between edges   [ENGINEERING]
+SEVERITY_SCORE_AT_LEVEL_START = {1: 0.25, 2: 0.50, 3: 0.75, 4: 1.00}
+
+
+# =============================================================================
+# GENERAL CONTRACT RULES
+# =============================================================================
+
+GENERAL = {
+    # Official agrometeorological data (e.g. ARPA) for the area containing the plot decide
+    # whether a threshold was reached; field sensors are indicative.        [CONTRACT p.4]
+    "official_data_area_km2": 30,
+    # Effects must also appear on apple orchards within this radius,
+    # similar topography. Not required for hail.                            [CONTRACT p.7]
+    "effect_radius_km": 3,
+    # 'Reference period' for every average = last N years                   [CONTRACT p.7]
+    "reference_period_years": 5,
+    # No indemnity unless farm/municipality/product loss exceeds this       [CONTRACT Art.7 p.16]
+    "damage_threshold_pct": 20,
+    # Claim notice deadlines                                                [CONTRACT Art.10 p.20]
+    "claim_notice_working_days": 3,
+    "claim_notice_frost_h": 36,
+    # A 'for the record' claim can be converted to an assessment request
+    # within N days of the event...                                         [CONTRACT Art.10 p.21]
+    "claim_conversion_days": 20,
+    # ...and only for events at least N days before maturity (Art.10 p.21 says
+    # 'maturity date'; the information pack p.7 says 'harvest date')        [CONTRACT Art.10 p.21]
+    "claim_conversion_before_maturity_days": 20,
+}
+
+# Cover starts at 12:00 on the Nth day after notification                   [CONTRACT Art.6 p.15]
+COVER_START_LAG_DAYS = {
+    "hail": 3,
+    "strong_wind": 3,
+    "excessive_rain": 6,
+    "snow": 6,
+    "hot_temperature": 6,     # as 'Sunstroke'
+    "brina": 12,
+    "gelo": 12,
+    "hot_wind": 30,
+}
+
+# Cover window for apples: (phenological start, end date "MM-DD" at 12:00)  [CONTRACT Special Art.5 p.31]
+COVER_WINDOWS = {
+    "brina":           ("budburst", "05-10"),
+    "gelo":            ("budburst", "05-10"),
+    "excessive_rain":  ("fruit_set", "10-31"),
+    "hail":            ("fruit_set", "10-31"),
+    "strong_wind":     ("fruit_set", "10-31"),
+    "hot_wind":        ("fruit_set", "10-31"),
+    "hot_temperature": ("fruit_set", "10-31"),   # as 'Sunstroke'
+    "snow":            ("budburst", "10-31"),    # [ENGINEERING] snow not listed in Art.5 - verify
+}
+
+# Apples under anti-hail nets: hail cover ends on this date                 [CONTRACT Special Art.5 p.31]
+HAIL_NET_COVER_END = "05-25"
+
+# Recommended run cadence (from the pipeline figure)
+RUN_SCHEDULE = {
+    "brina": "daily, morning: previous night (sunset -> sunrise)",
+    "gelo": "every 24 h",
+    "strong_wind": "every 24 h",
+    "hail": "every 24 h",
+    "snow": "every 24 h",
+    "excessive_rain": "every 24 h",
+    "hot_temperature": "every 24 h",
+    "hot_wind": "every 24 h, only in the 30 days before harvest",
+    "drought": "every month (1st day): the last 3 complete calendar months",
+}
+
+
+# =============================================================================
+# FROST: APPLE CRITICAL TEMPERATURES (used by BRINA and GELO)
+# =============================================================================
+
+# Bud temperature (deg C) killing 10% and 90% of buds after ~30 min.
+# [LITERATURE] WSU EB0913, reproduced in MSU 'Critical spring temperatures for tree fruit bud stages'
+APPLE_CRITICAL_TEMPS_C = {
+    #  stage              (T10,   T90)
+    "silver_tip":       (-9.4, -16.7),
+    "green_tip":        (-7.8, -12.2),
+    "half_inch_green":  (-5.0,  -9.4),
+    "tight_cluster":    (-2.8,  -6.1),
+    "first_pink":       (-2.2,  -4.4),
+    "full_pink":        (-2.2,  -3.9),
+    "first_bloom":      (-2.2,  -3.9),
+    "full_bloom":       (-2.2,  -3.9),
+    "post_bloom":       (-2.2,  -3.9),
+    "fruitlet":         (-2.2,  -3.9),   # [ENGINEERING] post_bloom values reused
+}
+
+# Highest BBCH code of each stage (BBCH below 51 = dormant, frost-hardy)    [ENGINEERING] approx. mapping
+APPLE_BBCH_TO_STAGE = {
+    52: "silver_tip",
+    53: "green_tip",
+    54: "half_inch_green",
+    56: "tight_cluster",
+    57: "first_pink",
+    59: "full_pink",
+    61: "first_bloom",
+    65: "full_bloom",
+    69: "post_bloom",
+    99: "fruitlet",
+}
+APPLE_DORMANT_BELOW_BBCH = 51
+
+# Kill-fraction curve built on the table above                              [ENGINEERING]
+#   0% kill at T10 + no_damage_offset, 10% at T10, 90% at T90, 100% at T90 - full_kill_offset
+FROST_KILL_CURVE = {
+    "no_damage_offset_c": 1.0,
+    "full_kill_offset_c": 2.0,
+}
+
+# Kill fraction where each severity level starts                            [ENGINEERING]
+FROST_KILL_BANDS = {"edges": [0.0001, 0.10, 0.50, 0.90], "higher_is_worse": True}
+
+# Used only when the phenological stage is unknown (minimum temperature)    [ENGINEERING]
+# strict: LIGHT starts strictly below 0 C, matching the contract's "below 0 C"
+FROST_FALLBACK_BANDS_C = {"edges": [0.0, -2.0, -4.0, -6.0], "higher_is_worse": False, "strict": True}
+
+
+# =============================================================================
+# BRINA (hoar frost)
+# =============================================================================
+# Contract (p.7): freezing of dew / sublimation of water vapour on the crop due to
+# nocturnal radiation. NO temperature in the contract: all values are criteria for
+# radiative frost with ice deposition. Severity: frost tables above.
+# Brina and gelo share one guarantee, one excess (30%) and one window.
+
+BRINA = {
+    "night_solar_max_wm2": 10.0,      # records with more radiation = daytime, ignored    [ENGINEERING]
+    "calm_wind_max_ms": 2.0,          # calm: bud offset + ice test; above: air < 0 C     [ENGINEERING]
+    "t_air_max_c": 2.0,               # 2 m air may be slightly above 0 C with brina      [ENGINEERING]
+    "radiative_offset_c": 2.0,        # bud temp = t_air - offset on calm nights          [ENGINEERING]
+    "t_wet_max_c": 0.0,               # wet-bulb <= this supports ice deposition          [ENGINEERING]
+    "deposition": "bud_temp <= min(0, frost_point)",   # calm hours: ice forms at the frost point
+    "min_duration_h": 0.5,            # critical temps refer to ~30 min exposure          [LITERATURE]
+}
+
+# When the brina block runs and which night it looks at (RUN_SCHEDULE: sunset -> sunrise,
+# approximated by fixed local hours)                                        [PROJECT]
+BRINA_SCHEDULE = {
+    "run_hour": 6,                    # local time; the night window ends here
+    "night_start_hour": 18,           # window starts at this hour of the previous day
+}
+
+
+# =============================================================================
+# GELO (frost, "Freezing" box)
+# =============================================================================
+# Contract (p.7): a drop in temperature below 0 C caused by cold air masses.
+
+GELO = {
+    "t_air_trigger_c": 0.0,           # "a drop in temperature below 0 C"                 [CONTRACT p.7]
+    "t_air_trigger_op": "<",
+    "advective_wind_min_ms": 2.0,     # mean wind while < 0 C in daylight: reported only  [ENGINEERING]
+    "min_duration_h": 0.5,            # ~30 min exposure                                  [LITERATURE]
+}
+
+
+# =============================================================================
+# STRONG WIND (storm)
+# =============================================================================
+# Contract (p.8): "at least force 7 on the Beaufort scale - a speed of 50 km/h or more
+# (14 m/s)"; measurement valid below 10 m height. Contract does not say mean or gust.
+# 13.9 m/s is used because 50 km/h and the Beaufort 7 lower bound are both 13.9 m/s;
+# "14 m/s" is a rounded value. Set 14.0 to follow the bracketed figure instead
+# (readings of 13.9 m/s would then no longer trigger).
+
+STRONG_WIND = {
+    "wind_trigger_ms": 13.9,          # 50 km/h = Beaufort 7 lower bound                  [CONTRACT p.8]
+    "wind_trigger_op": ">=",          # "at least" / "or more"
+    # 3A sends hourly wind only, so the contract's 10-min mean cannot be read directly.
+    # "hourly_max" = VelocitaVento2mMax (highest value of the hour: catches short storms;
+    # 3A does not say if it is a 10-min mean or a gust); "hourly_mean" = VelocitaVento2mMedia
+    # (the storm must last the whole hour). Chosen by the user 2026-10-22.  [ENGINEERING]
+    "use_mean_or_gust": "hourly_max",
+    "min_duration_min": 10,           # one 10-min averaging period                       [ENGINEERING]
+}
+STRONG_WIND_BANDS_MS = {"edges": [13.9, 17.2, 20.8, 24.5], "higher_is_worse": True}   # Beaufort 7/8/9/10  [SCALE]
+
+# When the strong wind block runs and which hours it reads                  [PROJECT]
+# Event day = the 24 h ending at run_hour (records after 07:00 of the previous day up to 07:00).
+STRONG_WIND_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+}
+
+
+# =============================================================================
+# HAIL
+# =============================================================================
+# Contract (p.8): frozen water falling as ice pellets of varying sizes. ANY hail counts,
+# no size threshold, 3-km rule does not apply. Weather sensors only give precursors;
+# confirmation comes from the CV model / external hail data.
+# No 3A station has a hail sensor, so the Hail block only flags POSSIBLE hail from these
+# precursors and asks the camera for a photo, as Snow does (see detectors/hail.py).
+# 3A data is hourly, so the 10-min and 30-min values are read per hour (comments below).
+
+HAIL = {
+    "rain_10min_mm": 5.0,             # burst rainfall in 10 min; hourly data: compared as a
+                                      # rain rate (RainRateMax, station 293 only) >= 6 x this
+                                      # in mm/h                                           [ENGINEERING]
+    "rain_1h_mm": 20.0,               # burst rainfall in 1 h                             [ENGINEERING]
+    "t_drop_30min_c": 4.0,            # temperature drop in 30 min (storm outflow); hourly
+                                      # data: previous hour's mean - this hour's minimum  [ENGINEERING]
+    "gust_ms": 13.9,                  # squall gust (hourly maximum wind)                 [ENGINEERING]
+    "min_indicators": 2,              # precursors (of the 3 above) for "likely" hail; 1 precursor
+                                      # = "possible", also asks for a photo (user's choice
+                                      # 2026-10-26: high recall, as Snow)                  [ENGINEERING]
+    "storm_window_h": 1,              # a precursor counts only within N h of a rain hour (hail
+                                      # melts in the gauge); signs within N h of each other
+                                      # belong to the same storm                            [ENGINEERING]
+}
+HAIL_SIZE_BANDS_MM = {"edges": [5.0, 15.0, 25.0, 40.0], "higher_is_worse": True}   # max stone diameter  [SCALE]
+
+# Hail-risk day (user's idea 2026-10-26): hail melts fast, so on a risk day the Hail block
+# also checks the day itself (daytime_run_hours below). Decided by the morning run from its own
+# 24 h (no extra API request; 3A has no forecast): "today will be like yesterday".
+# Risk = inside the season AND (a hail sign in the 24 h OR (highest air temperature >= t_max
+# AND highest hourly dew point >= dew_point; no humidity data -> temperature alone)).
+HAIL_RISK = {
+    "season": ("05-01", "09-30"),     # "MM-DD" first and last day of the hail season       [ENGINEERING]
+    "t_max_min_c": 25.0,              # warm air (hourly maximum of the 24 h)                [ENGINEERING]
+    "dew_point_min_c": 14.0,          # moist air (dew point on the hourly mean temperature
+                                      # and humidity, 3A formula): fuel for thunderstorms   [ENGINEERING]
+}
+
+# When the hail block runs, which hours it reads and when the photo is asked for  [PROJECT]
+# Event day = the 24 h ending at run_hour (records after 07:00 of the previous day up to 07:00);
+# storm_window_h more hours before it are fetched (temperature drop, rain near the first hour).
+# Daytime checks (hail-risk days only): at each daytime_run_hours (+5 min) the daytime_window_h
+# hours before it are checked (07-13, 13-19) and a photo is asked for at once. No night check:
+# the camera cannot take a photo in the dark; the night is in the next morning's 24 h.
+HAIL_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+    "photo_hour": 9,                  # photo at this hour of the run day (daylight)
+    "daytime_run_hours": (13, 19),    # extra checks on hail-risk days (local time)
+    "daytime_window_h": 6,            # each daytime check reads the 6 h before it
+}
+
+
+# =============================================================================
+# SNOW (excessive snow)
+# =============================================================================
+# Contract (p.8): snow causing significant MECHANICAL damage (breakage). No depth
+# threshold. Needs a heated rain gauge or a snow-depth sensor.
+# No 3A station has a snow sensor, so the Snow block only flags POSSIBLE snow from the
+# weather data and asks the camera for a photo; the Hail/Snow CV model confirms and
+# grades it (user's choice 2026-10-22). Signals A-D: see detectors/snow.py.
+
+SNOW = {
+    "t_wet_snow_max_c": 1.0,          # precipitation counted as snow at wet-bulb <= this [LITERATURE]
+    "snow_liquid_ratio": 10.0,        # cm of snow per cm of water (wet snow: 5-8)        [ENGINEERING]
+    "foliage_level_bump": 1,          # +levels when leaves/fruit are on the tree         [ENGINEERING]
+    # suspicion signals (camera trigger), values suggested 2026-10-22, to calibrate:
+    "melt_lookahead_h": 12,           # B: rain up to N h after a cold hour = snow melting
+                                      #    in an unheated rain gauge                       [ENGINEERING]
+    "saturated_rh_min_pct": 95.0,     # C: cold hour, no rain, humidity >= this            [ENGINEERING]
+    "covered_solar_max_wm2": 20.0,    # D: midday sun <= this after a suspect hour =
+                                      #    pyranometer under snow (overcast: ~50-200)      [ENGINEERING]
+    "midday_hours": (11, 14),         # D: hourly records ending 11:00 ... 14:00 (local)   [ENGINEERING]
+}
+# Depth bands: kept for the CV model (the Snow block does not grade severity)
+SNOW_DEPTH_BANDS_CM = {"edges": [2.0, 10.0, 25.0, 50.0], "higher_is_worse": True}   # [ENGINEERING]
+
+# When the snow block runs, which hours it reads and when the photo is asked for  [PROJECT]
+# Event day = the 24 h ending at run_hour (records after 07:00 of the previous day up to 07:00);
+# melt_lookahead_h more hours before it are fetched for signal B.
+SNOW_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+    "photo_hour": 9,                  # photo at this hour of the run day (daylight, before melting)
+}
+
+
+# =============================================================================
+# EXCESSIVE RAIN (eccesso di pioggia)
+# =============================================================================
+# Contract (p.8): any ONE of the three criteria below.
+# Excluded: surface runoff, erosion, slope uprooting, rain during flowering (Art.5 q).
+# Apple: rot/splitting only within 20 days before harvest (Art.2); earlier only root asphyxia.
+
+EXCESSIVE_RAIN = {
+    "downpour_1h_mm": 30.0,           # "at least 30 mm over 1 hour"                      [CONTRACT p.8]
+    "downpour_1h_op": ">=",
+    "intense_72h_mm": 80.0,           # "at least 80 mm within 72 hours"                  [CONTRACT p.8]
+    "intense_72h_op": ">=",
+    "prolonged_10d_ratio": 1.5,       # "exceeding the 5-year average by more than 50%"   [CONTRACT p.8]
+    "prolonged_10d_ratio_op": ">",
+    "prolonged_10d_min_mm": 80.0,     # "in no case less than 80 mm"                      [CONTRACT p.8]
+    "prolonged_10d_min_op": ">=",
+    "rot_window_before_harvest_days": 20,                                               # [CONTRACT Art.2 p.13]
+}
+# Severity = rainfall / the trigger of the criterion exceeded the most.
+# Grades only events already detected with the operators above.
+EXCESSIVE_RAIN_BANDS_RATIO = {"edges": [1.0, 1.5, 2.0, 3.0], "higher_is_worse": True}   # [ENGINEERING]
+
+# When the flood (excessive rain) block runs and which hours it reads       [PROJECT]
+# Event day = the 24 h ending at run_hour; the 1 h / 72 h / 10-day criteria are checked
+# for windows ending inside it, so look-back_days of hourly rain are fetched (1 request).
+# 07:00, not 06:00: two jobs in the same hour would make the scheduler skip one.
+FLOOD_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+    "lookback_days": 10,              # longest criterion: prolonged rain over 10 days
+    # claim row of the client report (Heavy Rain Watch): only the rain of the last N days
+    # counts, in 72 h windows (since 2026-10-26); needs N + 3 <= lookback_days to tell
+    # whether rain fell just before the N days
+    "claim_search_days": 4,
+}
+
+
+# =============================================================================
+# HOT TEMPERATURE ("High temperature" box)
+# =============================================================================
+# Contract heat covers for apples (p.8):
+#   - Sunscald: air >= 40 C at 2 m                     -> the trigger used by this block
+#   - Heatwave: t_min > 28 C AND t_max > 40 C on 15 consecutive days, fruit set -> 30 Sep,
+#               30-day start lag (Special Art.5 p.31, Art.6 p.15) -> OUT OF SCOPE
+#   - Hot wind: separate block below
+# Sunburn starts earlier because the sunlit fruit surface is 10-15 C above air, so
+# detection and severity use the estimated fruit surface temperature (FST):
+#     FST = t_air + fst_gain_max_c * (solar / 1000) / (1 + fst_wind_coeff * wind)
+# Damage below 40 C air is real but not insurable under Model 1506.
+
+HOT_TEMPERATURE = {
+    "contract_t_max_c": 40.0,         # Sunscald: "at least 40 C" at 2 m                  [CONTRACT p.8]
+    "contract_t_max_op": ">=",
+    "risk_t_air_c": 32.0,             # sunburn risk rises above 90 F air                 [LITERATURE WSU]
+    "daytime_solar_min_wm2": 300.0,   # only sunlit records count                         [ENGINEERING]
+    "fst_gain_max_c": 15.0,           # FST above air at 1000 W/m2, calm (WSU: 10-15 C)   [LITERATURE]
+    "fst_wind_coeff": 0.3,            # wind cooling, 1/(m/s) - calibrate with fruit probe [ENGINEERING]
+    "browning_duration_min": 60,      # FST held this long for browning classes           [LITERATURE]
+    "necrosis_duration_min": 10,      # FST held this long for necrosis classes           [LITERATURE]
+}
+# Fruit surface temperature: 46 browning (sensitive cultivars), 49 browning (most cultivars),
+# 52 necrosis [LITERATURE Schrader et al. 2003; Racsko & Schrader 2012], 55 extreme [ENGINEERING]
+HOT_TEMPERATURE_FST_BANDS_C = {"edges": [46.0, 49.0, 52.0, 55.0], "higher_is_worse": True}
+
+# When the high temperature block runs and which hours it reads             [PROJECT]
+# Event day = the 24 h ending at run_hour (records after 07:00 of the previous day up to 07:00).
+HIGH_TEMPERATURE_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+}
+
+
+# =============================================================================
+# HOT WIND (scirocco / libeccio)
+# =============================================================================
+# Contract (p.8): warm air masses from SE to SW with >= 40 C at 2 m, causing withering
+# in the 30 days before harvest. Needs a wind-direction sensor.
+
+HOT_WIND = {
+    "t_max_trigger_c": 40.0,          # "at least 40 C" at 2 m                            [CONTRACT p.8]
+    "t_max_trigger_op": ">=",
+    "dir_from_min_deg": 135.0,        # south-east                                        [CONTRACT p.8]
+    "dir_from_max_deg": 225.0,        # south-west                                        [CONTRACT p.8]
+    "wind_min_ms": 1.0,               # 'movement of air masses', no speed in contract    [ENGINEERING]
+    "days_before_harvest": 30,        #                                                   [CONTRACT p.8]
+}
+HOT_WIND_BANDS_C = {"edges": [40.0, 42.0, 44.0, 46.0], "higher_is_worse": True}   # air t_max  [ENGINEERING]
+
+# When the hot wind block runs and which hours it reads                     [PROJECT]
+# Event day = the 24 h ending at run_hour (records after 07:00 of the previous day up to 07:00).
+HOT_WIND_SCHEDULE = {
+    "run_hour": 7,                    # local time; the event day ends here
+}
+
+
+# =============================================================================
+# DROUGHT (siccità)
+# =============================================================================
+# No contract trigger in this file (Model 1506 is not transcribed here for drought), so
+# every value below is a project default to calibrate. The block compares the rain
+# measured by the 3A station over the last complete calendar months with the climate
+# average of the same months (RAIN_NORMALS_MM): a meteorological drought (rain deficit).
+# It does not know about irrigation, so it says "dry period", not "crop damaged".
+
+DROUGHT = {
+    "window_months": 3,               # complete calendar months checked (rain total)     [ENGINEERING]
+    "rain_pct_of_normal_max": 60.0,   # event: rain of the window <= this % of the average [ENGINEERING]
+    "rain_pct_of_normal_op": "<=",
+    "max_missing_days_pct": 20.0,     # more days without rain data than this % -> no_data
+                                      # (a deficit could be only missing data)             [ENGINEERING]
+}
+# Severity = rain of the window as % of its average (lower is worse)       [ENGINEERING]
+DROUGHT_BANDS_PCT = {"edges": [60.0, 45.0, 30.0, 15.0], "higher_is_worse": False}
+
+# When the drought block runs                                               [PROJECT]
+# The pipeline runs every day; drought runs only on this day of the month (the diagram:
+# "Run every month"). python -m damage_engine.main --once drought runs it on any day.
+DROUGHT_SCHEDULE = {
+    "run_day_of_month": 1,
+}
+
+# Climate average of the monthly rain, mm, Jan ... Dec, per 3A station id.  [EXTERNAL]
+# 1991-2020 mean of the monthly totals, CERRA reanalysis (Copernicus European Regional
+# ReAnalysis, 5.5 km grid), read once from Open-Meteo's historical-weather archive
+# (archive-api.open-meteo.com, models=cerra) at each station's coordinates, 2026-10-22.
+# Modelled, not measured: about 15 % above the measured Cuneo average (~990 mm/yr,
+# 2002-2020); ERA5 (31 km grid) was 40-60 % too high and is not used. Stations in the
+# same grid cell share the same values (5 / 12 / 544, 10 / 600, 293 / 566).
+# Replace a row with measured averages (e.g. ARPA Piemonte, or 3A's own history once it
+# has enough years) when available. A station missing here gets "no_data" from Drought.
+RAIN_NORMALS_SOURCE = "CERRA reanalysis 1991-2020 via Open-Meteo (read 2026-10-22)"
+RAIN_NORMALS_MM = {
+    #      Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec
+      3: ( 53.2,  63.3,  82.8, 132.5, 132.1, 110.8,  81.7,  90.2,  95.0,  99.3, 117.0,  63.5),  # Lagnasco, 1121 mm/yr
+      5: ( 53.1,  65.3,  85.2, 139.2, 139.2, 113.5,  77.8,  85.9, 103.6, 110.8, 124.5,  64.1),  # Castellar, 1162
+      6: ( 53.9,  63.1,  82.9, 140.5, 138.3, 104.2,  73.3,  74.6, 101.7, 111.6, 131.2,  63.5),  # Cavour, 1139
+      7: ( 52.6,  66.2,  91.5, 150.9, 163.0, 139.2, 111.2, 110.8, 109.6, 113.3, 122.4,  65.6),  # Costigliole di Saluzzo, 1296
+      9: ( 50.3,  58.3,  77.4, 121.4, 118.6,  97.2,  73.0,  74.7,  84.1,  89.9, 108.9,  60.5),  # Centallo, 1014
+     10: ( 49.3,  59.3,  81.6, 136.7, 141.7, 118.4,  80.2,  84.3, 101.2, 111.2, 120.9,  60.1),  # Revello, 1145
+     12: ( 53.1,  65.3,  85.2, 139.2, 139.2, 113.5,  77.8,  85.9, 103.6, 110.8, 124.5,  64.1),  # Saluzzo, 1162
+     30: ( 56.5,  64.8,  86.2, 132.3, 129.7, 107.2,  84.5,  91.2,  95.2, 100.9, 120.8,  67.3),  # Fossano Maddalene, 1137
+    145: ( 52.0,  63.6,  84.0, 134.7, 133.8, 109.9,  79.4,  82.7,  95.7, 102.7, 117.0,  62.6),  # Falicetto, 1118
+    153: ( 50.6,  58.2,  80.8, 137.0, 149.9, 129.2,  98.5, 108.4,  99.5, 105.4, 112.8,  63.2),  # Caraglio, 1194
+    293: ( 52.7,  61.5,  82.2, 131.0, 127.2, 102.1,  80.5,  74.0,  87.2,  98.3, 113.2,  64.7),  # Cuneo San Benigno, 1075
+    544: ( 53.1,  65.3,  85.2, 139.2, 139.2, 113.5,  77.8,  85.9, 103.6, 110.8, 124.5,  64.1),  # Revello San Pietro, 1162
+    566: ( 52.7,  61.5,  82.2, 131.0, 127.2, 102.1,  80.5,  74.0,  87.2,  98.3, 113.2,  64.7),  # Cuneo Giordano, 1075
+    600: ( 49.3,  59.3,  81.6, 136.7, 141.7, 118.4,  80.2,  84.3, 101.2, 111.2, 120.9,  60.1),  # Cascina Frati, 1145
+}
+
+
+# =============================================================================
+# EXCESS, LIMITS (apples)
+# =============================================================================
+
+# Policy package of the insured farm                                        [CONTRACT Art.7 p.16]
+#   "A"           -> one 30% ABSOLUTE excess for every peril (EXCESS_PCT is ignored)
+#   "B", "C", "F" -> fixed excess per peril (EXCESS_PCT) + combined excess (COMBINED_EXCESS)
+POLICY_PACKAGE = "B"
+PACKAGE_A_ABSOLUTE_EXCESS_PCT = 30
+
+# Optional higher hail excess chosen by the insured (Packages B/C/F): None, 15, 20 or 30.
+# If set above the table value, the strong-wind excess is raised to the same value.
+# With 30%, combined damage with other perils stays fixed at 30%.        [CONTRACT Art.7(c) p.18]
+OPTIONAL_HAIL_EXCESS_PCT = None
+OPTIONAL_HAIL_EXCESS_CHOICES = (15, 20, 30)
+
+# Fixed excess for a single event (Packages B/C/F, before any optional raise) [CONTRACT Art.7(a) p.17]
+EXCESS_PCT = {
+    "hail": 15,
+    "strong_wind": 15,
+    "other": 30,              # brina, gelo, excessive rain, snow, hot temperature, hot wind
+}
+
+# Combined events                                                           [CONTRACT Art.7(b) p.18]
+COMBINED_EXCESS = {
+    "hail_plus_wind": "strong_wind excess of the product",
+    "frost_plus_any_pct": 30,                 # brina/gelo + any other event on fruit
+    "hail_wind_plus_other_fixed_pct": 30,     # if total <= 30% or hail/wind < 10%
+    "hail_wind_plus_other_total_above_pct": 30,
+    "hail_wind_plus_other_min_hw_pct": 10,
+    "hail_wind_plus_other_reduction_per_pt": 1,   # -1 pt of excess per pt of hail/wind above 30%
+    "hail_wind_plus_other_floor_pct": 20,
+}
+
+# Indemnity limit (% of insured value, None = no cap) and scoperto (%)      [CONTRACT Art.7 p.19]
+# Strong wind: apples fall under "All other products 0" -> read as no cap (verify).
+# Combined damage (except hail+wind): limits of the predominant peril; tie -> most severe.
+INDEMNITY_LIMITS = {
+    "hail":        {"limit_pct": 80,   "scoperto_pct": 10},
+    "strong_wind": {"limit_pct": None, "scoperto_pct": 10},
+    "frost":       {"limit_pct": 50,   "scoperto_pct": 10},   # brina, gelo
+    "other":       {"limit_pct": 60,   "scoperto_pct": 10},
+}
+
+
+# =============================================================================
+# APPLE DAMAGE CLASSES (targets for the damage models)       [CONTRACT Special Art.6 p.32]
+# =============================================================================
+# A fruit is in the first class whose limits it respects. Quality loss applies to the
+# produce remaining after the quantity loss. Class a allows no necrotic bruise
+# (0.05 cm2 necrotic limit is for stone fruit only).
+
+APPLE_QUALITY_CLASSES = {
+    "a": {"elongated_max_cm": 2.0, "defect_max_cm2": 1.0, "bruise_max_cm2": 0.20,
+          "bruise_necrotic_max_cm2": 0.0, "flesh_depth_max_mm": 0.0,
+          "quantity_loss_pct": 0, "quality_loss_pct": 0},
+    "b": {"elongated_max_cm": 4.0, "defect_max_cm2": 2.5, "bruise_max_cm2": 0.75,
+          "bruise_necrotic_max_cm2": 0.25, "flesh_depth_max_mm": 3.0,
+          "quantity_loss_pct": 0, "quality_loss_pct": 40},
+    "c": {"description": "worse than b, industrial processing only",
+          "quantity_loss_pct": 0, "quality_loss_pct": 90},
+    "d": {"description": "lost, destroyed or rotten",
+          "quantity_loss_pct": 100, "quality_loss_pct": 0},
+}
+
+
+# =============================================================================
+# REPORT
+# =============================================================================
+
+REPORT = {
+    "history_runs": 10,               # past runs kept per block                          [PROJECT]
+}
+
+
+# =============================================================================
+# INDEX: everything for one phenomenon, by name
+# =============================================================================
+
+PHENOMENA = {
+    "brina":           {"params": BRINA,           "bands": FROST_KILL_BANDS},
+    "gelo":            {"params": GELO,            "bands": FROST_KILL_BANDS},
+    "strong_wind":     {"params": STRONG_WIND,     "bands": STRONG_WIND_BANDS_MS},
+    "hail":            {"params": HAIL,            "bands": HAIL_SIZE_BANDS_MM},
+    "snow":            {"params": SNOW,            "bands": SNOW_DEPTH_BANDS_CM},
+    "excessive_rain":  {"params": EXCESSIVE_RAIN,  "bands": EXCESSIVE_RAIN_BANDS_RATIO},
+    "flood":           {"params": EXCESSIVE_RAIN,  "bands": EXCESSIVE_RAIN_BANDS_RATIO},   # "Flood" box = excessive rain
+    "hot_temperature": {"params": HOT_TEMPERATURE, "bands": HOT_TEMPERATURE_FST_BANDS_C},
+    "hot_wind":        {"params": HOT_WIND,        "bands": HOT_WIND_BANDS_C},
+    "drought":         {"params": DROUGHT,         "bands": DROUGHT_BANDS_PCT},
+}
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_brina.py b/tests/test_brina.py
new file mode 100644
index 0000000..b1ec1c4
--- /dev/null
+++ b/tests/test_brina.py
@@ -0,0 +1,149 @@
+from datetime import date, datetime
+
+import pandas as pd
+
+from damage_engine.detectors import brina
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2025, 3, 12)
+
+
+def _night(station, temps, rh, wind=None, solar=None):
+    ts = pd.date_range("2025-03-11 18:00", periods=len(temps), freq="h")
+    df = pd.DataFrame({"CodiceStazione": station, "Timestamp": ts,
+                       brina.COL_TEMP: temps, brina.COL_RH: rh, brina.COL_RH_MAX: rh})
+    if wind is not None:
+        df[brina.COL_WIND] = wind
+    if solar is not None:
+        df[brina.COL_SOLAR] = solar
+    return df
+
+
+def _field(station, bbch=None, fid=None):
+    return Field(id=fid or f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+COLD_NIGHT = [3, 2, 1, 0.5, -0.5, -1.5, -2.5, -3, -2, -1, 0.5, 2, 3]
+
+
+def test_night_window():
+    assert brina.night_window(RUN_DAY) == (datetime(2025, 3, 11, 18), datetime(2025, 3, 12, 6))
+
+
+def test_cold_calm_humid_night_is_brina_fallback_severity():
+    df = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13)
+    [a] = brina.detect(df, [_field(3)], RUN_DAY, {3: "Lagnasco"})
+    assert a.status == EVENT and a.block == "brina"
+    assert a.field_id == "ST3" and a.station_name == "Lagnasco"
+    assert a.duration_h == 6                            # not the 0.5 °C hours: wet-bulb +0.22 °C (3A formula)
+    assert a.details["min_bud_temp_c"] == -5          # -3 °C air - 2 °C radiative offset
+    assert a.severity == Severity.SEVERE                # fallback bands: -4 >= x > -6
+    assert a.details["severity_method"] == "fallback_min_bud_temp"
+    assert a.details["in_cover_window"] is None         # stage unknown
+
+
+def test_severity_uses_field_stage_when_known():
+    df = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13)
+    bloom, dormant = brina.detect(df, [_field(3, 65, "A"), _field(3, 40, "B")], RUN_DAY)
+    assert bloom.details["stage"] == "full_bloom"
+    assert bloom.details["kill_fraction"] > 0.9
+    assert bloom.severity == Severity.EXTREME
+    assert bloom.details["in_cover_window"] is True
+    assert dormant.status == EVENT and dormant.severity == Severity.NONE
+
+
+def test_dry_or_daytime_night_is_not_brina():
+    temps = [-3] * 13
+    dry = _night(5, temps, [40] * 13)
+    sunny = _night(293, temps, [95] * 13, [0.5] * 13, [500.0] * 13)
+    out = brina.detect(pd.concat([dry, sunny]), [_field(5), _field(293)], RUN_DAY)
+    assert [a.status for a in out] == [NO_EVENT] * 2
+    assert all(a.severity == Severity.NONE for a in out)
+
+
+def test_windy_sub_zero_night_hour_is_brina_without_ice_test():
+    windy_humid = _night(3, [-3] * 13, [95] * 13, [5.0] * 13)    # no ice deposit at bud = air
+    windy_dry = _night(6, [-3] * 13, [40] * 13, [5.0] * 13)
+    windy_zero = _night(10, [0.0] * 13, [40] * 13, [5.0] * 13)   # contract: "below 0 °C"
+    windy_mild = _night(30, [1.5] * 13, [40] * 13, [5.0] * 13)   # air <= 2 °C, wet-bulb <= 0, not sub-zero
+    calm_humid = _night(544, [-3] * 13, [95] * 13, [0.5] * 13)   # calm: ice test, buds 2 °C colder
+    calm_dry = _night(566, [-3] * 13, [40] * 13, [0.5] * 13)     # calm and dry: no ice, no brina
+    out = brina.detect(pd.concat([windy_humid, windy_dry, windy_zero, windy_mild, calm_humid, calm_dry]),
+                       [_field(i) for i in (3, 6, 10, 30, 544, 566)], RUN_DAY)
+    assert [a.status for a in out] == [EVENT, EVENT, NO_EVENT, NO_EVENT, EVENT, NO_EVENT]
+    hum, dry, calm = out[0], out[1], out[4]
+    assert hum.duration_h == 13 and hum.details["min_bud_temp_c"] == -3      # bud = air when windy
+    assert hum.details["windy_hours"] == 13 and hum.details["max_wind_ms"] == 5.0
+    assert dry.severity == hum.severity == Severity.MODERATE                  # fallback: -3 °C
+    assert calm.details["min_bud_temp_c"] == -5 and calm.details["windy_hours"] == 0
+    rows = brina.brina_hours(windy_mild)
+    assert (rows["wet_bulb_c"] <= 0).all()                                    # only "below 0 °C" stops it
+
+
+def test_station_without_wind_sensor_still_evaluated():
+    [a] = brina.detect(_night(9, [-5] * 13, [92] * 13), [_field(9)], RUN_DAY, {9: "Centallo"})
+    assert a.status == EVENT
+    assert a.details["wind_checked"] is False
+    assert a.severity == Severity.EXTREME               # bud -7 °C < -6 °C
+
+
+def test_field_without_station_records_is_no_data():
+    [a] = brina.detect(_night(3, COLD_NIGHT, [95] * 13), [_field(77)], RUN_DAY)
+    assert a.status == NO_DATA
+
+
+def test_out_of_cover_window_flagged():
+    df = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13)
+    [a] = brina.detect(df, [_field(3, 65)], date(2025, 10, 19))
+    assert a.details["in_cover_window"] is False
+
+
+def test_missing_sensors_and_data_are_noted():
+    df = _night(9, [-5] * 13, [92] * 13)                       # no wind, no solar columns
+    [a] = brina.detect(df, [_field(9)], RUN_DAY, station_sensors={9: {"TemperaturaAria", "UmiditaRelativa"}})
+    assert any(n.startswith("No wind sensor at this station") for n in a.data_notes)
+    assert any(n.startswith("No solar radiation sensor at this station") for n in a.data_notes)
+
+
+def test_partly_missing_hours_are_noted():
+    solar = [0.0] * 10 + [None] * 3
+    rh = [95] * 12 + [None]
+    df = _night(293, COLD_NIGHT, rh, [0.5] * 13, solar)
+    [a] = brina.detect(df, [_field(293)], RUN_DAY)
+    assert "Solar radiation missing for 3 of 13 hours" in " ".join(a.data_notes)
+    assert "Temperature or humidity missing for 1 of 13 hours" in " ".join(a.data_notes)
+
+
+def test_complete_station_has_no_notes_and_no_records_is_noted():
+    full = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13, [0.0] * 13)
+    ok, missing = brina.detect(full, [_field(3), _field(77)], RUN_DAY)
+    assert ok.data_notes == []
+    assert missing.data_notes == ["No records from the station last night."]
+
+
+def test_3a_wet_bulb_is_reported_but_not_used_to_detect():
+    df = _night(566, COLD_NIGHT, [95] * 13, [0.5] * 13, [1.0] * 13)
+    df[brina.COL_WET_BULB_3A] = [5.0] * 13                  # warm on purpose: must not block the event
+    [a] = brina.detect(df, [_field(566)], RUN_DAY)
+    assert a.status == EVENT and a.duration_h == 6          # same as without TBulbMed
+    assert a.details["min_wet_bulb_3a_c"] == 5.0
+    [b] = brina.detect(_night(3, COLD_NIGHT, [95] * 13, [0.5] * 13), [_field(3)], RUN_DAY)
+    assert "min_wet_bulb_3a_c" not in b.details             # station without TBulbMed
+
+
+def test_solar_column_is_hourly_irradiance():
+    assert brina.COL_SOLAR == "RadiazioneSolareMax"         # W/m²; RadiazioneSolareInt is MJ/m², daily only
+
+
+def test_wet_bulb_is_the_shared_lowest_of_the_hour():
+    df = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13)
+    rows = brina.brina_hours(df)
+    assert rows["wet_bulb_c"].equals(rows["WetBulbMin"])     # one value for the whole pipeline
+
+
+def test_missing_max_humidity_uses_mean_and_is_noted():
+    df = _night(3, COLD_NIGHT, [95] * 13, [0.5] * 13).drop(columns=[brina.COL_RH_MAX])
+    [a] = brina.detect(df, [_field(3)], RUN_DAY)
+    assert a.duration_h == 6                                 # RH max = RH mean here: same result
+    assert "Hourly maximum humidity missing for 13 of 13 hours" in " ".join(a.data_notes)
diff --git a/tests/test_derived.py b/tests/test_derived.py
new file mode 100644
index 0000000..8ea9539
--- /dev/null
+++ b/tests/test_derived.py
@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from damage_engine.sources import derived
+
+# real hours of station 566 (2026-10-19): hourly mean T (°C), mean RH (%), 3A's TBulbMed (°C)
+STATION_566 = [(13.3, 99, 13.2), (13.8, 90, 12.8), (15.6, 82, 13.8),
+               (15.8, 78, 13.6), (15.9, 76, 13.4), (15.2, 78, 13.0)]
+
+
+@pytest.mark.parametrize("t, rh, tbulb_3a", STATION_566)
+def test_formula_reproduces_3a_tbulbmed(t, rh, tbulb_3a):
+    # same formula as 3A (document section 4): equal to 3A's value at its 0.1 °C rounding
+    assert abs(float(derived.wet_bulb_c(t, rh)) - tbulb_3a) <= 0.05
+
+
+def test_saturated_air_wet_bulb_equals_air_temperature():
+    for t in (-5.0, 0.0, 10.0):
+        assert abs(float(derived.wet_bulb_c(t, 100)) - t) < 1e-6
+
+
+def test_wet_bulb_below_air_when_dry_and_ice_branch_below_zero():
+    assert float(derived.wet_bulb_c(20, 50)) < 20
+    assert float(derived.wet_bulb_c(-3, 80)) < -3
+    # T <= 0 uses the ice constants: the result is continuous enough across 0 °C
+    assert abs(float(derived.wet_bulb_c(0.0, 90)) - float(derived.wet_bulb_c(0.01, 90))) < 0.2
+
+
+def test_dew_point_at_saturation_is_air_temperature():
+    assert abs(float(derived.dew_point_c(12.0, 100)) - 12.0) < 1e-6
+
+
+def test_add_wet_bulb_pairs_tmin_rhmax_and_tmax_rhmin():
+    df = pd.DataFrame({derived.T_MIN: [14.6], derived.T_MEAN: [15.6], derived.T_MAX: [17.5],
+                       derived.RH_MIN: [70.0], derived.RH_MEAN: [82.0], derived.RH_MAX: [90.0]})
+    out = derived.add_wet_bulb(df)
+    assert out[derived.WET_BULB_MIN][0] == round(float(derived.wet_bulb_c(14.6, 90)), 2)
+    assert out[derived.WET_BULB_MAX][0] == round(float(derived.wet_bulb_c(17.5, 70)), 2)
+    assert out[derived.WET_BULB_MIN][0] <= out[derived.WET_BULB_MAX][0]
+    assert derived.WET_BULB_MIN not in df                    # input not changed
+
+
+def test_add_wet_bulb_falls_back_to_means_and_never_fails():
+    df = pd.DataFrame({derived.T_MEAN: [5.0, None], derived.RH_MEAN: [90.0, 80.0]})
+    out = derived.add_wet_bulb(df)                           # no min/max columns at all
+    expected = round(float(derived.wet_bulb_c(5.0, 90)), 2)
+    assert out[derived.WET_BULB_MIN][0] == expected == out[derived.WET_BULB_MAX][0]
+    assert np.isnan(out[derived.WET_BULB_MIN][1])            # no temperature -> NaN
+    assert derived.add_wet_bulb(pd.DataFrame({"x": [1]}))[derived.WET_BULB_MIN].isna().all()
+
+
+def test_frost_point_below_zero_uses_ice_constants():
+    assert abs(float(derived.dew_point_c(0, 100))) < 1e-9
+    assert float(derived.dew_point_c(-3, 40)) < -10
+    # T <= 0 °C: e = RH * 6.1 * 10^(9.5 T / (T + 265.5)), inverted with the same constants
+    e = 0.95 * 6.1 * 10 ** (9.5 * -3 / (-3 + 265.5))
+    x = np.log10(e / 6.1)
+    assert abs(float(derived.dew_point_c(-3, 95)) - 265.5 * x / (9.5 - x)) < 1e-9
diff --git a/tests/test_drought.py b/tests/test_drought.py
new file mode 100644
index 0000000..f16dee6
--- /dev/null
+++ b/tests/test_drought.py
@@ -0,0 +1,141 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from damage_engine.detectors import drought
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources.fields import Field
+from damage_engine.thresholds import RAIN_NORMALS_MM
+
+RUN_DAY = date(2026, 11, 1)                       # checks Aug, Sep, Oct 2026 (92 days)
+DAYS = pd.date_range("2026-08-01", "2026-10-31", freq="D")
+NORMAL_566 = sum(RAIN_NORMALS_MM[566][7:10])      # 74.0 + 87.2 + 98.3 = 259.5 mm
+ALL = {"Pioggia", "TemperaturaAria", "UmiditaRelativa"}
+SOIL = ALL | {"Suolo1", "Suolo2"}
+
+
+def _daily(station, rain_total, et0=2.0, drop=None, days=DAYS):
+    """Daily rows with rain_total spread evenly; `drop` = indexes of days without any row."""
+    rain = [rain_total / len(days)] * len(days)
+    df = pd.DataFrame({"CodiceStazione": station, "Data": days, "Pioggia": rain,
+                       "EvapoPot": [et0] * len(days)})
+    return df.drop(index=list(drop)) if drop else df
+
+
+def _one(df, station=566, sensors=ALL, soil=None, soil_error=None, day=RUN_DAY):
+    f = Field(id=f"ST{station}", name=f"field {station}", station_id=station)
+    [a] = drought.detect(df, [f], day, station_sensors={station: sensors}, soil=soil,
+                         soil_error=soil_error)
+    return a
+
+
+def test_schedule_window_and_labels():
+    assert drought.due(date(2026, 11, 1)) and not drought.due(date(2026, 11, 2))
+    assert drought.window_months(RUN_DAY) == [date(2026, 8, 1), date(2026, 9, 1), date(2026, 10, 1)]
+    assert drought.window_months(date(2026, 10, 22)) == [date(2026, 7, 1), date(2026, 8, 1),
+                                                         date(2026, 9, 1)]
+    assert drought.window(RUN_DAY) == (datetime(2026, 8, 1), datetime(2026, 11, 1))
+    assert drought.months_label(drought.window_months(RUN_DAY)) == "Aug–Oct 2026"
+    assert drought.months_label(drought.window_months(date(2026, 1, 1))) == "Oct–Dec 2025"
+    assert drought.months_label(drought.window_months(date(2026, 2, 1))) == "Nov 2025–Jan 2026"
+    assert drought.normals_for(566, drought.window_months(RUN_DAY)) == [74.0, 87.2, 98.3]
+    assert drought.normals_for(999, drought.window_months(RUN_DAY)) is None
+
+
+def test_usual_rain_is_no_drought():
+    a = _one(_daily(566, NORMAL_566))
+    assert a.status == NO_EVENT and a.severity == Severity.NONE
+    d = a.details
+    assert (d["rain_mm"], d["normal_mm"], d["rain_pct_of_normal"]) == (259.5, 259.5, 100.0)
+    assert [m["label"] for m in d["months"]] == ["Aug 2026", "Sep 2026", "Oct 2026"]
+    assert [m["normal_mm"] for m in d["months"]] == [74.0, 87.2, 98.3]
+    assert d["window_label"] == "Aug–Oct 2026" and d["window_start"] == "2026-08-01T00:00"
+    assert "no drought" in a.summary and "100 % of the average" in a.summary
+    assert a.start is None and a.duration_h == 0
+
+
+def test_dry_months_are_a_drought_graded_on_percent_of_average():
+    a = _one(_daily(566, 50.0))                  # 19.3 % of 259.5 mm
+    assert a.status == EVENT and a.severity == Severity.SEVERE      # <= 30 %, > 15 %
+    assert 0.75 <= a.severity_score < 1.0
+    d = a.details
+    assert d["rain_pct_of_normal"] == 19.3 and d["deficit_mm"] == 209.5
+    assert (a.start, a.end) == (datetime(2026, 8, 1), datetime(2026, 11, 1))
+    assert a.duration_h == 92 * 24
+    assert a.summary.startswith("Dry period: 50 mm of rain in Aug–Oct 2026, only 19.3 %")
+    assert "Oct 2026: 17.1 % of its average" in a.summary     # 16.85 mm of 98.3 mm
+
+
+@pytest.mark.parametrize("rain, status, severity", [
+    (156.0, NO_EVENT, Severity.NONE),           # 60.1 %
+    (155.0, EVENT, Severity.LIGHT),             # 59.7 %
+    (100.0, EVENT, Severity.MODERATE),          # 38.5 %
+    (30.0, EVENT, Severity.EXTREME),            # 11.6 %
+])
+def test_trigger_and_bands(rain, status, severity):
+    a = _one(_daily(566, rain))
+    assert (a.status, a.severity) == (status, severity)
+
+
+def test_one_dry_month_alone_is_not_enough():
+    rain = [0.0] * 61 + [9.0] * 31               # Aug, Sep dry; Oct 279 mm
+    df = pd.DataFrame({"CodiceStazione": 566, "Data": DAYS, "Pioggia": rain, "EvapoPot": 2.0})
+    a = _one(df)
+    assert a.status == NO_EVENT and a.details["rain_pct_of_normal"] == 107.5
+    assert [m["rain_pct_of_normal"] for m in a.details["months"]] == [0.0, 0.0, 283.8]
+
+
+def test_water_balance_from_et0():
+    d = _one(_daily(566, 92.0, et0=3.0)).details
+    assert (d["et0_mm"], d["water_balance_mm"], d["water_balance_months"]) == (276.0, -184.0, 3)
+    assert d["months"][0]["water_balance_mm"] == -62.0
+
+
+def test_missing_days_are_a_lower_bound_until_too_many():
+    a = _one(_daily(566, 92.0, drop=range(10)))    # 10 of 92 days missing (10.9 %)
+    assert a.status == EVENT and a.details["missing_days"] == 10 and a.details["rain_mm"] == 82.0
+    assert any("Rain missing for 10 of 92 days (Aug 2026 10)" in n for n in a.data_notes)
+    b = _one(_daily(566, 92.0, drop=range(20)))    # 21.7 % > 20 %
+    assert b.status == NO_DATA and b.details["reason"] == "too many days without rain data"
+    assert "could be only missing data" in b.data_notes[0] and b.summary == b.data_notes[0]
+
+
+def test_no_records_or_no_average_is_no_data():
+    a = _one(pd.DataFrame())
+    assert a.status == NO_DATA and "No daily rain data for Aug–Oct 2026" in a.data_notes[0]
+    b = _one(_daily(999, 50.0), station=999)
+    assert b.status == NO_DATA and "No average rain for station 999" in b.data_notes[0]
+
+
+def test_missing_et0_column_and_no_soil_sensor_are_notes():
+    df = _daily(566, 259.5).drop(columns=["EvapoPot"])
+    a = _one(df)
+    assert a.status == NO_EVENT and a.details["water_balance_mm"] is None
+    assert any("No evapotranspiration data" in n for n in a.data_notes)
+    assert any("No soil moisture sensor" in n for n in a.data_notes)
+    assert any("CERRA" in n for n in a.data_notes)          # where the average comes from
+
+
+def _soil(station, s1, s2=None):
+    hours = pd.date_range("2026-10-01 01:00", "2026-11-01 00:00", freq="h")
+    first = hours <= pd.Timestamp("2026-10-08 00:00")
+    v1 = np.where(first, s1[0], s1[1])
+    df = pd.DataFrame({"CodiceStazione": station, "Timestamp": hours, "Suolo1": v1})
+    if s2 is not None:
+        df["Suolo2"] = s2
+    return df
+
+
+def test_soil_moisture_of_the_last_month_is_information_only():
+    a = _one(_daily(600, 50.0), station=600, sensors=SOIL, soil=_soil(600, (30.0, 18.0), 25.0))
+    soil = a.details["soil_moisture"]
+    assert soil["Suolo1"]["first_week_pct"] == 30.0 and soil["Suolo1"]["last_week_pct"] == 18.0
+    assert soil["Suolo2"]["mean_pct"] == 25.0
+    assert a.status == EVENT                                 # decided by the rain only
+    b = _one(_daily(600, 50.0), station=600, sensors=SOIL, soil_error="ConnectionError")
+    assert b.details["soil_moisture"] is None and b.status == EVENT
+    assert any("could not be read" in n for n in b.data_notes)
+    c = _one(_daily(600, 50.0), station=600, sensors=SOIL)
+    assert any("No soil moisture data" in n for n in c.data_notes)
diff --git a/tests/test_flood.py b/tests/test_flood.py
new file mode 100644
index 0000000..f44fe08
--- /dev/null
+++ b/tests/test_flood.py
@@ -0,0 +1,189 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.detectors import flood
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2025, 6, 12)
+HOURS = 24 * flood.LOOKBACK_DAYS        # 240 hourly records in the fetch window
+
+
+def _rain(station=3, rain=None):
+    """Hourly rain over the whole fetch window; `rain` = 240 values (default dry)."""
+    start, end = flood.fetch_window(RUN_DAY)
+    ts = pd.date_range(start, end, freq="h")
+    values = np.zeros(len(ts)) if rain is None else np.asarray(rain, dtype=float)
+    return pd.DataFrame({"CodiceStazione": station, "Timestamp": ts, flood.COL_RAIN: values})
+
+
+def _with_day(day_values, before=0.0):
+    """240 values: `before` mm/h in the 9 days before, then the 24 event-day values."""
+    return [before] * (HOURS - 24) + list(day_values)
+
+
+def _field(station=3, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _one(df, bbch=None, **kw):
+    [a] = flood.detect(df, [_field(bbch=bbch)], RUN_DAY, {3: "Lagnasco"}, **kw)
+    return a
+
+
+def test_windows():
+    assert flood.event_window(RUN_DAY) == (datetime(2025, 6, 11, 7), datetime(2025, 6, 12, 7))
+    start, end = flood.fetch_window(RUN_DAY)
+    assert (start, end) == (datetime(2025, 6, 2, 8), datetime(2025, 6, 12, 7))
+    assert len(_rain()) == HOURS
+
+
+def test_downpour_in_one_hour():
+    day = [0.0] * 24
+    day[10] = 35.0
+    a = _one(_rain(rain=_with_day(day)))
+    assert a.status == EVENT and a.details["criteria_met"] == ["downpour_1h"]
+    assert a.severity == Severity.LIGHT and a.duration_h == 1
+    assert a.start == datetime(2025, 6, 11, 18)
+
+
+def test_contract_operator_at_least_30mm():
+    day = [0.0] * 24
+    day[0] = 30.0
+    assert _one(_rain(rain=_with_day(day))).status == EVENT
+    day[0] = 29.9
+    assert _one(_rain(rain=_with_day(day))).status == NO_EVENT
+
+
+def test_severity_grows_with_ratio():
+    day = [0.0] * 24
+    day[5] = 95.0                                    # 95 / 30 = 3.2 -> extreme
+    a = _one(_rain(rain=_with_day(day)))
+    assert a.severity == Severity.EXTREME and a.severity_score == 1.0
+
+
+def test_intense_rain_over_72_hours():
+    rain = [0.0] * (HOURS - 72) + [1.2] * 72           # 86.4 mm in 72 h, no downpour
+    a = _one(_rain(rain=rain))
+    assert a.status == EVENT and a.details["criteria_met"] == ["intense_72h"]
+    assert a.details["max_72h_mm"] == 86.4 and a.severity == Severity.LIGHT
+
+
+def test_dry_day_after_heavy_rain_is_not_repeated():
+    rain = [0.0] * (HOURS - 96) + [1.2] * 72 + [0.0] * 24
+    a = _one(_rain(rain=rain))
+    assert a.status == NO_EVENT and a.details["max_72h_mm"] is None
+    assert a.details["rain_72h_to_run_mm"] > 0
+
+
+def test_light_rain_is_no_event():
+    a = _one(_rain(rain=_with_day([0.5] * 24)))
+    assert a.status == NO_EVENT and "12.0 mm in the last 24 h" in a.summary
+
+
+def test_prolonged_rain_only_with_reference_average():
+    rain = [0.375] * HOURS                             # 90 mm in 10 days, 27 mm in 72 h
+    a = _one(_rain(rain=rain))
+    assert a.status == NO_EVENT
+    assert any("No 5-year reference average" in n for n in a.data_notes)
+    a = _one(_rain(rain=rain), reference_10d_mm={3: 50.0})   # 90 > 1.5 x 50 and >= 80
+    assert a.status == EVENT and a.details["criteria_met"] == ["prolonged_10d"]
+    assert a.details["rain_trigger_ratio"] == round(90 / 80, 3)  # trigger = max(75, 80)
+    assert not any("reference average" in n for n in a.data_notes)
+    a = _one(_rain(rain=rain), reference_10d_mm={3: 70.0})   # 90 < 1.5 x 70
+    assert a.status == NO_EVENT
+
+
+def test_missing_hours_are_noted_and_counted_as_zero():
+    rain = _with_day([0.0] * 24)
+    rain[3], rain[HOURS - 1] = np.nan, np.nan
+    a = _one(_rain(rain=rain).drop(index=10))          # a missing row too
+    assert a.status == NO_EVENT
+    assert any("1 of 24 hours of the event day" in n for n in a.data_notes)
+    assert any("2 of 216 hours" in n for n in a.data_notes)
+
+
+def test_no_records_is_no_data():
+    a = _one(_rain(station=5))
+    assert a.status == NO_DATA and a.data_notes
+    rain = _with_day([np.nan] * 24, before=0.1)
+    a = _one(_rain(rain=rain))
+    assert a.status == NO_DATA and "No rain data for the event day" in a.data_notes[0]
+
+
+def _at(i):
+    """Timestamp (ISO, minutes) of record i of the fetch window (record 0 = 2025-06-02 08:00)."""
+    start, _ = flood.fetch_window(RUN_DAY)
+    return (start + i * flood.HOUR).isoformat(timespec="minutes")
+
+
+def test_last_heavy_72h_found_days_later_for_claims():
+    rain = [0.0] * HOURS
+    rain[150], rain[160], rain[180] = 30.0, 30.0, 30.0   # 90 mm in 31 h, 2-4 days ago
+    a = _one(_rain(rain=rain))
+    assert a.status == NO_EVENT                         # the event day is dry: no new event
+    heavy = a.details["last_heavy_72h"]
+    assert heavy == {"mm": 90.0, "first_rain": _at(150), "last_rain": _at(180),
+                     "reached_at": _at(180), "partial": False}
+    assert heavy["first_rain"] == "2025-06-08T14:00" and heavy["last_rain"] == "2025-06-09T20:00"
+
+
+def test_last_heavy_72h_is_the_latest_not_the_wettest():
+    rain = [0.0] * HOURS
+    rain[150], rain[230] = 100.0, 85.0                 # both end inside the last 4 days
+    d = _one(_rain(rain=rain)).details
+    assert d["last_heavy_72h"]["mm"] == 85.0 and d["last_heavy_72h"]["reached_at"] == _at(230)
+    assert d["wettest_72h"] == {"mm": 100.0, "first_rain": _at(150), "last_rain": _at(150),
+                                "partial": False}
+
+
+def test_no_heavy_72h_gives_the_wettest_window():
+    d = _one(_rain(rain=_with_day([0.5] * 24))).details
+    assert d["last_heavy_72h"] is None
+    assert d["wettest_72h"]["mm"] == 12.0 and d["wettest_72h"]["first_rain"] == _at(HOURS - 24)
+    d = _one(_rain(rain=_with_day([0.0] * 24))).details
+    assert d["last_heavy_72h"] is None and d["wettest_72h"] is None    # no rain at all
+
+
+def test_heavy_rain_watch_counts_only_the_rain_of_the_last_4_days():
+    first = HOURS - 24 * flood.CLAIM_SEARCH_DAYS        # first record of the watch period
+    assert flood.CLAIM_SEARCH_DAYS == 4 and first == 144 and _at(first) == "2025-06-08T08:00"
+    rain = [0.0] * HOURS
+    rain[first - 1] = 85.0                               # 07:00 four days ago: just before the watch
+    d = _one(_rain(rain=rain)).details
+    assert d["last_heavy_72h"] is None and d["wettest_72h"] is None
+    rain[first] = 85.0                                   # first hour of the watch
+    heavy = _one(_rain(rain=rain)).details["last_heavy_72h"]
+    assert heavy["mm"] == 85.0 and heavy["first_rain"] == _at(first)   # the earlier 85 mm left out
+    assert heavy["reached_at"] == _at(first) and heavy["partial"] is True   # rain just before it
+    rain[first - 1] = 0.0
+    assert _one(_rain(rain=rain)).details["last_heavy_72h"]["partial"] is False
+
+
+def test_heavy_rain_watch_storm_partly_before_the_4_days():
+    rain = [0.0] * HOURS
+    for i in range(110, 150):                            # 2 mm/h, 40 h; 6 h inside the watch
+        rain[i] = 2.0
+    d = _one(_rain(rain=rain)).details
+    assert d["last_heavy_72h"] is None                   # 80 mm in all, only 12 mm counted
+    assert d["wettest_72h"] == {"mm": 12.0, "first_rain": _at(144), "last_rain": _at(149),
+                                "partial": True}
+
+
+def test_last_heavy_72h_uses_the_contract_operator_at_least_80mm():
+    rain = [0.0] * (HOURS - 90) + [1.1] * 70 + [3.0] + [0.0] * 19    # 80 mm (float noise)
+    assert _one(_rain(rain=rain)).details["last_heavy_72h"]["mm"] == 80.0
+    rain[HOURS - 20] = 2.9                                         # 79.9 mm
+    assert _one(_rain(rain=rain)).details["last_heavy_72h"] is None
+
+
+def test_cover_window_starts_at_fruit_set():
+    assert flood.in_cover_window(RUN_DAY, 65) is False     # flowering: excluded
+    assert flood.in_cover_window(RUN_DAY, 72) is True
+    assert flood.in_cover_window(RUN_DAY, None) is None
+    assert flood.in_cover_window(date(2025, 11, 2), 85) is False
+    day = [0.0] * 24
+    day[0] = 40.0
+    assert _one(_rain(rain=_with_day(day)), bbch=72).details["in_cover_window"] is True
diff --git a/tests/test_freezing.py b/tests/test_freezing.py
new file mode 100644
index 0000000..2bffc18
--- /dev/null
+++ b/tests/test_freezing.py
@@ -0,0 +1,154 @@
+from datetime import date, datetime
+
+import pandas as pd
+
+from damage_engine.detectors import brina, freezing
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2025, 3, 12)
+# the 24 h checked for RUN_DAY: index 0-10 = day hours 07:00-17:00 of 11 March,
+# index 11-23 = Brina's night window 18:00 (11 March) -> 06:00 (12 March)
+HOURS = pd.date_range("2025-03-11 07:00", "2025-03-12 06:00", freq="h")
+DAY, NIGHT = 11, 13
+WARM_NIGHT = [3.0] * NIGHT
+COLD_DAY = [1, 0.5, 0, -0.5, -1.5, -2.5, -1, 0, 1, 2, 3] + WARM_NIGHT   # 4 h below 0 °C, 10:00-13:00
+
+
+def _day(station, temps, rh=80.0, wind=None, solar=None, ts=None):
+    ts = HOURS[:len(temps)] if ts is None else ts
+    df = pd.DataFrame({"CodiceStazione": station, "Timestamp": ts, freezing.COL_TEMP: temps,
+                       freezing.COL_RH: rh, freezing.COL_RH_MAX: rh})
+    if wind is not None:
+        df[freezing.COL_WIND] = wind
+    if solar is not None:
+        df[freezing.COL_SOLAR] = solar
+    return df
+
+
+def _field(station, bbch=None, fid=None):
+    return Field(id=fid or f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def test_windows_match_brina():
+    assert freezing.day_window(RUN_DAY) == (datetime(2025, 3, 11, 6), datetime(2025, 3, 12, 6))
+    b_start, b_end = brina.night_window(RUN_DAY)
+    assert freezing.night_start(RUN_DAY) == b_start and freezing.day_window(RUN_DAY)[1] == b_end
+
+
+def test_cold_day_is_freezing_fallback_severity():
+    [a] = freezing.detect(_day(3, COLD_DAY), [_field(3)], RUN_DAY, {3: "Lagnasco"})
+    assert a.status == EVENT and a.block == "freezing" and a.station_name == "Lagnasco"
+    assert a.duration_h == 4                                   # 0 °C hours do not count ("below 0 °C")
+    assert a.start == datetime(2025, 3, 11, 10) and a.end == datetime(2025, 3, 11, 13)
+    assert a.details["day_min_temp_c"] == -2.5
+    assert a.severity == Severity.MODERATE                     # fallback: -2 > -2.5 >= -4
+    assert a.details["severity_method"] == "fallback_min_bud_temp"
+    assert a.details["in_cover_window"] is None                # stage unknown
+
+
+def test_exactly_zero_is_not_freezing():
+    [a] = freezing.detect(_day(3, [0.0] * 24), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.severity == Severity.NONE
+
+
+def test_sub_zero_night_is_left_to_brina():
+    [a] = freezing.detect(_day(3, [5.0] * DAY + [-3.0] * NIGHT), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT
+    assert a.details["day_min_temp_c"] == 5.0
+    assert a.details["sunlit_hours_in_night_window"] == 0
+
+
+def test_sunlit_hours_of_the_night_window_are_freezing():
+    solar = [400.0] * DAY + [200, 50, 5] + [0.0] * 9 + [30]    # 18:00, 19:00 and 06:00 sunlit
+    temps = [5.0] * DAY + [-1.0] * NIGHT
+    [a] = freezing.detect(_day(566, temps, solar=solar), [_field(566)], RUN_DAY)
+    assert a.status == EVENT and a.duration_h == 3
+    assert a.details["sunlit_hours_in_night_window"] == 3 and a.details["daylight_hours"] == 14
+    assert a.start == datetime(2025, 3, 11, 18) and a.end == datetime(2025, 3, 12, 6)
+
+
+def test_every_hour_goes_to_brina_or_freezing_never_both():
+    solar = [400.0] * DAY + [200, 50, 5] + [0.0] * 9 + [30]
+    for rh, wind in ((95.0, 0.5), (40.0, 5.0)):           # brina all night: calm + humid, windy + dry
+        df = _day(566, [-3.0] * 24, rh=rh, wind=[wind] * 24, solar=solar)
+        frz = freezing.day_hours(df, RUN_DAY)
+        frz_hours = set(frz.loc[frz["freezing"], "Timestamp"])
+        night = df[df["Timestamp"] >= brina.night_window(RUN_DAY)[0]]
+        br = brina.brina_hours(night)
+        brina_hours_ = set(br.loc[br["brina"], "Timestamp"])
+        assert not frz_hours & brina_hours_
+        assert frz_hours | brina_hours_ == set(HOURS)
+
+
+def test_severity_uses_field_stage_when_known():
+    bloom, dormant = freezing.detect(_day(3, COLD_DAY), [_field(3, 65, "A"), _field(3, 40, "B")], RUN_DAY)
+    assert bloom.details["stage"] == "full_bloom"
+    assert 0.10 < bloom.details["kill_fraction"] < 0.50          # -2.5 °C at full bloom
+    assert bloom.severity == Severity.MODERATE
+    assert bloom.details["in_cover_window"] is True
+    assert dormant.status == EVENT and dormant.severity == Severity.NONE
+
+
+def test_out_of_cover_window_flagged():
+    df = _day(3, COLD_DAY, ts=pd.date_range("2025-10-18 07:00", periods=24, freq="h"))
+    [a] = freezing.detect(df, [_field(3, 65)], date(2025, 10, 19))
+    assert a.status == EVENT and a.details["in_cover_window"] is False
+
+
+def test_wind_and_wet_bulb_are_reported_not_used_to_detect():
+    windy = _day(3, COLD_DAY, wind=[3.0] * 24)
+    calm = _day(6, COLD_DAY, wind=[0.5] * 24)
+    no_wind = _day(9, COLD_DAY)
+    out = freezing.detect(pd.concat([windy, calm, no_wind]), [_field(3), _field(6), _field(9)], RUN_DAY)
+    assert [a.status for a in out] == [EVENT] * 3 and len({a.duration_h for a in out}) == 1
+    assert [a.details["advective_wind"] for a in out] == [True, False, None]
+    assert out[0].details["mean_wind_ms"] == 3.0 and out[2].details["mean_wind_ms"] is None
+    rows = freezing.day_hours(windy, RUN_DAY)
+    assert rows["wet_bulb_c"].equals(rows[derived.WET_BULB_MIN])   # the shared value
+    assert out[0].details["min_wet_bulb_c"] == round(float(rows.loc[rows["freezing"], "wet_bulb_c"].min()), 2)
+
+
+def test_rows_outside_the_24_hours_are_ignored():
+    df = _day(3, [5.0] * 24)
+    extra = _day(3, [-10.0, -10.0], ts=pd.to_datetime(["2025-03-11 06:00", "2025-03-12 07:00"]))
+    [a] = freezing.detect(pd.concat([df, extra]), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["records"] == DAY
+
+
+def test_no_records_or_no_daylight_temperature_is_no_data():
+    night_only = _day(3, [5.0] * NIGHT, ts=HOURS[DAY:])
+    missing, no_day = freezing.detect(night_only, [_field(77), _field(3)], RUN_DAY)
+    assert missing.status == NO_DATA
+    assert missing.data_notes == ["No records from the station for yesterday's daylight hours."]
+    assert no_day.status == NO_DATA
+    assert "Air temperature missing for 11 of 11 daylight hours" in " ".join(no_day.data_notes)
+
+
+def test_missing_sensors_are_noted():
+    [a] = freezing.detect(_day(9, COLD_DAY), [_field(9)], RUN_DAY,
+                          station_sensors={9: {"TemperaturaAria", "UmiditaRelativa"}})
+    assert a.status == EVENT
+    assert any(n.startswith("No wind sensor at this station") for n in a.data_notes)
+    assert any(n.startswith("No solar radiation sensor at this station: daylight = the fixed day "
+                            "hours (records 07:00-17:00)") for n in a.data_notes)
+
+
+def test_partly_missing_hours_are_noted():
+    solar = [400.0] * DAY + [0.0] * 10 + [None] * 3
+    df = _day(566, COLD_DAY, wind=[1.0] * 24, solar=solar).drop(index=[0, 1])   # no 07:00, 08:00 rows
+    df.loc[2, freezing.COL_TEMP] = None
+    df[freezing.COL_TEMP_FALLBACK] = 0.0
+    [a] = freezing.detect(df, [_field(566)], RUN_DAY)
+    text = " ".join(a.data_notes)
+    assert "Solar radiation missing for 3 of 13 hours of the night window" in text
+    assert "Air temperature missing for 2 of 11 daylight hours" in text      # the two missing rows
+    assert "Hourly minimum temperature missing for 1 of 9 daylight hours: hourly mean used" in text
+
+
+def test_complete_station_has_no_notes():
+    solar = [400.0] * DAY + [0.0] * NIGHT
+    [a] = freezing.detect(_day(566, COLD_DAY, wind=[1.0] * 24, solar=solar), [_field(566)], RUN_DAY)
+    assert a.status == EVENT and a.data_notes == []
+    assert a.details["solar_checked"] is True
diff --git a/tests/test_hail.py b/tests/test_hail.py
new file mode 100644
index 0000000..d1fad86
--- /dev/null
+++ b/tests/test_hail.py
@@ -0,0 +1,290 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.detectors import hail
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity, camera_request
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2026, 7, 10)
+# fetched for RUN_DAY: 1 look-back record (07:00 of 9 Jul), then the 24 h checked
+# (08:00 of 9 Jul ... 07:00 of 10 Jul)
+HOURS = pd.date_range("2026-07-09 07:00", "2026-07-10 07:00", freq="h")
+N = len(HOURS)                             # 25
+LB = 1                                     # index of the first record of the 24 h (08:00)
+ALL = {"TemperaturaAria", "UmiditaRelativa", "Pioggia", "VelocitaVento2m"}
+NO_WIND = {"TemperaturaAria", "UmiditaRelativa", "Pioggia"}
+
+
+def _day(station=3, t=None, rain=None, wind=None, wind_mean=None, rate=None, t_min=None, rh=None):
+    """Hourly rows: mean temperature t (min = t - 0.5, max = t + 0.5 unless t_min given), rain,
+    wind, humidity rh (default 50 %)."""
+    t = [22.0] * N if t is None else t
+    nan = [np.nan] * N
+    return pd.DataFrame({
+        "CodiceStazione": station, "Timestamp": HOURS,
+        hail.COL_T_MEAN: t,
+        hail.COL_T_MIN: [x - 0.5 for x in t] if t_min is None else t_min,
+        hail.COL_T_MAX: [x + 0.5 for x in t],
+        hail.COL_RH_MEAN: [50.0] * N if rh is None else rh,
+        hail.COL_RAIN: [0.0] * N if rain is None else rain,
+        hail.COL_RATE: nan if rate is None else rate,
+        hail.COL_WIND: [3.0] * N if wind is None else wind,
+        hail.COL_WIND_MEAN: [1.5] * N if wind_mean is None else wind_mean,
+    })
+
+
+def _set(base, at, values):
+    out = list(base)
+    out[at:at + len(values)] = values
+    return out
+
+
+def _field(station=3, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _one(df, sensors=ALL, bbch=None, day=RUN_DAY):
+    [a] = hail.detect(df, [_field(3, bbch)], day, station_sensors={3: sensors})
+    return a
+
+
+ZERO = [0.0] * N
+H16 = LB + 8                               # record ending 16:00 of 9 Jul
+
+
+def test_window_photo_time_lookback_and_rate():
+    assert hail.RUN_HOUR == 7 and hail.LOOKBACK_H == 1 and hail.STORM_H == 1
+    assert hail.day_window(RUN_DAY) == (datetime(2026, 7, 9, 7), datetime(2026, 7, 10, 7))
+    assert hail.photo_time(RUN_DAY) == datetime(2026, 7, 10, 9)
+    assert hail.RATE_MMH == 30.0                       # 5 mm in 10 min
+
+
+def test_dry_calm_day_no_hail():
+    a = _one(_day())
+    assert a.status == NO_EVENT and a.severity == Severity.NONE
+    assert a.details["signals"] == [] and a.details["suspicion"] is None
+    assert a.details["records"] == 24 and "camera_request" not in a.details
+    assert a.summary == "No hail suspected: no rain in the 24 hours."
+    assert a.data_notes == []
+
+
+def test_burst_rain_alone_is_possible_and_asks_for_a_photo():
+    a = _one(_day(rain=_set(ZERO, H16, [20.0])))       # exactly 20 mm counts (>=)
+    assert a.status == EVENT and a.severity == Severity.NONE and a.severity_score == 0.0
+    d = a.details
+    assert d["signals"] == ["A"] and d["suspicion"] == "possible" and d["storm_signs"] == 1
+    assert (d["max_rain_1h_mm"], d["max_rain_1h_at"]) == (20.0, "2026-07-09T16:00")
+    assert (a.duration_h, a.start, a.end) == (1, datetime(2026, 7, 9, 16), datetime(2026, 7, 9, 16))
+    req = camera_request(d)
+    assert req == {"phenomenon": "hail", "station_id": 3, "photo_at": "2026-07-10T09:00",
+                   "level": "possible", "signals": ["A"], "reason": "Hail possible: burst rain."}
+    assert a.summary.startswith("Hail possible: burst rain of 20 mm in one hour (09 Jul 16:00)")
+    assert "Photo requested for 10 Jul 09:00" in a.summary
+
+
+def test_rain_below_burst_no_sign():
+    a = _one(_day(rain=_set(ZERO, H16, [19.9])))
+    assert a.status == NO_EVENT
+    assert a.summary.startswith("No hail suspected: wettest hour 19.9 mm (sign at ≥ 20 mm)")
+
+
+def test_all_three_signs_in_one_storm_are_likely():
+    t = _set([28.0] * N, H16 + 1, [21.0, 20.0])         # 17:00 min 20.5 after 16:00 mean 28
+    rain = _set(ZERO, H16 + 1, [25.0, 3.0])
+    wind = _set([3.0] * N, H16, [15.0])                 # gust at 16:00, rain starts at 17:00
+    a = _one(_day(t=t, rain=rain, wind=wind))
+    d = a.details
+    assert a.status == EVENT and d["suspicion"] == "likely"
+    assert d["signals"] == ["A", "B", "C"] and d["storm_signs"] == 3
+    assert (d["max_t_drop_c"], d["max_t_drop_at"]) == (7.5, "2026-07-09T17:00")
+    assert (d["max_gust_ms"], d["max_gust_at"]) == (15.0, "2026-07-09T16:00")
+    assert (a.start, a.end, a.duration_h) == (datetime(2026, 7, 9, 16), datetime(2026, 7, 9, 17), 2)
+    assert camera_request(d)["reason"] == ("Hail likely: burst rain; sudden temperature drop near "
+                                           "rain; strong gust near rain.")
+
+
+def test_drop_and_gust_without_rain_nearby_do_not_count():
+    t = _set([28.0] * N, H16, [22.0])                   # 6 °C drop, dry
+    wind = _set([3.0] * N, H16 + 6, [16.0])             # gust, dry
+    rain = _set(ZERO, H16 + 3, [2.0])                   # rain 3 h after the drop
+    a = _one(_day(t=t, rain=rain, wind=wind))
+    d = a.details
+    assert a.status == NO_EVENT
+    assert (d["drop_hours"], d["drop_dry_hours"]) == (0, 1)
+    assert (d["gust_hours"], d["gust_dry_hours"]) == (0, 1)
+
+
+def test_drop_with_light_rain_next_hour_is_possible():
+    t = _set([28.0] * N, H16, [22.0])
+    a = _one(_day(t=t, rain=_set(ZERO, H16 + 1, [0.4])))
+    d = a.details
+    assert a.status == EVENT and d["signals"] == ["B"] and d["suspicion"] == "possible"
+    assert "temperature drop of 6.5 °C near rain" in a.summary
+
+
+def test_two_signs_hours_apart_are_two_storms_possible():
+    rain = _set(ZERO, H16, [22.0])
+    rain = _set(rain, H16 + 4, [1.0])
+    wind = _set([3.0] * N, H16 + 4, [14.0])              # 4 h later: another storm
+    a = _one(_day(rain=rain, wind=wind))
+    d = a.details
+    assert d["signals"] == ["A", "C"] and d["storm_signs"] == 1 and d["suspicion"] == "possible"
+
+
+def test_rain_rate_at_293_gives_sign_a():
+    rain = _set(ZERO, H16, [8.0])
+    rate = _set([0.0] * N, H16, [36.0])
+    [a] = hail.detect(_day(293, rain=rain, rate=rate), [_field(293)], RUN_DAY)
+    d = a.details
+    assert a.status == EVENT and d["signals"] == ["A"] and d["rate_checked"]
+    assert d["max_rain_rate_mmh"] == 36.0
+    assert "rain rate up to 36 mm/h" in a.summary
+
+
+def test_drop_in_first_hour_uses_the_lookback_record():
+    t = _set([28.0] * N, LB, [22.0])                    # 08:00 of 9 Jul, after 07:00 mean 28
+    a = _one(_day(t=t, rain=_set(ZERO, LB, [1.0])))
+    assert a.details["signals"] == ["B"] and a.start == datetime(2026, 7, 9, 8)
+
+
+def test_no_wind_sensor_note_and_gust_not_checked():
+    nan = [np.nan] * N
+    a = _one(_day(rain=_set(ZERO, H16, [21.0]), wind=nan, wind_mean=nan), sensors=NO_WIND)
+    assert a.details["wind_checked"] is False and a.details["max_gust_ms"] is None
+    assert any("No wind sensor (VelocitaVento2m)" in n for n in a.data_notes)
+
+
+def test_wind_max_missing_uses_hourly_mean():
+    wind = _set([3.0] * N, H16, [np.nan])
+    wind_mean = _set([1.5] * N, H16, [14.5])
+    a = _one(_day(rain=_set(ZERO, H16, [1.0]), wind=wind, wind_mean=wind_mean))
+    assert a.details["signals"] == ["C"] and a.details["wind_fallback_hours"] == 1
+    assert any("Hourly maximum wind missing for 1 hour(s)" in n for n in a.data_notes)
+
+
+def test_no_rain_data_counts_drop_as_if_it_rained():
+    t = _set([28.0] * N, H16, [22.0])
+    a = _one(_day(t=t, rain=[np.nan] * N))
+    assert a.status == EVENT and a.details["signals"] == ["B"]
+    assert a.details["rain_checked"] is False and a.details["rain_day_mm"] is None
+    assert any(n.startswith("No rain data in the 24 hours") for n in a.data_notes)
+
+
+def test_missing_temperature_hours_noted():
+    t = _set([25.0] * N, H16, [np.nan])
+    a = _one(_day(t=t, t_min=_set([24.5] * N, H16, [np.nan])))
+    # the drop is not known for 16:00 (no value) and 17:00 (no value in the hour before)
+    assert any("Temperature drop not checked for 2 of 24 hours" in n for n in a.data_notes)
+
+
+def test_no_data_and_station_without_records():
+    nan = [np.nan] * N
+    a = _one(_day(t=nan, t_min=nan, rain=nan, wind=nan, wind_mean=nan))
+    assert a.status == NO_DATA
+    assert a.summary.startswith("No rain, temperature or wind data from the station for the last 24 hours.")
+    assert a.details["hail_risk"] is None                   # unknown in the season: checked later
+    [b] = hail.detect(pd.DataFrame(), [_field(6)], RUN_DAY)
+    assert b.status == NO_DATA and b.data_notes == ["No records from the station for the last 24 hours."]
+
+
+def test_cover_window_fruit_set_and_nets_note():
+    storm = _day(rain=_set(ZERO, H16, [25.0]))
+    assert _one(storm, bbch=72).details["in_cover_window"] is True
+    assert _one(storm, bbch=65).details["in_cover_window"] is False      # before fruit set
+    a = _one(storm)
+    assert a.details["in_cover_window"] is None and a.details["net_checked"] is False
+    assert any("under nets the hail cover ended on 25 May" in n for n in a.data_notes)
+    assert hail.in_cover_window(date(2026, 11, 2), 80) is False
+    assert hail.in_cover_window(date(2026, 5, 20), 72) is True
+
+
+def test_nets_note_not_before_25_may():
+    hours = pd.date_range("2026-05-19 07:00", "2026-05-20 07:00", freq="h")
+    df = _day(rain=_set(ZERO, H16, [25.0])).assign(Timestamp=hours)
+    a = _one(df, day=date(2026, 5, 20))
+    assert a.status == EVENT
+    assert not any("nets" in n for n in a.data_notes)
+
+
+# ─────────────────────────── hail-risk day ───────────────────────────
+
+def test_risk_day_warm_and_moist_in_season():
+    t = _set([22.0] * N, LB + 6, [27.0, 28.0])           # afternoon up to 28.5 °C (max)
+    a = _one(_day(t=t, rh=[70.0] * N))                   # dew point ~22 °C at 28 °C, 70 %
+    d = a.details
+    assert a.status == NO_EVENT and d["hail_risk"] is True
+    assert d["max_temp_c"] == 28.5 and d["max_dew_point_c"] >= 14
+    assert d["hail_risk_reason"].startswith("warm, moist air")
+    assert a.summary.endswith("Hail-risk day: the station is checked again today at 13:00 and 19:00.")
+
+
+def test_no_risk_when_dry_cool_or_out_of_season():
+    hot = _set([22.0] * N, LB + 6, [28.0])
+    dry = _one(_day(t=hot, rh=[25.0] * N)).details             # dew point ~6 °C
+    assert dry["hail_risk"] is False and dry["hail_risk_reason"].startswith("warm (28.5 °C) but dry")
+    cool = _one(_day(rh=[90.0] * N)).details                   # 22.5 °C max
+    assert cool["hail_risk"] is False and "< 25 °C" in cool["hail_risk_reason"]
+    hours = pd.date_range("2026-10-25 07:00", "2026-10-26 07:00", freq="h")
+    october = _day(t=hot, rh=[80.0] * N, rain=_set(ZERO, H16, [25.0])).assign(Timestamp=hours)
+    d = _one(october, day=date(2026, 10, 26)).details
+    assert d["hail_risk"] is False and d["hail_risk_reason"] == "outside the hail season"
+    assert not hail.in_season(date(2026, 4, 30)) and hail.in_season(date(2026, 5, 1))
+
+
+def test_risk_day_after_a_hail_sign_and_without_humidity():
+    a = _one(_day(rain=_set(ZERO, H16, [25.0])))
+    assert a.details["hail_risk"] is True
+    assert a.details["hail_risk_reason"] == "a hail sign in the last 24 hours"
+    hot = _set([22.0] * N, LB + 6, [28.0])
+    b = _one(_day(t=hot, rh=[np.nan] * N))
+    assert b.details["hail_risk"] is True and b.details["max_dew_point_c"] is None
+    assert any("hail-risk day was decided on the air temperature alone" in n for n in b.data_notes)
+
+
+# ─────────────────────────── daytime checks ───────────────────────────
+
+DAY_HOURS = pd.date_range("2026-07-10 06:00", "2026-07-10 13:00", freq="h")   # 1 look-back + 6 h
+
+
+def _daytime(rain=None, t=None):
+    n = len(DAY_HOURS)
+    t = [24.0] * n if t is None else t
+    return pd.DataFrame({"CodiceStazione": 3, "Timestamp": DAY_HOURS, hail.COL_T_MEAN: t,
+                         hail.COL_T_MIN: [x - 0.5 for x in t], hail.COL_T_MAX: [x + 0.5 for x in t],
+                         hail.COL_RH_MEAN: [60.0] * n,
+                         hail.COL_RAIN: [0.0] * n if rain is None else rain,
+                         hail.COL_RATE: [np.nan] * n, hail.COL_WIND: [3.0] * n,
+                         hail.COL_WIND_MEAN: [1.5] * n})
+
+
+def test_daytime_windows_and_hours():
+    assert hail.DAYTIME_HOURS == (13, 19) and hail.DAYTIME_H == 6
+    assert hail.daytime_window(RUN_DAY, 13) == (datetime(2026, 7, 10, 7), datetime(2026, 7, 10, 13))
+    assert hail.daytime_window(RUN_DAY, 19) == (datetime(2026, 7, 10, 13), datetime(2026, 7, 10, 19))
+    import pytest
+    with pytest.raises(ValueError):
+        hail.run_daytime(RUN_DAY, 1)                        # no night check
+
+
+def test_daytime_check_asks_for_a_photo_at_once():
+    rain = [0.0] * len(DAY_HOURS)
+    rain[4] = 23.0                                            # record ending 10:00
+    window = hail.daytime_window(RUN_DAY, 13)
+    [a] = hail.detect(_daytime(rain=rain), [_field()], RUN_DAY, station_sensors={3: ALL}, window=window)
+    d = a.details
+    assert a.block == "hail_daytime" and a.status == EVENT and d["check"] == "daytime"
+    assert (d["window_start"], d["window_end"], d["hours"]) == ("2026-07-10T07:00", "2026-07-10T13:00", 6)
+    assert camera_request(d)["photo_at"] == "2026-07-10T13:00"       # at once, not next morning
+    assert "hail_risk" not in d
+    assert "Photo requested at once 10 Jul 13:00" in a.summary
+
+
+def test_daytime_check_quiet_and_no_records():
+    window = hail.daytime_window(RUN_DAY, 13)
+    [a] = hail.detect(_daytime(), [_field()], RUN_DAY, station_sensors={3: ALL}, window=window)
+    assert a.status == NO_EVENT and a.summary == "No hail suspected: no rain in the 6 hours."
+    assert a.details["records"] == 6
+    [b] = hail.detect(pd.DataFrame(), [_field(6)], RUN_DAY, window=window)
+    assert b.status == NO_DATA and b.data_notes == ["No records from the station for the 6 hours."]
diff --git a/tests/test_high_temperature.py b/tests/test_high_temperature.py
new file mode 100644
index 0000000..1f60217
--- /dev/null
+++ b/tests/test_high_temperature.py
@@ -0,0 +1,250 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from damage_engine.detectors import high_temperature as ht
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2026, 7, 20)
+# the 24 h checked for RUN_DAY: records 08:00 of 19 Jul ... 07:00 of 20 Jul
+HOURS = pd.date_range("2026-07-19 08:00", "2026-07-20 07:00", freq="h")
+MILD = [30.0] * 24
+NOON = 6                                   # index of the 14:00 record
+SUN = [800.0] * 11 + [0.0] * 13            # 08:00 ... 18:00 sunny, then night
+BREEZE = [2.0] * 24
+ALL_SENSORS = {"TemperaturaAria", "UmiditaRelativa", "VelocitaVento2m", "RadiazioneSolare"}
+NO_SOLAR = {"TemperaturaAria", "UmiditaRelativa"}
+
+
+def _day(station, t_max, solar=None, wind=None, t_mean=None, rh=None, ts=None):
+    ts = HOURS[:len(t_max)] if ts is None else ts
+    n = len(ts)
+    nan = [np.nan] * n
+    t_mean = ([t - 2 if t is not None and not np.isnan(t) else np.nan for t in t_max]
+              if t_mean is None else t_mean)
+    rh = [30.0] * n if rh is None else rh
+    return pd.DataFrame({
+        "CodiceStazione": station, "Timestamp": ts,
+        derived.T_MAX: t_max, derived.T_MEAN: t_mean,
+        derived.T_MIN: [t - 4 if not np.isnan(t) else np.nan for t in t_mean],
+        derived.RH_MIN: rh, derived.RH_MEAN: rh, derived.RH_MAX: rh,
+        ht.COL_SOLAR: nan if solar is None else solar,
+        ht.COL_WIND: nan if wind is None else wind,
+    })
+
+
+def _field(station, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _with(values, at=NOON, base=MILD):
+    out = list(base)
+    out[at:at + len(values)] = values
+    return out
+
+
+def test_window_is_the_24h_ending_at_07():
+    assert ht.RUN_HOUR == 7
+    assert ht.day_window(RUN_DAY) == (datetime(2026, 7, 19, 7), datetime(2026, 7, 20, 7))
+
+
+def test_fruit_surface_temperature_formula():
+    fst = ht.fruit_surface_c([40.0, 40.0, 40.0, 40.0, 40.0, 40.0],
+                             [1000.0, 1000.0, 299.0, 300.0, np.nan, 1000.0],
+                             [0.0, 2.0, 0.0, 0.0, 0.0, np.nan])
+    assert fst[0] == pytest.approx(55.0)                       # calm, full sun: + 15 °C
+    assert fst[1] == pytest.approx(40 + 15 / 1.6)              # wind cools the fruit
+    assert fst[2] == 40.0                                      # below 300 W/m²: no sun gain
+    assert fst[3] == pytest.approx(44.5)                       # 300 W/m² counts
+    assert np.isnan(fst[4])                                    # no solar value: no FST
+    assert fst[5] == pytest.approx(55.0)                       # no wind value: calm (worst case)
+
+
+def test_event_without_solar_is_graded_on_the_air():
+    # 14:00-16:00: 40.5, 42.3, 41.0 °C hourly maximum, no pyranometer (11 of 14 stations)
+    df = _day(3, _with([40.5, 42.3, 41.0]), wind=BREEZE)
+    [a] = ht.detect(df, [_field(3)], RUN_DAY, {3: "Lagnasco"}, {3: NO_SOLAR | {"VelocitaVento2m"}})
+    assert a.status == EVENT and a.block == "high_temperature" and a.station_name == "Lagnasco"
+    assert a.duration_h == 3
+    assert a.start == datetime(2026, 7, 19, 14) and a.end == datetime(2026, 7, 19, 16)
+    d = a.details
+    assert d["max_temp_c"] == 42.3 and d["max_temp_at"] == "2026-07-19T15:00"
+    assert d["hot_hours"] == 3 and d["max_mean_temp_c"] == 40.3
+    assert d["severity_method"] == "air" and d["severity_value_c"] == 42.3
+    assert d["solar_checked"] is False and d["max_fst_c"] is None
+    assert d["sunburn_risk_hours"] is None
+    assert a.severity == Severity.MODERATE                    # air bands: 42 <= 42.3 < 44
+    assert d["in_cover_window"] is None                       # stage unknown
+    assert a.summary == "High temperature for 3 h, highest air temperature 42.3 °C: severity moderate."
+    assert any(n.startswith("No solar radiation sensor (RadiazioneSolare) at this station") for n in a.data_notes)
+
+
+def test_trigger_is_at_least_40():
+    [hit] = ht.detect(_day(3, _with([40.0])), [_field(3)], RUN_DAY)
+    [miss] = ht.detect(_day(3, _with([39.9])), [_field(3)], RUN_DAY)
+    assert hit.status == EVENT and hit.severity == Severity.LIGHT and hit.duration_h == 1
+    assert miss.status == NO_EVENT and miss.severity == Severity.NONE
+    assert miss.details["max_temp_c"] == 39.9
+    assert miss.summary == "No high temperature: highest air temperature 39.9 °C."
+
+
+@pytest.mark.parametrize("temp, level", [(42.0, Severity.MODERATE), (44.0, Severity.SEVERE),
+                                         (46.0, Severity.EXTREME)])
+def test_air_bands_without_solar(temp, level):
+    [a] = ht.detect(_day(3, _with([temp])), [_field(3)], RUN_DAY)
+    assert a.severity == level
+
+
+def test_event_with_solar_is_graded_on_the_fruit_surface():
+    # 14:00: 41 °C, 900 W/m², wind 1 m/s -> FST = 41 + 13.5 / 1.3 = 51.4 °C
+    solar, wind = _with([900.0], base=SUN), _with([1.0], base=BREEZE)
+    df = _day(566, _with([41.0]), solar=solar, wind=wind)
+    [a] = ht.detect(df, [_field(566)], RUN_DAY, station_sensors={566: ALL_SENSORS})
+    d = a.details
+    assert a.status == EVENT and d["severity_method"] == "fst"
+    assert d["max_fst_c"] == 51.4 and d["max_fst_at"] == "2026-07-19T14:00"
+    assert d["solar_at_max_fst_wm2"] == 900 and d["wind_at_max_fst_ms"] == 1.0
+    assert d["sunlit_hours"] == 11 and d["solar_checked"] is True
+    assert a.severity == Severity.MODERATE                    # 49 <= 51.4 < 52
+    assert "fruit surface about 51.4 °C" in a.summary
+    assert a.data_notes == []
+
+
+def test_fst_below_46_is_an_event_without_severity():
+    # 14:00: 40 °C but a weak sun and wind: FST = 40 + 6 / 1.9 = 43.2 °C
+    solar, wind = _with([400.0], base=[0.0] * 24), _with([3.0], base=BREEZE)
+    [a] = ht.detect(_day(566, _with([40.0]), solar=solar, wind=wind), [_field(566)], RUN_DAY)
+    assert a.status == EVENT and a.severity == Severity.NONE
+    assert a.details["severity_method"] == "fst" and a.details["max_fst_c"] == 43.2
+
+
+def test_severity_uses_the_hottest_fruit_of_the_day():
+    # 13:00: 39 °C in full calm sun (FST 54) comes before the 40 °C hour (cloudy, FST 40)
+    t = _with([39.0, 40.0], at=NOON - 1)
+    solar = _with([1000.0, 100.0], at=NOON - 1, base=SUN)
+    wind = _with([0.0, 2.0], at=NOON - 1, base=BREEZE)
+    [a] = ht.detect(_day(566, t, solar=solar, wind=wind), [_field(566)], RUN_DAY)
+    assert a.status == EVENT and a.duration_h == 1
+    assert a.details["max_fst_c"] == 54.0 and a.severity == Severity.SEVERE
+
+
+def test_sunburn_risk_below_40_is_information_only():
+    # 14:00: 35 °C, 1000 W/m², wind 0.5 -> FST 48.0 °C; 15:00: 31 °C (FST 46) is too cool
+    t = _with([35.0, 31.0])
+    solar = _with([1000.0, 1000.0], base=SUN)
+    wind = _with([0.5, 0.0], base=BREEZE)
+    [a] = ht.detect(_day(566, t, solar=solar, wind=wind), [_field(566)], RUN_DAY)
+    assert a.status == NO_EVENT and a.severity == Severity.NONE
+    assert a.details["sunburn_risk_hours"] == 1 and a.details["max_risk_fst_c"] == 48.0
+    assert a.summary == ("No high temperature: highest air temperature 35 °C. Sunburn risk: fruit "
+                         "surface about 48 °C in 1 hour(s) (below the contract's 40 °C air, "
+                         "not insurable).")
+
+
+def test_no_sunburn_risk_on_a_mild_sunny_day():
+    [a] = ht.detect(_day(566, MILD, solar=SUN, wind=BREEZE), [_field(566)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["sunburn_risk_hours"] == 0
+    assert a.details["max_risk_fst_c"] is None
+
+
+def test_missing_wind_in_sunlit_hours_is_calm():
+    wind = _with([np.nan], base=BREEZE)
+    [a] = ht.detect(_day(566, _with([40.0]), solar=_with([1000.0], base=SUN), wind=wind),
+                    [_field(566)], RUN_DAY, station_sensors={566: ALL_SENSORS})
+    assert a.details["max_fst_c"] == 55.0 and a.severity == Severity.EXTREME
+    assert ("Wind missing for 1 sunlit hour(s): fruit surface temperature computed for calm "
+            "air in those hours (worst case).") in a.data_notes
+
+
+def test_solar_station_without_anemometer_is_calm():
+    [a] = ht.detect(_day(9, _with([40.0]), solar=SUN), [_field(9)], RUN_DAY,
+                    station_sensors={9: NO_SOLAR | {"RadiazioneSolare"}})
+    assert a.details["wind_at_max_fst_ms"] is None
+    assert ("No wind sensor (VelocitaVento2m) at this station: fruit surface temperature "
+            "computed for calm air (worst case).") in a.data_notes
+
+
+def test_missing_solar_hours_are_noted():
+    solar = list(SUN)
+    solar[NOON] = np.nan
+    [a] = ht.detect(_day(566, _with([41.0]), solar=solar, wind=BREEZE), [_field(566)], RUN_DAY,
+                    station_sensors={566: ALL_SENSORS})
+    assert a.status == EVENT and a.details["severity_method"] == "fst"
+    assert any(n.startswith("Solar radiation missing for 1 of 24 hours") for n in a.data_notes)
+
+
+def test_solar_sensor_without_data_uses_the_air():
+    [a] = ht.detect(_day(566, _with([42.0]), wind=BREEZE), [_field(566)], RUN_DAY,
+                    station_sensors={566: ALL_SENSORS})
+    assert a.details["severity_method"] == "air" and a.severity == Severity.MODERATE
+    assert any(n.startswith("No solar radiation data in the 24 hours") for n in a.data_notes)
+
+
+def test_missing_maximum_temperature_uses_the_mean():
+    t_max = _with([np.nan])
+    t_mean = _with([40.2], base=[28.0] * 24)                   # only the mean of 14:00 is known
+    [a] = ht.detect(_day(3, t_max, t_mean=t_mean), [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.details["fallback_hours"] == 1
+    assert any("Hourly maximum temperature missing for 1 of 24 hours: hourly mean used" in n
+               for n in a.data_notes)
+
+
+def test_humidity_and_wet_bulb_of_the_hottest_hour():
+    rh = _with([20.0], base=[50.0] * 24)
+    [a] = ht.detect(_day(3, _with([41.0]), rh=rh), [_field(3)], RUN_DAY)
+    assert a.details["humidity_at_max_temp_pct"] == 20
+    assert a.details["wet_bulb_at_max_temp_c"] == round(float(derived.wet_bulb_c(41.0, 20.0)), 1)
+
+
+def test_missing_humidity_is_noted_only():
+    [a] = ht.detect(_day(3, _with([41.0]), rh=[np.nan] * 24), [_field(3)], RUN_DAY,
+                    station_sensors={3: NO_SOLAR})
+    assert a.status == EVENT and a.details["humidity_at_max_temp_pct"] is None
+    assert a.details["wet_bulb_at_max_temp_c"] is None
+    assert ("No humidity data in the 24 hours: humidity and wet-bulb not reported "
+            "(not used to detect).") in a.data_notes
+
+
+def test_cover_window_fruit_set_to_31_october():
+    df = _day(3, _with([41.0]))
+    [after, before] = ht.detect(df, [_field(3, bbch=75), _field(3, bbch=65)], RUN_DAY)
+    assert after.details["in_cover_window"] is True and after.details["bbch"] == 75
+    assert before.details["in_cover_window"] is False          # still in bloom
+    assert after.severity == before.severity                   # severity does not depend on the stage
+    assert ht.in_cover_window(date(2026, 11, 2), 80) is False
+
+
+def test_hours_outside_the_24h_are_ignored():
+    ts = pd.date_range("2026-07-19 07:00", "2026-07-20 08:00", freq="h")     # 26 records
+    [a] = ht.detect(_day(3, [45.0] + MILD + [45.0], ts=ts), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["records"] == 24
+
+
+def test_missing_hours_are_noted_and_not_checked():
+    df = _day(3, _with([41.0])).drop(index=[0, 1, 2])          # 3 hours with no record
+    [a] = ht.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.details["records"] == 21
+    assert "Air temperature missing for 3 of 24 hours: those hours not checked." in a.data_notes
+
+
+def test_no_temperature_values_is_no_data():
+    df = _day(3, [np.nan] * 24, t_mean=[np.nan] * 24)
+    [a] = ht.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+    assert a.data_notes == ["No air temperature data in the 24 hours checked: high temperature "
+                            "cannot be judged."]
+
+
+def test_missing_columns_do_not_break_the_block():
+    df = pd.DataFrame({"CodiceStazione": 3, "Timestamp": HOURS})          # no columns at all
+    [a] = ht.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+
+
+def test_station_without_records_is_no_data():
+    [a] = ht.detect(pd.DataFrame(), [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA and a.data_notes == ["No records from the station for the last 24 hours."]
diff --git a/tests/test_hot_wind.py b/tests/test_hot_wind.py
new file mode 100644
index 0000000..8564597
--- /dev/null
+++ b/tests/test_hot_wind.py
@@ -0,0 +1,194 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from damage_engine.detectors import hot_wind
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2026, 7, 20)
+# the 24 h checked for RUN_DAY: records 08:00 of 19 Jul ... 07:00 of 20 Jul
+HOURS = pd.date_range("2026-07-19 08:00", "2026-07-20 07:00", freq="h")
+MILD = [30.0] * 24
+BREEZE = [2.5] * 24
+ALL_SENSORS = {"TemperaturaAria", "UmiditaRelativa", "VelocitaVento2m"}
+
+
+def _day(station, t_max, wind=None, t_mean=None, wind_max=None, rh=None, ts=None):
+    ts = HOURS[:len(t_max)] if ts is None else ts
+    n = len(ts)
+    return pd.DataFrame({
+        "CodiceStazione": station, "Timestamp": ts,
+        hot_wind.COL_T_MAX: t_max,
+        hot_wind.COL_T_MEAN: [t - 2 if t is not None and not np.isnan(t) else np.nan for t in t_max]
+        if t_mean is None else t_mean,
+        hot_wind.COL_WIND: BREEZE[:n] if wind is None else wind,
+        hot_wind.COL_WIND_MAX: [np.nan] * n if wind_max is None else wind_max,
+        hot_wind.COL_RH: [25.0] * n if rh is None else rh,
+    })
+
+
+def _field(station, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _with_heat(values, at=6, base=MILD):
+    t = list(base)
+    t[at:at + len(values)] = values
+    return t
+
+
+def test_window_is_the_24h_ending_at_07():
+    assert hot_wind.RUN_HOUR == 7
+    assert hot_wind.day_window(RUN_DAY) == (datetime(2026, 7, 19, 7), datetime(2026, 7, 20, 7))
+
+
+def test_hot_moving_air_is_an_event_graded_on_temperature():
+    # 14:00-16:00: 40.5, 42.3, 41.0 °C hourly maximum, breeze 2.5 m/s
+    df = _day(3, _with_heat([40.5, 42.3, 41.0]))
+    [a] = hot_wind.detect(df, [_field(3)], RUN_DAY, {3: "Lagnasco"}, {3: ALL_SENSORS})
+    assert a.status == EVENT and a.block == "hot_wind" and a.station_name == "Lagnasco"
+    assert a.duration_h == 3
+    assert a.start == datetime(2026, 7, 19, 14) and a.end == datetime(2026, 7, 19, 16)
+    d = a.details
+    assert d["max_temp_c"] == 42.3 and d["max_temp_at"] == "2026-07-19T15:00"
+    assert d["max_hot_wind_temp_c"] == 42.3 and d["hot_wind_hours"] == 3 and d["hot_hours"] == 3
+    assert d["max_mean_temp_c"] == 40.3
+    assert d["wind_checked"] is True and d["wind_assumed_hours"] == 0
+    assert d["wind_at_max_temp_ms"] == 2.5 and d["min_humidity_pct"] == 25
+    assert d["direction_checked"] is False and d["harvest_checked"] is False
+    assert a.severity == Severity.MODERATE                    # 42 <= 42.3 < 44
+    assert d["in_cover_window"] is None                       # stage and harvest unknown
+    assert "42.3 °C" in a.summary
+    assert hot_wind.DIRECTION_NOTE in a.data_notes
+    assert any("30 days before harvest" in n for n in a.data_notes)
+
+
+def test_trigger_is_at_least_40():
+    [hit] = hot_wind.detect(_day(3, _with_heat([40.0])), [_field(3)], RUN_DAY)
+    [miss] = hot_wind.detect(_day(3, _with_heat([39.9])), [_field(3)], RUN_DAY)
+    assert hit.status == EVENT and hit.severity == Severity.LIGHT and hit.duration_h == 1
+    assert miss.status == NO_EVENT and miss.severity == Severity.NONE
+    assert miss.details["max_temp_c"] == 39.9 and miss.details["hot_hours"] == 0
+    assert miss.summary == "No hot wind: highest air temperature 39.9 °C."
+
+
+@pytest.mark.parametrize("temp, level", [(42.0, Severity.MODERATE), (44.0, Severity.SEVERE),
+                                         (46.0, Severity.EXTREME), (48.0, Severity.EXTREME)])
+def test_temperature_bands(temp, level):
+    [a] = hot_wind.detect(_day(3, _with_heat([temp])), [_field(3)], RUN_DAY)
+    assert a.severity == level
+
+
+def test_still_air_is_not_hot_wind():
+    wind = list(BREEZE)
+    wind[6] = 0.6                                              # 14:00: 41 °C, still air
+    [a] = hot_wind.detect(_day(3, _with_heat([41.0]), wind=wind), [_field(3)], RUN_DAY,
+                          station_sensors={3: ALL_SENSORS})
+    assert a.status == NO_EVENT
+    assert a.details["hot_hours"] == 1 and a.details["still_hot_hours"] == 1
+    assert a.summary.endswith("1 hour(s) at 40 °C or more had still air.")
+
+
+def test_wind_exactly_at_the_minimum_counts_as_moving():
+    wind = list(BREEZE)
+    wind[6] = 1.0
+    [a] = hot_wind.detect(_day(3, _with_heat([41.0]), wind=wind), [_field(3)], RUN_DAY)
+    assert a.status == EVENT
+
+
+def test_severity_uses_the_hot_wind_hours_only():
+    wind = list(BREEZE)
+    wind[7] = 0.2                                              # the 45 °C hour had still air
+    [a] = hot_wind.detect(_day(3, _with_heat([41.0, 45.0]), wind=wind), [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.duration_h == 1
+    assert a.details["max_temp_c"] == 45.0 and a.details["max_hot_wind_temp_c"] == 41.0
+    assert a.severity == Severity.LIGHT
+
+
+def test_station_without_anemometer_assumes_moving_air():
+    df = _day(9, _with_heat([41.0]), wind=[np.nan] * 24)
+    [a] = hot_wind.detect(df, [_field(9)], RUN_DAY,
+                          station_sensors={9: {"TemperaturaAria", "UmiditaRelativa"}})
+    assert a.status == EVENT
+    assert a.details["wind_checked"] is False and a.details["wind_assumed_hours"] == 1
+    assert a.details["wind_at_max_temp_ms"] is None
+    assert ("No wind sensor (VelocitaVento2m) at this station: air movement not checked, "
+            "taken as moving (worst case).") in a.data_notes
+
+
+def test_missing_wind_hours_are_taken_as_moving():
+    wind = list(BREEZE)
+    wind[6] = np.nan
+    [a] = hot_wind.detect(_day(3, _with_heat([41.0]), wind=wind), [_field(3)], RUN_DAY,
+                          station_sensors={3: ALL_SENSORS})
+    assert a.status == EVENT and a.details["wind_assumed_hours"] == 1
+    assert "Wind missing for 1 of 24 hours: air taken as moving in those hours (worst case)." in a.data_notes
+
+
+def test_missing_mean_wind_uses_the_maximum():
+    wind, wind_max = list(BREEZE), [np.nan] * 24
+    wind[6], wind_max[6] = np.nan, 0.8                         # even the maximum is still air
+    [a] = hot_wind.detect(_day(3, _with_heat([41.0]), wind=wind, wind_max=wind_max), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["still_hot_hours"] == 1
+    assert "Hourly mean wind missing for 1 of 24 hours: hourly maximum used." in a.data_notes
+
+
+def test_missing_maximum_temperature_uses_the_mean():
+    t_max = _with_heat([np.nan])
+    t_mean = [28.0] * 24
+    t_mean[6] = 40.2                                           # only the mean of 14:00 is known
+    [a] = hot_wind.detect(_day(3, t_max, t_mean=t_mean), [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.details["fallback_hours"] == 1
+    assert any("Hourly maximum temperature missing for 1 of 24 hours: hourly mean used" in n
+               for n in a.data_notes)
+
+
+def test_hours_outside_the_24h_are_ignored():
+    ts = pd.date_range("2026-07-19 07:00", "2026-07-20 08:00", freq="h")     # 26 records
+    t = [45.0] + MILD + [45.0]
+    [a] = hot_wind.detect(_day(3, t, ts=ts, wind=[2.5] * 26, rh=[25.0] * 26), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["records"] == 24
+
+
+def test_cover_window_fruit_set_to_31_october():
+    df = _day(3, _with_heat([41.0]))
+    [after, before] = hot_wind.detect(df, [_field(3, bbch=75), _field(3, bbch=65)], RUN_DAY)
+    assert after.details["in_cover_window"] is None            # fruit set, harvest date unknown
+    assert before.details["in_cover_window"] is False          # still in bloom
+    assert after.severity == before.severity                   # severity does not depend on the stage
+    assert hot_wind.in_cover_window(date(2026, 11, 2), 80) is False
+
+
+def test_missing_humidity_is_noted_only():
+    df = _day(3, _with_heat([41.0]), rh=[np.nan] * 24)
+    [a] = hot_wind.detect(df, [_field(3)], RUN_DAY, station_sensors={3: ALL_SENSORS})
+    assert a.status == EVENT and a.details["min_humidity_pct"] is None
+    assert "No humidity data in the 24 hours: humidity not reported (not used to detect)." in a.data_notes
+
+
+def test_missing_hours_are_noted_and_not_checked():
+    df = _day(3, _with_heat([41.0])).drop(index=[0, 1, 2])      # 3 hours with no record
+    [a] = hot_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.details["records"] == 21
+    assert "Air temperature missing for 3 of 24 hours: those hours not checked." in a.data_notes
+
+
+def test_no_temperature_values_is_no_data():
+    df = _day(3, [np.nan] * 24, t_mean=[np.nan] * 24)
+    [a] = hot_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+    assert a.data_notes == ["No air temperature data in the 24 hours checked: hot wind cannot be judged."]
+
+
+def test_missing_columns_do_not_break_the_block():
+    df = pd.DataFrame({"CodiceStazione": 3, "Timestamp": HOURS})          # no columns at all
+    [a] = hot_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+
+
+def test_station_without_records_is_no_data():
+    [a] = hot_wind.detect(pd.DataFrame(), [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA and a.data_notes == ["No records from the station for the last 24 hours."]
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
new file mode 100644
index 0000000..92aed32
--- /dev/null
+++ b/tests/test_pipeline.py
@@ -0,0 +1,182 @@
+"""The whole pipeline (main.py) offline: blocks are replaced, no API call."""
+
+import json
+import sys
+import types
+from datetime import date, datetime
+
+import pytest
+
+from damage_engine import main
+from damage_engine.detectors import (brina, drought, flood, freezing, hail, high_temperature, hot_wind,
+                                     snow, strong_wind)
+from damage_engine.report import report, station_report
+from damage_engine.schemas import NO_EVENT, Analysis
+
+DAY = date(2026, 10, 20)
+
+
+@pytest.fixture
+def out(tmp_path, monkeypatch):
+    """Send every stored file to tmp_path and use one station (566)."""
+    monkeypatch.setattr(report, "REPORTS_DIR", tmp_path / "reports")
+    monkeypatch.setattr(station_report, "STATION_REPORTS_DIR", tmp_path / "station_reports")
+    monkeypatch.setattr(main, "station_ids", lambda: [566])
+    monkeypatch.setattr(brina, "run", lambda day: [Analysis(
+        block="brina", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"night_min_temp_c": 9.5})])
+    monkeypatch.setattr(freezing, "run", lambda day: [Analysis(
+        block="freezing", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"day_min_temp_c": 12.0})])
+    monkeypatch.setattr(strong_wind, "run", lambda day: [Analysis(
+        block="strong_wind", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"max_wind_ms": 6.2})])
+    monkeypatch.setattr(hot_wind, "run", lambda day: [Analysis(
+        block="hot_wind", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"max_temp_c": 24.0})])
+    monkeypatch.setattr(high_temperature, "run", lambda day: [Analysis(
+        block="high_temperature", run_day=day, field_id="ST566", field_name="field 566",
+        station_id=566, station_name="CuneoGiordano", status=NO_EVENT,
+        details={"max_temp_c": 24.0})])
+    monkeypatch.setattr(snow, "run", lambda day: [Analysis(
+        block="snow", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"min_cold_c": 6.0})])
+    monkeypatch.setattr(hail, "run", lambda day: [Analysis(
+        block="hail", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"max_rain_1h_mm": 0.0})])
+    monkeypatch.setattr(drought, "run", lambda day: [Analysis(
+        block="drought", run_day=day, field_id="ST566", field_name="field 566", station_id=566,
+        station_name="CuneoGiordano", status=NO_EVENT, details={"rain_pct_of_normal": 95.0})])
+
+    def broken(day):
+        raise ConnectionError("3A API not reachable")
+    monkeypatch.setattr(flood, "run", broken)
+    return tmp_path
+
+
+def test_block_without_code_is_not_ready(monkeypatch):
+    # every block of the diagram has code now: a made-up empty detectors/ file stands in
+    monkeypatch.setitem(sys.modules, "damage_engine.detectors.empty_block",
+                        types.ModuleType("damage_engine.detectors.empty_block"))
+    with pytest.raises(main.BlockNotReady, match="no run"):
+        main.load_block("empty_block")
+    with pytest.raises(main.BlockNotReady, match="does not exist"):
+        main.load_block("no_such_block")
+
+
+def test_pipeline_runs_every_block_and_reports_errors(out):
+    summary = main.run_pipeline(DAY)
+    assert list(summary["blocks"]) == [b for b in main.BLOCKS if b != "drought"]
+    assert list(summary["skipped"]) == ["drought"]          # monthly: not due on the 20th
+    assert summary["blocks"]["brina"] is None
+    assert "ConnectionError" in summary["blocks"]["flood"]
+    assert summary["blocks"]["freezing"] is None
+    assert summary["blocks"]["strong_wind"] is None
+    assert summary["blocks"]["hot_wind"] is None
+    assert summary["blocks"]["high_temperature"] is None
+    assert summary["blocks"]["snow"] is None
+    assert summary["blocks"]["hail"] is None
+    assert summary["stations"] == {566: None}
+
+    stored = json.loads((out / "reports" / "flood" / f"{DAY}.json").read_text(encoding="utf-8"))
+    assert "Traceback" in stored["error_detail"]           # full detail kept internally only
+
+    folder = out / "station_reports" / "566"
+    content = json.loads((folder / f"{DAY}.json").read_text(encoding="utf-8"))
+    failed = {e["block"]: e["error"] for e in content["overall"]["errors"]}
+    assert set(failed) == {"flood"}                          # flood crashed
+    assert _drought_status(content) == "not_run"            # skipped, nothing stored
+    assert "error_detail" not in json.dumps(content)        # no traceback for the client
+    assert (folder / f"{DAY}.pdf").read_bytes()[:5] == b"%PDF-"
+
+
+def _drought_status(content):
+    return next(b["status"] for b in content["blocks"] if b["block"] == "drought")
+
+
+def test_once_runs_one_block(out):
+    summary = main.run_pipeline(DAY, ["brina"])
+    assert summary["blocks"] == {"brina": None}
+    summary = main.run_pipeline(DAY, ["drought"])            # --once runs it on any day
+    assert summary["blocks"] == {"drought": None} and summary["skipped"] == {}
+
+
+def test_monthly_block_runs_on_the_first_and_is_shown_afterwards(out):
+    first = date(2026, 11, 1)
+    summary = main.run_pipeline(first)
+    assert summary["blocks"]["drought"] is None and summary["skipped"] == {}
+    later = date(2026, 11, 5)
+    assert "drought" in main.run_pipeline(later)["skipped"]
+    content = json.loads((out / "station_reports" / "566" / f"{later}.json").read_text(encoding="utf-8"))
+    b = next(b for b in content["blocks"] if b["block"] == "drought")
+    assert b["status"] == "no_event" and b["run_day"] == "2026-11-01" and b["stale"]
+
+
+def test_default_run_day_waits_for_the_latest_window():
+    hour = main.pipeline_hour()                                 # 7: Flood's window ends at 07:00
+    assert hour == flood.RUN_HOUR
+    assert main.default_run_day(datetime(2026, 10, 21, hour)) == date(2026, 10, 21)
+    assert main.default_run_day(datetime(2026, 10, 21, hour - 1)) == date(2026, 10, 20)
+
+
+# ─────────────────────────── Hail daytime checks ───────────────────────────
+
+JULY = date(2026, 7, 10)
+
+
+def _hail(station, risk):
+    return Analysis(block="hail", run_day=JULY, field_id=f"ST{station}", field_name=f"field {station}",
+                    station_id=station, station_name=f"S{station}", status=NO_EVENT,
+                    details={"hail_risk": risk})
+
+
+def test_hail_risk_stations_from_the_morning_run(out):
+    assert main.hail_risk_stations(JULY) is None                  # no morning run, in season: all
+    assert main.hail_risk_stations(date(2026, 11, 3)) == set()    # outside the season: none
+    report.submit("hail", JULY, [_hail(3, True), _hail(6, False), _hail(10, None)])
+    assert main.hail_risk_stations(JULY) == {3, 10}               # unknown risk is checked too
+
+
+def test_hail_daytime_check_runs_only_for_risk_stations_and_keeps_the_morning_run(out, monkeypatch):
+    report.submit("hail", JULY, [_hail(566, True), _hail(3, False)])
+    calls = []
+
+    def fake(day, hour, ids):
+        calls.append((day, hour, ids))
+        return [Analysis(block="hail_daytime", run_day=day, field_id="ST566", field_name="field 566",
+                         station_id=566, station_name="CuneoGiordano", status=NO_EVENT,
+                         summary="No hail suspected: no rain in the 6 hours.",
+                         details={"check": "daytime", "window_start": "2026-07-10T07:00",
+                                  "window_end": "2026-07-10T13:00"})]
+    monkeypatch.setattr(hail, "run_daytime", fake)
+    summary = main.run_hail_daytime(JULY, 13)
+    assert calls == [(JULY, 13, {566})] and summary["error"] is None and summary["checked"] == [566]
+    assert [r.get("slot") for r in report.runs("hail")] == [None]            # morning run kept
+    assert [r["slot"] for r in report.day_runs("hail_daytime", JULY)] == ["13h"]
+    content = json.loads((out / "station_reports" / "566" / f"{JULY}.json").read_text(encoding="utf-8"))
+    b = next(b for b in content["blocks"] if b["block"] == "hail")
+    assert b["watch_window"] == {"label": "Daytime checks", "start": "2026-07-10T07:00",
+                                 "end": "2026-07-10T13:00"}
+    assert not any(b["block"] == "hail_daytime" for b in content["blocks"])
+
+
+def test_hail_daytime_check_skipped_without_risk_and_errors_stored(out, monkeypatch):
+    report.submit("hail", JULY, [_hail(566, False)])
+    monkeypatch.setattr(hail, "run_daytime", lambda *a: pytest.fail("must not run"))
+    assert main.run_hail_daytime(JULY, 19)["checked"] == []
+    assert report.day_runs("hail_daytime", JULY) == []                     # nothing stored
+    report.submit("hail", JULY, [_hail(566, True)])
+
+    def broken(*a):
+        raise ConnectionError("3A API not reachable")
+    monkeypatch.setattr(hail, "run_daytime", broken)
+    assert "ConnectionError" in main.run_hail_daytime(JULY, 19)["error"]
+    [run] = report.day_runs("hail_daytime", JULY)
+    assert run["slot"] == "19h" and "ConnectionError" in run["error"]
+
+
+def test_serve_schedule_pipeline_then_daytime_checks():
+    assert main.next_job(datetime(2026, 7, 10, 6, 0)) == (datetime(2026, 7, 10, 7, 5), None)
+    assert main.next_job(datetime(2026, 7, 10, 7, 5)) == (datetime(2026, 7, 10, 13, 5), 13)
+    assert main.next_job(datetime(2026, 7, 10, 14, 0)) == (datetime(2026, 7, 10, 19, 5), 19)
+    assert main.next_job(datetime(2026, 7, 10, 19, 30)) == (datetime(2026, 7, 11, 7, 5), None)
diff --git a/tests/test_report.py b/tests/test_report.py
new file mode 100644
index 0000000..b8ca0ba
--- /dev/null
+++ b/tests/test_report.py
@@ -0,0 +1,106 @@
+from datetime import date, datetime, timedelta
+
+from damage_engine.report import report
+from damage_engine.schemas import EVENT, NO_EVENT, Analysis, Severity
+from damage_engine.severity import grade
+from damage_engine.thresholds import FROST_FALLBACK_BANDS_C, FROST_KILL_BANDS
+
+
+def _analysis(day, field="F1", status=NO_EVENT, severity=Severity.NONE, block="brina"):
+    start = datetime.combine(day, datetime.min.time()) if status == EVENT else None
+    return Analysis(block=block, run_day=day, field_id=field, field_name=f"name {field}",
+                    station_id=3, station_name="Lagnasco", status=status, severity=severity,
+                    severity_score=0.6 if status == EVENT else 0.0,
+                    duration_h=4 if status == EVENT else 0, start=start, end=start,
+                    summary="test", details={"min_bud_temp_c": -3.0})
+
+
+def test_grade_bands():
+    assert grade(0.0, FROST_FALLBACK_BANDS_C)[0] == Severity.NONE       # strict: below 0 °C only
+    sev, score = grade(-3.1, FROST_FALLBACK_BANDS_C)
+    assert sev == Severity.MODERATE and abs(score - 0.6375) < 1e-3
+    assert grade(0.95, FROST_KILL_BANDS) == (Severity.EXTREME, 1.0)
+
+
+def test_analysis_round_trip():
+    a = _analysis(date(2025, 3, 12), status=EVENT, severity=Severity.SEVERE)
+    assert Analysis.from_dict(a.to_dict()) == a
+
+
+def test_submit_writes_block_report_and_keeps_last_runs(tmp_path):
+    first = date(2025, 3, 1)
+    for i in range(report.HISTORY_RUNS + 2):
+        day = first + timedelta(days=i)
+        text = report.submit("brina", day, [_analysis(day)], root=tmp_path)
+    assert "BRINA report" in text
+    kept = report.runs("brina", tmp_path)
+    assert len(kept) == report.HISTORY_RUNS
+    assert kept[0]["run_day"] == first + timedelta(days=report.HISTORY_RUNS + 1)
+    assert len(list((tmp_path / "brina").glob("*.txt"))) == report.HISTORY_RUNS
+
+
+def test_rerun_same_day_replaces_run(tmp_path):
+    day = date(2025, 3, 12)
+    report.submit("brina", day, [_analysis(day)], root=tmp_path)
+    report.submit("brina", day, [_analysis(day, status=EVENT, severity=Severity.LIGHT)], root=tmp_path)
+    [run] = report.runs("brina", tmp_path)
+    assert run["analyses"][0].status == EVENT
+
+
+def test_field_report_combines_blocks_and_past_dates(tmp_path):
+    d1, d2 = date(2025, 3, 11), date(2025, 3, 12)
+    report.submit("brina", d1, [_analysis(d1, status=EVENT, severity=Severity.SEVERE),
+                                _analysis(d1, field="F2")], root=tmp_path)
+    report.submit("brina", d2, [_analysis(d2), _analysis(d2, field="F2")], root=tmp_path)
+    report.submit("hail", d2, [_analysis(d2, block="hail")], root=tmp_path)
+
+    assert report.fields(tmp_path) == {"F1": "name F1", "F2": "name F2"}
+    assert report.field_report_dates("F1", tmp_path) == [d2, d1]
+
+    latest = report.field_report("F1", root=tmp_path)
+    assert "[BRINA] run 2025-03-12" in latest and "[HAIL] run 2025-03-12" in latest
+
+    past = report.field_report("F1", d1, root=tmp_path)
+    assert "[BRINA] run 2025-03-11" in past and "SEVERE" in past
+    assert "[HAIL] no run yet" in past
+
+
+def test_reports_show_data_notes(tmp_path):
+    day = date(2025, 3, 12)
+    a = _analysis(day)
+    a.data_notes = ["No wind sensor at this station: calm test not applied."]
+    text = report.submit("brina", day, [a], root=tmp_path)
+    assert "Missing sensors / data:" in text and "No wind sensor" in text
+    assert "Data:     No wind sensor" in report.field_report("F1", root=tmp_path)
+
+
+def test_fields_report_groups_blocks_under_each_field(tmp_path):
+    day = date(2025, 6, 12)
+    report.submit("flood", day, [_analysis(day, field="F2", block="flood"),
+                                 _analysis(day, field="F1", block="flood")], root=tmp_path)
+    report.submit("brina", day, [_analysis(day, field="F1", status=EVENT, severity=Severity.LIGHT),
+                                 _analysis(day, field="F2")], root=tmp_path)
+    text = (tmp_path / report.FIELDS_REPORT_FILE).read_text(encoding="utf-8")
+    assert text.startswith("REPORT BY FIELD") and "blocks: brina, flood" in text
+    f1, f2 = text.index("FIELD name F1 [F1]"), text.index("FIELD name F2 [F2]")
+    # field F1 first, with its brina and flood sections, then field F2 with its own
+    assert f1 < text.index("[BRINA]", f1) < text.index("[FLOOD]", f1) < f2
+    assert f2 < text.index("[BRINA]", f2) < text.index("[FLOOD]", f2)
+    assert "LIGHT" in text[f1:f2] and "LIGHT" not in text[f2:]
+
+
+def test_slots_keep_every_check_of_a_day_and_history_counts_days(tmp_path):
+    first = date(2025, 7, 1)
+    for i in range(report.HISTORY_RUNS + 1):
+        day = first + timedelta(days=i)
+        for slot in ("13h", "19h"):
+            text = report.submit("hail_daytime", day, [_analysis(day, block="hail_daytime")],
+                                 root=tmp_path, slot=slot)
+    assert "HAIL_DAYTIME report - run day 2025-07-11, check 19h" in text
+    last = first + timedelta(days=report.HISTORY_RUNS)
+    assert [r["slot"] for r in report.day_runs("hail_daytime", last, tmp_path)] == ["13h", "19h"]
+    days = {r["run_day"] for r in report.runs("hail_daytime", tmp_path)}
+    assert len(days) == report.HISTORY_RUNS and first not in days        # 10 days, both slots each
+    assert len(list((tmp_path / "hail_daytime").glob("*.json"))) == 2 * report.HISTORY_RUNS
+    report.submit("hail_daytime", last, [], root=tmp_path, slot="13h")    # same slot: replaced
+    assert [len(r["analyses"]) for r in report.day_runs("hail_daytime", last, tmp_path)] == [0, 1]
diff --git a/tests/test_snow.py b/tests/test_snow.py
new file mode 100644
index 0000000..3a43218
--- /dev/null
+++ b/tests/test_snow.py
@@ -0,0 +1,155 @@
+import json
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+
+from damage_engine.detectors import snow
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity, camera_request
+from damage_engine.sources import derived
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2026, 2, 10)
+# fetched for RUN_DAY: 12 look-back records (20:00 of 8 Feb ... 07:00 of 9 Feb), then the 24 h
+# checked (08:00 of 9 Feb ... 07:00 of 10 Feb)
+HOURS = pd.date_range("2026-02-08 20:00", "2026-02-10 07:00", freq="h")
+LB = 12                                    # index of the first record of the 24 h (08:00)
+WARM = [8.0] * 36
+ALL = {"TemperaturaAria", "UmiditaRelativa", "Pioggia", "RadiazioneSolare"}
+NO_SOLAR = {"TemperaturaAria", "UmiditaRelativa", "Pioggia"}
+
+
+def _day(station, t, rh=None, rain=None, solar=None):
+    n = len(t)
+    nan = [np.nan] * n
+    rh = [70.0] * n if rh is None else rh
+    return pd.DataFrame({
+        "CodiceStazione": station, "Timestamp": HOURS[:n],
+        derived.T_MIN: t, derived.T_MEAN: [x + 0.5 for x in t], derived.T_MAX: [x + 1 for x in t],
+        derived.RH_MIN: rh, derived.RH_MEAN: rh, derived.RH_MAX: rh,
+        snow.COL_RAIN: [0.0] * n if rain is None else rain,
+        snow.COL_SOLAR: nan if solar is None else solar,
+    })
+
+
+def _field(station, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _set(base, at, values):
+    out = list(base)
+    out[at:at + len(values)] = values
+    return out
+
+
+def _one(df, sensors=NO_SOLAR, bbch=None, day=RUN_DAY):
+    [a] = snow.detect(df, [_field(3, bbch)], day, station_sensors={3: sensors})
+    return a
+
+
+def test_window_photo_time_and_lookback():
+    assert snow.RUN_HOUR == 7 and snow.LOOKBACK_H == 12
+    assert snow.day_window(RUN_DAY) == (datetime(2026, 2, 9, 7), datetime(2026, 2, 10, 7))
+    assert snow.photo_time(RUN_DAY) == datetime(2026, 2, 10, 9)
+
+
+def test_warm_day_no_snow_possible():
+    a = _one(_day(3, WARM, rain=_set([0.0] * 36, LB + 5, [4.0])))
+    assert a.status == NO_EVENT and a.severity == Severity.NONE
+    assert a.details["cold_hours"] == 0 and a.details["signals"] == []
+    assert "camera_request" not in a.details
+    assert a.summary.startswith("No snow possible: lowest wet-bulb")
+
+
+def test_signal_a_cold_precipitation_is_likely_and_asks_for_a_photo():
+    t = _set(WARM, LB + 15, [-1.0, -1.5, -1.0])          # 23:00, 00:00, 01:00
+    rain = _set([0.0] * 36, LB + 15, [1.2, 2.0, 0.8])
+    a = _one(_day(3, t, rh=[90.0] * 36, rain=rain))
+    assert a.status == EVENT and a.severity == Severity.NONE and a.severity_score == 0.0
+    d = a.details
+    assert d["signals"] == ["A"] and d["suspicion"] == "likely"
+    assert d["snow_water_mm"] == 4.0 and d["est_new_snow_cm"] == 4.0     # × 10, mm -> cm
+    assert (a.duration_h, a.start, a.end) == (3, datetime(2026, 2, 9, 23), datetime(2026, 2, 10, 1))
+    req = camera_request(d)
+    assert req == {"phenomenon": "snow", "station_id": 3, "photo_at": "2026-02-10T09:00",
+                   "level": "likely", "signals": ["A"], "reason": req["reason"]}
+    assert "Photo requested for 10 Feb 09:00" in a.summary and "4 cm" in a.summary
+
+
+def test_signal_b_rain_soon_after_cold_hours_only_within_12h():
+    t = _set(WARM, 0, [-2.0] * LB)                      # cold dry night before the window
+    rain = _set([0.0] * 36, LB + 2, [3.0])               # 10:00, 3 h after the last cold hour
+    a = _one(_day(3, t, rain=rain))
+    assert a.status == EVENT and a.details["signals"] == ["B"] and a.details["suspicion"] == "likely"
+    assert a.details["melt_water_mm"] == 3.0 and a.details["est_new_snow_cm"] == 0.0
+    assert a.details["est_new_snow_max_cm"] == 3.0
+    late = _one(_day(3, t, rain=_set([0.0] * 36, LB + 20, [3.0])))   # 04:00: 21 h later
+    assert late.status == NO_EVENT
+
+
+def test_signal_c_only_is_possible():
+    t = _set(WARM, LB + 18, [-0.5] * 4)
+    rh = _set([70.0] * 36, LB + 18, [98.0] * 4)
+    a = _one(_day(3, t, rh=rh))
+    assert a.status == EVENT and a.details["signals"] == ["C"] and a.details["suspicion"] == "possible"
+    assert a.details["saturated_hours"] == 4 and a.duration_h == 4
+
+
+def test_signal_d_raises_c_to_likely_but_never_triggers_alone():
+    t = _set(WARM, LB, [-0.5] * 3)                     # 08:00 ... 10:00 cold and saturated
+    rh = _set([70.0] * 36, LB, [98.0] * 3)
+    dark = _set([0.0] * 36, LB + 3, [10.0] * 4)        # midday records 11:00 ... 14:00
+    a = _one(_day(3, t, rh=rh, solar=dark), sensors=ALL)
+    assert a.details["signals"] == ["C", "D"] and a.details["suspicion"] == "likely"
+    assert a.details["sensor_covered"] is True and a.details["midday_solar_max_wm2"] == 10
+    grey = _one(_day(3, t, rh=rh, solar=_set(dark, LB + 3, [150.0] * 4)), sensors=ALL)
+    assert grey.details["signals"] == ["C"] and grey.details["sensor_covered"] is False
+    dry_cold = _one(_day(3, [-1.0] * 36, solar=dark), sensors=ALL)     # cold, dark, no other sign
+    assert dry_cold.status == NO_EVENT and dry_cold.details["cold_hours"] == 24
+
+
+def test_no_humidity_uses_air_temperature():
+    t = _set(WARM, LB + 15, [0.5])
+    rain = _set([0.0] * 36, LB + 15, [2.0])
+    sensors = {"TemperaturaAria", "Pioggia"}
+    a = _one(_day(3, t, rh=[np.nan] * 36, rain=rain), sensors=sensors)
+    assert a.status == EVENT and a.details["signals"] == ["A"]
+    assert a.details["cold_basis"] == "air" and a.details["air_fallback_hours"] == 24
+    assert any("No humidity sensor" in n for n in a.data_notes)
+    dry = _one(_day(3, [-1.0] * 36, rh=[np.nan] * 36), sensors=sensors)   # C cannot fire
+    assert dry.status == NO_EVENT
+
+
+def test_no_rain_gauge_only_saturated_air_can_flag():
+    rh = _set([70.0] * 36, LB + 18, [98.0] * 2)
+    sensors = {"TemperaturaAria", "UmiditaRelativa"}
+    a = _one(_day(3, [-1.0] * 36, rh=rh, rain=[np.nan] * 36), sensors=sensors)
+    assert a.status == EVENT and a.details["signals"] == ["C"] and a.details["rain_checked"] is False
+    assert any("No rain gauge" in n for n in a.data_notes)
+
+
+def test_missing_temperature_and_missing_station():
+    a = _one(_day(3, [np.nan] * 36))
+    assert a.status == NO_DATA and "No air temperature data" in a.data_notes[0]
+    part = _one(_day(3, _set(WARM, LB, [np.nan] * 4)))
+    assert part.status == NO_EVENT and part.details["records"] == 20
+    assert any("missing for 4 of 24" in n for n in part.data_notes)
+    [none] = snow.detect(_day(3, WARM), [_field(5)], RUN_DAY)
+    assert none.status == NO_DATA and "No records" in none.data_notes[0]
+
+
+def test_solar_note_and_cover_window():
+    a = _one(_day(3, WARM))
+    assert any("No solar radiation sensor" in n for n in a.data_notes)
+    assert snow.in_cover_window(date(2026, 11, 15), 60) is False
+    assert snow.in_cover_window(date(2026, 4, 1), None) is None
+    assert snow.in_cover_window(date(2026, 4, 1), 40) is False          # dormant
+    assert snow.in_cover_window(date(2026, 4, 1), 60) is True
+
+
+def test_event_stores_and_reloads_as_json():
+    t = _set(WARM, LB + 15, [-1.0])
+    a = _one(_day(3, t, rain=_set([0.0] * 36, LB + 15, [1.0])), bbch=55)
+    assert a.details["in_cover_window"] is True and a.details["bbch"] == 55
+    back = Analysis.from_dict(json.loads(json.dumps(a.to_dict())))
+    assert camera_request(back.details)["photo_at"] == "2026-02-10T09:00"
diff --git a/tests/test_station_report.py b/tests/test_station_report.py
new file mode 100644
index 0000000..eb0dfee
--- /dev/null
+++ b/tests/test_station_report.py
@@ -0,0 +1,507 @@
+import json
+from datetime import date, datetime
+
+from damage_engine.report import report, station_report
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Analysis, Severity
+
+DAY = date(2026, 4, 22)
+
+
+def _a(block, station, status=NO_EVENT, details=None, field=None, run_day=DAY, **kw):
+    return Analysis(block=block, run_day=run_day, field_id=field or f"ST{station}",
+                    field_name=f"field {station}", station_id=station, station_name=f"S{station}",
+                    status=status, details=details or {}, **kw)
+
+
+BRINA_EVENT = dict(status=EVENT, severity=Severity.SEVERE, severity_score=0.79, duration_h=5,
+                   start=datetime(2026, 4, 22, 1), end=datetime(2026, 4, 22, 5),
+                   details={"wind_checked": True, "records": 13, "night_min_temp_c": -1.2,
+                            "min_temp_c": -1.2, "min_bud_temp_c": -3.2, "min_wet_bulb_c": -1.58,
+                            "severity_method": "kill_fraction", "bbch": 65, "stage": "full_bloom",
+                            "kill_fraction": 0.57, "in_cover_window": True,
+                            "max_wind_ms": 3.4, "windy_hours": 2})
+FLOOD_DRY = {"records": 24, "rain_day_mm": 0.0, "max_1h_mm": 0.0, "max_72h_mm": None,
+             "rain_72h_to_run_mm": 8.6, "rain_10d_mm": 8.8, "reference_10d_mm": None}
+
+
+def _store(root):
+    report.submit("brina", DAY, [_a("brina", 566, **BRINA_EVENT),
+                                 _a("brina", 3, details={"night_min_temp_c": 9.0})], root=root)
+    report.submit("flood", DAY, [_a("flood", 566, details=FLOOD_DRY),
+                                 _a("flood", 3, details=FLOOD_DRY)], root=root)
+
+
+def _check(block, name):
+    return next(c for c in block["checks"] if c["check"] == name)
+
+
+def test_report_holds_only_its_station_and_every_block(tmp_path):
+    _store(tmp_path)
+    c = station_report.build(566, DAY, tmp_path)
+    assert [b["block"] for b in c["blocks"]] == list(station_report.EVENTS)   # every event, diagram order
+    assert {b["block"] for b in c["blocks"] if b["status"] != "not_run"} == {"brina", "flood"}
+    assert {f["field_id"] for b in c["blocks"] for f in b["fields"]} == {"ST566"}
+    assert c["overall"]["events"] == 1 and c["overall"]["worst_severity"] == "severe"
+
+
+def test_checks_show_value_threshold_and_margin(tmp_path):
+    _store(tmp_path)
+    c = station_report.build(566, DAY, tmp_path)
+    brina, flood = _block(c, "brina"), _block(c, "flood")
+    air = _check(brina, "Lowest air temperature")
+    assert (air["measured"], air["threshold"], air["status"], air["margin"]) == (-1.2, 2.0, "crossed", 3.2)
+    wind = _check(brina, "Highest wind in brina hours")             # not a condition: info only
+    assert (wind["measured"], wind["status"]) == (3.4, "info")
+    rain = _check(flood, "Rain in 1 hour (wettest hour)")
+    assert (rain["status"], rain["margin"], rain["threshold_text"]) == ("not_crossed", 30.0, "≥ 30 mm")
+    assert _check(flood, "Rain in the last 10 days")["status"] == "info"   # no 5-year average
+
+
+def _block(content, name):
+    return next(b for b in content["blocks"] if b["block"] == name)
+
+
+def test_no_event_night_marks_unstored_values(tmp_path):
+    _store(tmp_path)
+    brina = _block(station_report.build(3, DAY, tmp_path), "brina")
+    assert _check(brina, "Lowest wet-bulb temperature")["status"] == "not_recorded"
+    assert _check(brina, "Lowest air temperature")["status"] == "not_crossed"
+    assert _check(brina, "Highest wind in brina hours")["status"] == "not_applied"   # no wind_checked
+
+
+def test_no_data_has_no_checks(tmp_path):
+    report.submit("brina", DAY, [_a("brina", 600, status=NO_DATA, data_notes=["No sensor."])],
+                  root=tmp_path)
+    c = station_report.build(600, DAY, tmp_path)
+    assert _block(c, "brina")["checks"] == [] and _block(c, "brina")["data_notes"] == ["No sensor."]
+    assert c["overall"]["headline"] == "No usable data for this station."
+
+
+def test_write_makes_json_and_pdf(tmp_path):
+    _store(tmp_path / "reports")
+    json_path, pdf_path = station_report.write(566, DAY, tmp_path / "reports", tmp_path / "out")
+    assert json_path.name == "2026-04-22.json" and json_path.parent.name == "566"
+    assert json.loads(json_path.read_text(encoding="utf-8"))["station"]["id"] == 566
+    assert pdf_path.read_bytes()[:5] == b"%PDF-"
+
+
+def test_missing_sensors_compared_with_other_stations(tmp_path):
+    c = station_report.build(153, DAY, tmp_path)                # Caraglio: no wind, no solar
+    brina = [m["sensor"] for m in _block(c, "brina")["missing_sensors"]]
+    assert brina == ["VelocitaVento2m", "RadiazioneSolare"]
+    wind = _block(c, "strong_wind")["missing_sensors"][0]
+    assert wind == {"sensor": "VelocitaVento2m", "name": wind["name"]}   # no counts of other stations
+    assert _block(c, "flood")["missing_sensors"] == []          # every station has a rain gauge
+    drought = [m["sensor"] for m in _block(c, "drought")["missing_sensors"]]   # rain + soil moisture
+    assert drought == ["Suolo1", "Suolo2"]
+
+
+def test_five_year_note_hidden_and_new_block_gets_generic_table(tmp_path):
+    report.submit("flood", DAY, [_a("flood", 3, details=FLOOD_DRY,
+                                    data_notes=["No 5-year reference average for the 10-day rain: x.",
+                                                "Rain missing for 2 of 24 hours."])], root=tmp_path)
+    report.submit("new_block", DAY, [_a("new_block", 3, details={"max_size_mm": 21.5,
+                                                                 "wind_checked": True})],
+                  root=tmp_path)
+    c = station_report.build(3, DAY, tmp_path)
+    assert _block(c, "flood")["data_notes"] == ["Rain missing for 2 of 24 hours."]
+    [size] = _block(c, "new_block")["checks"]
+    assert (size["check"], size["measured"], size["status"]) == ("Max size mm", 21.5, "info")
+
+
+WIND_EVENT = dict(status=EVENT, severity=Severity.MODERATE, severity_score=0.6, duration_h=3,
+                  start=datetime(2026, 4, 21, 18), end=datetime(2026, 4, 21, 20),
+                  details={"records": 24, "trigger": "hourly_max", "max_wind_ms": 18.5,
+                           "max_wind_kmh": 66.6, "max_wind_at": "2026-04-21T19:00",
+                           "max_mean_wind_ms": 9.3, "fallback_hours": 0, "strong_wind_hours": 3,
+                           "in_cover_window": None})
+
+
+def test_strong_wind_checks_period_and_severity_text(tmp_path):
+    report.submit("strong_wind", DAY, [
+        _a("strong_wind", 566, **WIND_EVENT),
+        _a("strong_wind", 3, details={"records": 24, "trigger": "hourly_max", "max_wind_ms": 8.0,
+                                      "max_wind_kmh": 28.8, "max_wind_at": "2026-04-22T02:00",
+                                      "max_mean_wind_ms": None, "fallback_hours": 0})], root=tmp_path)
+    wind = _block(station_report.build(566, DAY, tmp_path), "strong_wind")
+    assert wind["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    top = _check(wind, "Highest wind in the 24 h")
+    assert (top["status"], top["margin"], top["threshold_text"]) == ("crossed", 4.6, "≥ 13.9 m/s")
+    assert top["note"].startswith("At 21 Apr 19:00, 66.6 km/h. ≥ 50 km/h, Beaufort 7")
+    time_ = _check(wind, "Time with wind ≥ 13.9 m/s")
+    assert (time_["measured"], time_["status"], time_["threshold_text"]) == (3, "crossed", "≥ 10 min")
+    assert _check(wind, "Highest hourly mean wind")["status"] == "info"
+    field = wind["fields"][0]
+    assert field["severity_basis"].startswith("Graded on the highest wind")
+    assert field["severity_scale"] == ("Wind: light ≥ 13.9 m/s, moderate ≥ 17.2 m/s, severe ≥ 20.8 m/s, "
+                                       "extreme ≥ 24.5 m/s (Beaufort 7, 8, 9, 10)")
+    calm = _block(station_report.build(3, DAY, tmp_path), "strong_wind")
+    assert _check(calm, "Highest wind in the 24 h")["status"] == "not_crossed"
+    assert _check(calm, "Time with wind ≥ 13.9 m/s")["status"] == "not_crossed"
+    assert _check(calm, "Highest hourly mean wind")["status"] == "not_recorded"
+
+
+FREEZING_EVENT = dict(status=EVENT, severity=Severity.MODERATE, severity_score=0.56, duration_h=4,
+                      start=datetime(2026, 4, 21, 10), end=datetime(2026, 4, 21, 13),
+                      details={"records": 13, "daylight_hours": 13, "sunlit_hours_in_night_window": 2,
+                               "solar_checked": True, "day_min_temp_c": -2.5, "mean_wind_ms": 3.0,
+                               "advective_wind": True, "min_wet_bulb_c": -3.47,
+                               "severity_method": "fallback_min_bud_temp", "in_cover_window": None})
+
+
+def test_freezing_checks_period_and_severity_text(tmp_path):
+    report.submit("freezing", DAY, [_a("freezing", 566, **FREEZING_EVENT),
+                                    _a("freezing", 153, details={"records": 11, "daylight_hours": 11,
+                                                                 "sunlit_hours_in_night_window": 0,
+                                                                 "solar_checked": False,
+                                                                 "day_min_temp_c": 8.0})], root=tmp_path)
+    frz = _block(station_report.build(566, DAY, tmp_path), "freezing")
+    assert frz["window"] == {"start": "2026-04-21T06:00", "end": "2026-04-22T06:00"}
+    air = _check(frz, "Lowest daylight air temperature")
+    assert (air["status"], air["margin"], air["threshold_text"]) == ("crossed", 2.5, "< 0 °C")
+    assert _check(frz, "Daylight hours below 0 °C")["status"] == "crossed"
+    assert _check(frz, "Mean wind while below 0 °C")["status"] == "crossed"        # ≥ 2 m/s
+    assert _check(frz, "Sunlit hours of the night (solar radiation)")["measured"] == 2
+    assert frz["fields"][0]["severity_basis"] == ("Crop stage not set: graded on the lowest "
+                                                  "daylight air temperature.")
+    quiet = _block(station_report.build(153, DAY, tmp_path), "freezing")      # no pyranometer, warm day
+    assert _check(quiet, "Lowest daylight air temperature")["status"] == "not_crossed"
+    assert _check(quiet, "Sunlit hours of the night (solar radiation)")["status"] == "not_applied"
+    assert _check(quiet, "Mean wind while below 0 °C")["status"] == "not_recorded"
+    assert _check(quiet, "Lowest wet-bulb while below 0 °C")["status"] == "not_recorded"
+
+
+HEAVY = {"mm": 90.0, "first_rain": "2026-04-18T12:00", "last_rain": "2026-04-19T18:00",
+         "reached_at": "2026-04-19T18:00", "partial": False}
+
+
+def test_rain_row_3_is_the_last_72h_over_the_threshold(tmp_path):
+    report.submit("flood", DAY, [
+        _a("flood", 566, details={**FLOOD_DRY, "last_heavy_72h": HEAVY, "wettest_72h": HEAVY}),
+        _a("flood", 3, details={**FLOOD_DRY, "last_heavy_72h": None,
+                                "wettest_72h": {**HEAVY, "mm": 41.5, "partial": True}}),
+        _a("flood", 5, details=FLOOD_DRY),                       # run stored before this row existed
+    ], root=tmp_path)
+    row = _block(station_report.build(566, DAY, tmp_path), "flood")["checks"][2]
+    assert row["check"] == "72-Hour Heavy Rain Watch (18 Apr 07:00 → 22 Apr 07:00)"          # the 4 days checked
+    assert (row["measured"], row["status"], row["margin"]) == (90.0, "crossed", 10.0)
+    assert row["note"] == "Rain 18 Apr 12:00 → 19 Apr 18:00; 80 mm reached on 19 Apr 18:00."
+    c = station_report.build(566, DAY, tmp_path)               # no new event: past one in the overview
+    assert _block(c, "flood")["notice"]["short"] == "Past: 90 mm in 72 h (18 Apr → 19 Apr)"
+    assert c["overall"]["notices"][0]["text"].startswith("No new event, but 90 mm of rain fell")
+    assert c["overall"]["headline"].startswith("No new damaging event")
+    assert _block(station_report.build(3, DAY, tmp_path), "flood")["notice"] is None
+    none = _block(station_report.build(3, DAY, tmp_path), "flood")["checks"][2]
+    assert (none["measured"], none["status"], none["margin"]) == (41.5, "not_crossed", 38.5)
+    assert none["check"] == "72-Hour Heavy Rain Watch (18 Apr 07:00 → 22 Apr 07:00)"
+    assert none["note"] == ("Rain 18 Apr 12:00 → 19 Apr 18:00. "              # the wettest window
+                            "Rain also fell just before these days (not counted).")
+    old = _block(station_report.build(5, DAY, tmp_path), "flood")["checks"][2]
+    assert old["status"] == "not_recorded" and old["check"] == "72-Hour Heavy Rain Watch (18 Apr 07:00 → 22 Apr 07:00)"
+
+
+def test_rain_row_3_name_is_the_watch_and_its_4_days(tmp_path):
+    report.submit("flood", DAY, [_a("flood", 3, details={**FLOOD_DRY, "last_heavy_72h": None,
+                                                         "wettest_72h": {**HEAVY, "mm": 63.2}})],
+                  root=tmp_path)
+    row = _block(station_report.build(3, DAY, tmp_path), "flood")["checks"][2]
+    assert (row["check"], row["note"]) == ("72-Hour Heavy Rain Watch (18 Apr 07:00 → 22 Apr 07:00)", "Rain 18 Apr 12:00 → 19 Apr 18:00.")
+    later = date(2026, 4, 26)
+    report.submit("flood", later, [_a("flood", 3, run_day=later, details={
+        **FLOOD_DRY, "last_heavy_72h": None, "wettest_72h": None})], root=tmp_path)
+    row = _block(station_report.build(3, later, tmp_path), "flood")["checks"][2]
+    assert row["check"] == "72-Hour Heavy Rain Watch (22 Apr 07:00 → 26 Apr 07:00)"   # follows the run day
+    assert row["note"] == "No rain in the last 4 days."
+    one = {"mm": 0.2, "first_rain": "2026-04-24T06:00", "last_rain": "2026-04-24T06:00", "partial": False}
+    report.submit("flood", later, [_a("flood", 3, run_day=later, details={
+        **FLOOD_DRY, "last_heavy_72h": None, "wettest_72h": one})], root=tmp_path)
+    row = _block(station_report.build(3, later, tmp_path), "flood")["checks"][2]
+    assert row["note"] == "Rain at 24 Apr 06:00."                   # one rainy hour
+
+
+def test_rain_row_2_is_the_last_72_hours(tmp_path):
+    intense = dict(status=EVENT, severity=Severity.LIGHT, severity_score=0.1, duration_h=1,
+                   details={**FLOOD_DRY, "rain_day_mm": 1.0, "max_72h_mm": 85.0,
+                            "rain_72h_to_run_mm": 70.0, "criteria_met": ["intense_72h"],
+                            "worst_criterion": "intense_72h", "rain_trigger_ratio": 1.06})
+    report.submit("flood", DAY, [
+        _a("flood", 566, details=FLOOD_DRY),
+        _a("flood", 3, details={**FLOOD_DRY, "rain_72h_to_run_mm": 95.0}),   # dry day after a storm
+        _a("flood", 5, **intense),                                           # earlier window crossed
+    ], root=tmp_path)
+    rows = {s: _block(station_report.build(s, DAY, tmp_path), "flood")["checks"] for s in (566, 3, 5)}
+    assert [r["check"] for r in rows[566]][:2] == ["Rain in 1 hour (wettest hour)", "Rain in the last 72 hours"]
+    quiet, after_storm, earlier = (rows[s][1] for s in (566, 3, 5))
+    assert (quiet["measured"], quiet["status"], quiet["margin"], quiet["note"]) == (8.6, "not_crossed", 71.4, "")
+    assert quiet["threshold_text"] == "≥ 80 mm"
+    assert (after_storm["status"], after_storm["margin"]) == ("crossed", 15.0)
+    assert after_storm["note"].startswith("No rain in the last 24 h: not a new event")
+    assert (earlier["measured"], earlier["status"]) == (70.0, "not_crossed")
+    assert earlier["note"].startswith("The event comes from an earlier 72 h window")
+
+
+def test_overview_period_of_rain_includes_the_last_4_days(tmp_path):
+    _store(tmp_path)
+    c = station_report.build(566, DAY, tmp_path)
+    flood = _block(c, "flood")
+    assert flood["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    assert flood["watch_window"] == {"label": "Last 4 days", "start": "2026-04-18T07:00",
+                                     "end": "2026-04-22T07:00"}
+    assert _block(c, "brina")["watch_window"] is None
+
+
+def test_new_run_replaces_only_that_day_and_keeps_10_days(tmp_path):
+    _store(tmp_path / "reports")
+    folder = tmp_path / "out" / "566"
+    days = [date(2026, 4, d) for d in range(11, 23)]                       # 12 days: 11 → 22 April
+    for d in days:
+        station_report.write(566, d, tmp_path / "reports", tmp_path / "out")
+    kept = sorted(p.stem for p in folder.glob("*.json"))
+    assert kept == [d.isoformat() for d in days[-10:]]                     # the last 10 days stay
+    assert sorted(p.stem for p in folder.glob("*.pdf")) == kept
+    report.submit("flood", DAY, [_a("flood", 566, details={**FLOOD_DRY, "rain_day_mm": 7.5})],
+                  root=tmp_path / "reports")                               # new run of the same day
+    station_report.write(566, DAY, tmp_path / "reports", tmp_path / "out")
+    assert sorted(p.stem for p in folder.glob("*.json")) == kept           # no other day removed
+    new = json.loads((folder / "2026-04-22.json").read_text(encoding="utf-8"))
+    assert _block(new, "flood")["details"]["rain_day_mm"] == 7.5            # that day's report replaced
+
+
+HOT_WIND_EVENT = dict(status=EVENT, severity=Severity.MODERATE, severity_score=0.55, duration_h=2,
+                      start=datetime(2026, 4, 21, 14), end=datetime(2026, 4, 21, 15),
+                      details={"records": 24, "trigger": "hourly_max", "max_temp_c": 42.3,
+                               "max_temp_at": "2026-04-21T15:00", "max_mean_temp_c": 40.1,
+                               "fallback_hours": 0, "hot_hours": 3, "still_hot_hours": 1,
+                               "wind_checked": True, "wind_at_max_temp_ms": 2.4,
+                               "humidity_at_max_temp_pct": 18, "direction_checked": False,
+                               "hot_wind_hours": 2, "max_hot_wind_temp_c": 42.3,
+                               "wind_assumed_hours": 0, "min_humidity_pct": 18,
+                               "harvest_checked": False, "in_cover_window": None})
+
+
+def test_hot_wind_checks_period_and_severity_text(tmp_path):
+    report.submit("hot_wind", DAY, [
+        _a("hot_wind", 566, **HOT_WIND_EVENT),
+        _a("hot_wind", 3, details={"records": 24, "trigger": "hourly_max", "max_temp_c": 31.0,
+                                   "max_temp_at": "2026-04-21T16:00", "max_mean_temp_c": 29.5,
+                                   "fallback_hours": 0, "hot_hours": 0, "still_hot_hours": 0,
+                                   "wind_checked": False, "wind_at_max_temp_ms": None,
+                                   "humidity_at_max_temp_pct": None, "direction_checked": False})],
+                   root=tmp_path)
+    hot = _block(station_report.build(566, DAY, tmp_path), "hot_wind")
+    assert hot["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    top = _check(hot, "Highest air temperature in the 24 h")
+    assert (top["status"], top["margin"], top["threshold_text"]) == ("crossed", 2.3, "≥ 40 °C")
+    assert top["note"].startswith("At 21 Apr 15:00. ≥ 40 °C at 2 m (contract)")
+    hours = _check(hot, "Hours at ≥ 40 °C with moving air")
+    assert (hours["measured"], hours["status"], hours["threshold_text"]) == (2, "crossed", "≥ 1 h")
+    assert "1 hour(s) at ≥ 40 °C had still air." in hours["note"]
+    wind = _check(hot, "Wind in the hottest hour")
+    assert (wind["status"], wind["threshold_text"]) == ("crossed", "≥ 1 m/s")
+    direction = _check(hot, "Wind from south-east to south-west")
+    assert (direction["status"], direction["threshold_text"]) == ("not_applied", "135°–225°")
+    assert _check(hot, "Humidity in the hottest hour")["status"] == "info"
+    field = hot["fields"][0]
+    assert field["severity_basis"].startswith("Graded on the highest air temperature")
+    assert field["severity_scale"] == ("Air temperature: light ≥ 40 °C, moderate ≥ 42 °C, "
+                                       "severe ≥ 44 °C, extreme ≥ 46 °C")
+    mild = _block(station_report.build(3, DAY, tmp_path), "hot_wind")
+    assert _check(mild, "Highest air temperature in the 24 h")["status"] == "not_crossed"
+    assert _check(mild, "Hours at ≥ 40 °C with moving air")["status"] == "not_crossed"
+    assert _check(mild, "Wind in the hottest hour")["status"] == "not_applied"
+    assert _check(mild, "Humidity in the hottest hour")["status"] == "not_recorded"
+
+
+HIGH_TEMPERATURE_EVENT = dict(
+    status=EVENT, severity=Severity.MODERATE, severity_score=0.6, duration_h=2,
+    start=datetime(2026, 4, 21, 14), end=datetime(2026, 4, 21, 15),
+    details={"records": 24, "trigger": "hourly_max", "max_temp_c": 41.2,
+             "max_temp_at": "2026-04-21T15:00", "max_mean_temp_c": 39.5, "fallback_hours": 0,
+             "humidity_at_max_temp_pct": 22, "wet_bulb_at_max_temp_c": 24.1,
+             "solar_checked": True, "sunlit_hours": 10, "max_solar_wm2": 950,
+             "max_fst_c": 51.1, "max_fst_at": "2026-04-21T14:00", "solar_at_max_fst_wm2": 950,
+             "wind_at_max_fst_ms": 1.2, "sunburn_risk_hours": 4, "max_risk_fst_c": 51.1,
+             "hot_hours": 2, "severity_method": "fst", "severity_value_c": 51.1,
+             "in_cover_window": None})
+
+
+def test_high_temperature_checks_period_and_severity_text(tmp_path):
+    report.submit("high_temperature", DAY, [
+        _a("high_temperature", 566, **HIGH_TEMPERATURE_EVENT),
+        _a("high_temperature", 3, status=EVENT, severity=Severity.LIGHT, duration_h=1,
+           start=datetime(2026, 4, 21, 15), end=datetime(2026, 4, 21, 15),
+           details={"records": 24, "trigger": "hourly_max", "max_temp_c": 40.4,
+                    "max_temp_at": "2026-04-21T15:00", "fallback_hours": 0,
+                    "humidity_at_max_temp_pct": None, "wet_bulb_at_max_temp_c": None,
+                    "solar_checked": False, "max_fst_c": None, "sunburn_risk_hours": None,
+                    "hot_hours": 1, "severity_method": "air", "severity_value_c": 40.4})],
+                   root=tmp_path)
+    hot = _block(station_report.build(566, DAY, tmp_path), "high_temperature")
+    assert hot["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    top = _check(hot, "Highest air temperature in the 24 h")
+    assert (top["status"], top["margin"], top["threshold_text"]) == ("crossed", 1.2, "≥ 40 °C")
+    assert top["note"].startswith("At 21 Apr 15:00. ≥ 40 °C at 2 m (contract)")
+    hours = _check(hot, "Hours at ≥ 40 °C")
+    assert (hours["measured"], hours["status"]) == (2, "crossed")
+    fst = _check(hot, "Highest fruit surface temperature")
+    assert (fst["status"], fst["margin"], fst["threshold_text"]) == ("crossed", 5.1, "≥ 46 °C")
+    assert fst["note"].startswith("At 21 Apr 14:00: sun 950 W/m², wind 1.2 m/s.")
+    risk = _check(hot, "Sunburn risk hours (air ≥ 32 °C, fruit ≥ 46 °C)")
+    assert (risk["measured"], risk["status"]) == (4, "crossed")
+    assert _check(hot, "Wet-bulb in the hottest hour")["status"] == "info"
+    field = hot["fields"][0]
+    assert field["severity_basis"].startswith("Graded on the highest estimated fruit surface temperature (51.1 °C)")
+    assert field["severity_scale"] == ("Fruit surface: light ≥ 46 °C, moderate ≥ 49 °C, "
+                                       "severe ≥ 52 °C, extreme ≥ 55 °C")
+    no_sun = _block(station_report.build(3, DAY, tmp_path), "high_temperature")
+    assert _check(no_sun, "Highest fruit surface temperature")["status"] == "not_applied"
+    assert _check(no_sun, "Sunburn risk hours (air ≥ 32 °C, fruit ≥ 46 °C)")["status"] == "not_applied"
+    assert _check(no_sun, "Humidity in the hottest hour")["status"] == "not_recorded"
+    assert no_sun["fields"][0]["severity_basis"].startswith("No solar radiation data: graded on the highest air")
+    assert no_sun["fields"][0]["severity_scale"].startswith("Air temperature: light ≥ 40 °C")
+
+
+SNOW_SUSPECTED = dict(status=EVENT, duration_h=3, start=datetime(2026, 4, 21, 23),
+                      end=datetime(2026, 4, 22, 1),
+                      details={"records": 24, "cold_basis": "wet_bulb", "air_fallback_hours": 0,
+                               "min_cold_c": -1.6, "min_cold_at": "2026-04-22T00:00",
+                               "cold_hours": 3, "rain_checked": True, "humidity_checked": True,
+                               "solar_checked": False, "snow_hours": 3, "snow_water_mm": 4.0,
+                               "melt_hours": 0, "melt_water_mm": 0.0, "saturated_hours": 0,
+                               "midday_solar_max_wm2": None, "est_new_snow_cm": 4.0,
+                               "est_new_snow_max_cm": 4.0, "signals": ["A"], "suspicion": "likely",
+                               "in_cover_window": True,
+                               "camera_request": {"phenomenon": "snow", "station_id": 3,
+                                                  "photo_at": "2026-04-22T09:00", "level": "likely",
+                                                  "signals": ["A"],
+                                                  "reason": "Snow likely: precipitation in a cold hour."}})
+
+
+def test_snow_is_shown_as_suspected_with_its_signs_and_photo(tmp_path):
+    report.submit("snow", DAY, [_a("snow", 3, **SNOW_SUSPECTED)], root=tmp_path)
+    c = station_report.build(3, DAY, tmp_path)
+    b = _block(c, "snow")
+    assert b["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    [f] = b["fields"]
+    assert f["suspected"] and f["camera_request"]["photo_at"] == "2026-04-22T09:00"
+    assert f["severity_basis"].startswith("Not graded")
+    assert c["overall"]["events"] == 0                      # not a detected event: a photo decides
+    assert c["overall"]["headline"] == "Snow suspected  ·  photo requested to confirm"
+    assert "22 Apr 09:00" in c["overall"]["suspected"][0]["text"]
+    assert _check(b, "A · Precipitation in cold hours")["status"] == "crossed"
+    assert _check(b, "B · Rain within 12 h after a cold hour")["status"] == "not_crossed"
+    assert _check(b, "D · Highest midday sun after the first suspect hour")["status"] == "not_applied"
+    assert _check(b, "Lowest wet-bulb in the 24 h")["margin"] == 2.6
+    json_path, pdf_path = station_report.write(3, DAY, tmp_path, out_dir=tmp_path / "out")
+    assert pdf_path.read_bytes()[:5] == b"%PDF-"
+
+
+def test_hail_is_shown_as_suspected_with_its_signs_and_photo(tmp_path):
+    import pandas as pd
+    from damage_engine.detectors import hail
+    from damage_engine.sources.fields import Field
+    hours = pd.date_range("2026-04-21 07:00", "2026-04-22 07:00", freq="h")
+    t = [20.0] * len(hours)
+    t[10:12] = [14.0, 14.0]                                # 17:00: 6.5 °C drop
+    rain = [0.0] * len(hours)
+    rain[10:12] = [22.0, 2.0]                              # burst rain at 17:00
+    df = pd.DataFrame({"CodiceStazione": 3, "Timestamp": hours, hail.COL_T_MEAN: t,
+                       hail.COL_T_MIN: [x - 0.5 for x in t], hail.COL_RAIN: rain,
+                       hail.COL_WIND: [3.0] * len(hours), hail.COL_WIND_MEAN: [1.0] * len(hours)})
+    [a] = hail.detect(df, [Field(id="ST3", name="field 3", station_id=3)], DAY)
+    assert a.status == EVENT and a.details["suspicion"] == "likely"
+    report.submit("hail", DAY, [a], root=tmp_path)
+    c = station_report.build(3, DAY, tmp_path)
+    b = _block(c, "hail")
+    assert b["window"] == {"start": "2026-04-21T07:00", "end": "2026-04-22T07:00"}
+    [f] = b["fields"]
+    assert f["suspected"] and f["camera_request"]["photo_at"] == "2026-04-22T09:00"
+    assert f["severity_basis"].startswith("Not graded")
+    assert c["overall"]["events"] == 0
+    assert c["overall"]["headline"] == "Hail suspected  ·  photo requested to confirm"
+    burst = _check(b, "A · Rain in the wettest hour")
+    assert (burst["status"], burst["margin"]) == ("crossed", 2.0)
+    assert _check(b, "A · Highest rain rate")["status"] == "not_applied"
+    assert _check(b, "B · Largest temperature drop")["measured"] == 6.5
+    assert _check(b, "C · Highest wind (gust)")["status"] == "not_crossed"
+    assert _check(b, "Signs in one storm")["measured"] == 2
+    json_path, pdf_path = station_report.write(3, DAY, tmp_path, out_dir=tmp_path / "out")
+    assert pdf_path.read_bytes()[:5] == b"%PDF-"
+
+
+DROUGHT_MONTHS = [
+    {"month": "2026-01", "label": "Jan 2026", "days": 31, "missing_days": 0, "rain_mm": 10.0,
+     "normal_mm": 52.7, "rain_pct_of_normal": 19.0, "et0_mm": 20.0, "water_balance_mm": -10.0},
+    {"month": "2026-02", "label": "Feb 2026", "days": 28, "missing_days": 2, "rain_mm": 20.0,
+     "normal_mm": 61.5, "rain_pct_of_normal": 32.5, "et0_mm": 30.0, "water_balance_mm": -10.0},
+    {"month": "2026-03", "label": "Mar 2026", "days": 31, "missing_days": 0, "rain_mm": 30.0,
+     "normal_mm": 82.2, "rain_pct_of_normal": 36.5, "et0_mm": 50.0, "water_balance_mm": -20.0},
+]
+DROUGHT_EVENT = dict(status=EVENT, severity=Severity.MODERATE, severity_score=0.6,
+                     duration_h=90 * 24, start=datetime(2026, 1, 1), end=datetime(2026, 4, 1),
+                     details={"months": DROUGHT_MONTHS, "window_label": "Jan–Mar 2026",
+                              "window_months": 3, "days": 90, "missing_days": 2, "rain_mm": 60.0,
+                              "normal_mm": 196.4, "rain_pct_of_normal": 30.5, "deficit_mm": 136.4,
+                              "last_month_label": "Mar 2026", "et0_mm": 100.0,
+                              "water_balance_mm": -40.0, "water_balance_months": 3,
+                              "soil_moisture": None, "window_start": "2026-01-01T00:00",
+                              "window_end": "2026-04-01T00:00"})
+
+
+def test_drought_shows_rain_against_the_average_month_by_month(tmp_path):
+    report.submit("drought", date(2026, 4, 1), [_a("drought", 566, **DROUGHT_EVENT)], root=tmp_path)
+    _store(tmp_path)                                                   # daily blocks of 22 Apr
+    c = station_report.build(566, DAY, tmp_path)
+    b = _block(c, "drought")
+    assert b["window"]["label"] == "Jan–Mar 2026" and b["monthly"] and b["stale"]
+    pct = _check(b, "Rain as % of the average")
+    assert (pct["measured"], pct["status"], pct["margin"]) == (30.5, "crossed", 29.5)
+    assert pct["threshold_text"] == "≤ 60 % of the average (= 117.8 mm)"
+    total = _check(b, "Rain in the last 3 months (Jan–Mar 2026)")
+    assert total["threshold_text"] == "average 196.4 mm" and "136.4 mm below" in total["note"]
+    feb = _check(b, "Rain in Feb 2026")
+    assert (feb["measured"], feb["threshold_text"], feb["status"]) == (20.0, "average 61.5 mm", "info")
+    assert _check(b, "Water balance: rain − ET0 (Jan–Mar 2026)")["measured"] == -40.0
+    assert _check(b, "Soil moisture, mean of Mar 2026")["status"] == "not_applied"
+    [f] = b["fields"]
+    assert f["period_text"] == "Jan–Mar 2026 (90 days)" and f["duration_text"] == "90 d"
+    assert f["severity_scale"].startswith("Rain / average: light ≤ 60 %")
+    json_path, pdf_path = station_report.write(566, DAY, tmp_path, out_dir=tmp_path / "out")
+    assert pdf_path.read_bytes()[:5] == b"%PDF-"
+
+
+def test_hail_daytime_suspicion_is_merged_into_the_hail_section(tmp_path):
+    report.submit("hail", DAY, [_a("hail", 3, summary="No hail suspected: no rain in the 24 hours. "
+                                                    "Hail-risk day: the station is checked again "
+                                                    "today at 13:00 and 19:00.",
+                                   details={"hail_risk": True, "storm_signs": 0})], root=tmp_path)
+    quiet = _a("hail_daytime", 3, summary="No hail suspected: no rain in the 6 hours.",
+               details={"check": "daytime", "window_start": "2026-04-22T07:00",
+                        "window_end": "2026-04-22T13:00"})
+    request = {"phenomenon": "hail", "station_id": 3, "photo_at": "2026-04-22T19:00",
+               "level": "likely", "signals": ["A", "B"], "reason": "Hail likely: burst rain; x."}
+    storm = _a("hail_daytime", 3, status=EVENT, duration_h=2, start=datetime(2026, 4, 22, 16),
+               end=datetime(2026, 4, 22, 17), summary="Hail likely: burst rain of 24 mm.",
+               details={"check": "daytime", "window_start": "2026-04-22T13:00",
+                        "window_end": "2026-04-22T19:00", "camera_request": request,
+                        "in_cover_window": None},
+               data_notes=["No wind sensor (VelocitaVento2m) at this station: x."])
+    report.submit("hail_daytime", DAY, [quiet], root=tmp_path, slot="13h")
+    report.submit("hail_daytime", DAY, [storm], root=tmp_path, slot="19h")
+    c = station_report.build(3, DAY, tmp_path)
+    assert not any(b["block"] == "hail_daytime" for b in c["blocks"])
+    b = _block(c, "hail")
+    assert b["status"] == EVENT
+    assert b["watch_window"] == {"label": "Daytime checks", "start": "2026-04-22T07:00",
+                                 "end": "2026-04-22T19:00"}
+    [f] = b["fields"]
+    assert f["suspected"] and f["camera_request"]["photo_at"] == "2026-04-22T19:00"
+    assert (f["start"], f["end"], f["duration_h"]) == ("2026-04-22T16:00", "2026-04-22T17:00", 2)
+    assert "Daytime check 07:00 → 13:00: No hail suspected" in f["summary"]
+    assert "Daytime check 13:00 → 19:00: Hail likely" in f["summary"]
+    assert [d["slot"] for d in f["daytime_checks"]] == ["13h", "19h"]
+    assert b["data_notes"] == ["Daytime check 13:00 → 19:00: No wind sensor (VelocitaVento2m) at "
+                               "this station: x."]
+    assert c["overall"]["headline"] == "Hail suspected  ·  photo requested to confirm"
+    json_path, pdf_path = station_report.write(3, DAY, tmp_path, out_dir=tmp_path / "out")
+    assert pdf_path.read_bytes()[:5] == b"%PDF-"
diff --git a/tests/test_strong_wind.py b/tests/test_strong_wind.py
new file mode 100644
index 0000000..3703a6a
--- /dev/null
+++ b/tests/test_strong_wind.py
@@ -0,0 +1,160 @@
+from datetime import date, datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from damage_engine import thresholds
+from damage_engine.detectors import strong_wind
+from damage_engine.schemas import EVENT, NO_DATA, NO_EVENT, Severity
+from damage_engine.sources.fields import Field
+
+RUN_DAY = date(2026, 9, 15)
+# the 24 h checked for RUN_DAY: records 08:00 of 14 Sep ... 07:00 of 15 Sep
+HOURS = pd.date_range("2026-09-14 08:00", "2026-09-15 07:00", freq="h")
+CALM = [3.0] * 24
+
+
+def _day(station, gust, mean=None, ts=None):
+    ts = HOURS[:len(gust)] if ts is None else ts
+    mean = [g / 2 if g is not None and not np.isnan(g) else np.nan for g in gust] if mean is None else mean
+    return pd.DataFrame({"CodiceStazione": station, "Timestamp": ts,
+                         strong_wind.COL_MAX: gust, strong_wind.COL_MEAN: mean})
+
+
+def _field(station, bbch=None):
+    return Field(id=f"ST{station}", name=f"field {station}", station_id=station, bbch=bbch)
+
+
+def _with_storm(values, at=10):
+    gust = list(CALM)
+    gust[at:at + len(values)] = values
+    return gust
+
+
+def test_window_is_the_24h_ending_at_07():
+    assert strong_wind.RUN_HOUR == 7
+    assert strong_wind.day_window(RUN_DAY) == (datetime(2026, 9, 14, 7), datetime(2026, 9, 15, 7))
+
+
+def test_trigger_is_the_hourly_maximum():
+    assert thresholds.STRONG_WIND["use_mean_or_gust"] == "hourly_max"
+    assert strong_wind.trigger_columns() == (strong_wind.COL_MAX, strong_wind.COL_MEAN)
+
+
+def test_storm_is_an_event_graded_on_beaufort():
+    # 18:00-20:00: 15.0, 18.5, 14.0 m/s hourly maximum, means far below the trigger
+    [a] = strong_wind.detect(_day(3, _with_storm([15.0, 18.5, 14.0])), [_field(3)], RUN_DAY, {3: "Lagnasco"})
+    assert a.status == EVENT and a.block == "strong_wind" and a.station_name == "Lagnasco"
+    assert a.duration_h == 3
+    assert a.start == datetime(2026, 9, 14, 18) and a.end == datetime(2026, 9, 14, 20)
+    assert a.details["max_wind_ms"] == 18.5 and a.details["max_wind_kmh"] == 66.6
+    assert a.details["max_wind_at"] == "2026-09-14T19:00"
+    assert a.details["max_mean_wind_ms"] == 9.2           # rounded to 0.1 m/s
+    assert a.details["strong_wind_hours"] == 3
+    assert a.severity == Severity.MODERATE                     # Beaufort 8: 17.2 <= 18.5 < 20.8
+    assert a.details["in_cover_window"] is None                # stage unknown
+    assert "18.5 m/s (66.6 km/h)" in a.summary
+
+
+def test_trigger_is_at_least_13_9():
+    [hit] = strong_wind.detect(_day(3, _with_storm([13.9])), [_field(3)], RUN_DAY)
+    [miss] = strong_wind.detect(_day(3, _with_storm([13.8])), [_field(3)], RUN_DAY)
+    assert hit.status == EVENT and hit.severity == Severity.LIGHT and hit.duration_h == 1
+    assert miss.status == NO_EVENT and miss.severity == Severity.NONE
+    assert miss.details["max_wind_ms"] == 13.8
+    assert miss.summary == "No strong wind: highest wind 13.8 m/s (49.7 km/h)."
+
+
+@pytest.mark.parametrize("wind, level", [(17.2, Severity.MODERATE), (20.8, Severity.SEVERE),
+                                         (24.5, Severity.EXTREME), (30.0, Severity.EXTREME)])
+def test_beaufort_bands(wind, level):
+    [a] = strong_wind.detect(_day(3, _with_storm([wind])), [_field(3)], RUN_DAY)
+    assert a.severity == level
+
+
+def test_hours_outside_the_24h_are_ignored():
+    ts = pd.date_range("2026-09-14 07:00", "2026-09-15 08:00", freq="h")     # 26 records
+    gust = [25.0] + CALM + [25.0]                               # storms at 07:00 (14th), 08:00 (15th)
+    [a] = strong_wind.detect(_day(3, gust, ts=ts), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["records"] == 24
+
+
+def test_cover_window_fruit_set_to_31_october():
+    df = _day(3, _with_storm([16.0]))
+    [after, before] = strong_wind.detect(df, [_field(3, bbch=72), _field(3, bbch=65)], RUN_DAY)
+    assert after.details["in_cover_window"] is True            # fruit set (BBCH > 69)
+    assert before.details["in_cover_window"] is False          # still in bloom
+    assert after.severity == before.severity                   # severity does not depend on the stage
+    assert strong_wind.in_cover_window(date(2026, 11, 2), 75) is False
+
+
+def test_missing_maximum_uses_the_mean():
+    gust = _with_storm([np.nan, 16.0])
+    mean = [1.5] * 24
+    mean[10] = 15.0                                             # only the mean of 18:00 is known
+    [a] = strong_wind.detect(_day(3, gust, mean=mean), [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.duration_h == 2 and a.details["fallback_hours"] == 1
+    assert any("Hourly maximum wind missing for 1 of 24 hours: hourly mean used" in n for n in a.data_notes)
+
+
+def test_missing_hours_are_noted_and_not_checked():
+    df = _day(3, _with_storm([20.0])).drop(index=[0, 1, 2])     # 3 hours with no record
+    [a] = strong_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == EVENT and a.details["records"] == 21
+    assert "Wind missing for 3 of 24 hours: those hours not checked." in a.data_notes
+
+
+def test_no_wind_values_is_no_data():
+    df = _day(3, [np.nan] * 24, mean=[np.nan] * 24)
+    [a] = strong_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+    assert a.data_notes == ["No wind data in the 24 hours checked: strong wind cannot be judged."]
+
+
+def test_missing_columns_do_not_break_the_block():
+    df = pd.DataFrame({"CodiceStazione": 3, "Timestamp": HOURS})          # no wind columns at all
+    [a] = strong_wind.detect(df, [_field(3)], RUN_DAY)
+    assert a.status == NO_DATA
+
+
+def test_station_without_records_is_no_data():
+    [a] = strong_wind.detect(_day(3, CALM), [_field(6)], RUN_DAY)
+    assert a.status == NO_DATA and a.data_notes == ["No records from the station for the last 24 hours."]
+
+
+def test_station_without_wind_sensor_is_no_data(monkeypatch):
+    station = type("S", (), {"id": 3, "name": "Lagnasco", "sensors": ["VelocitaVento2m"]})()
+    monkeypatch.setattr(strong_wind, "stations_with", lambda *s: [station])
+    monkeypatch.setattr(strong_wind, "all_fields", lambda: [_field(3), _field(153)])
+    monkeypatch.setattr(strong_wind, "fetch_hourly", lambda ids, start, end, cols: _day(3, CALM))
+    out = {a.station_id: a for a in strong_wind.run(RUN_DAY)}
+    assert out[3].status == NO_EVENT
+    assert out[153].status == NO_DATA
+    assert out[153].data_notes == ["No wind sensor (VelocitaVento2m) at this station: "
+                                   "strong wind cannot be judged."]
+
+
+def test_run_asks_for_the_24h_records(monkeypatch):
+    calls = []
+    station = type("S", (), {"id": 3, "name": "Lagnasco", "sensors": ["VelocitaVento2m"]})()
+    monkeypatch.setattr(strong_wind, "stations_with", lambda *s: [station])
+    monkeypatch.setattr(strong_wind, "all_fields", lambda: [_field(3)])
+    monkeypatch.setattr(strong_wind, "fetch_hourly",
+                        lambda ids, start, end, cols: calls.append((ids, start, end, cols)) or _day(3, CALM))
+    strong_wind.run(RUN_DAY)
+    assert calls == [([3], datetime(2026, 9, 14, 8), datetime(2026, 9, 15, 7), strong_wind.COLUMNS)]
+
+
+def test_unknown_trigger_setting_is_an_error(monkeypatch):
+    monkeypatch.setitem(thresholds.STRONG_WIND, "use_mean_or_gust", "gust")
+    with pytest.raises(ValueError, match="use_mean_or_gust"):
+        strong_wind.trigger_columns()
+
+
+def test_hourly_mean_setting_uses_the_mean(monkeypatch):
+    monkeypatch.setitem(thresholds.STRONG_WIND, "use_mean_or_gust", "hourly_mean")
+    gust = _with_storm([20.0])
+    mean = [2.0] * 24
+    [a] = strong_wind.detect(_day(3, gust, mean=mean), [_field(3)], RUN_DAY)
+    assert a.status == NO_EVENT and a.details["max_wind_ms"] == 2.0
diff --git a/tests/test_threea.py b/tests/test_threea.py
new file mode 100644
index 0000000..250ab12
--- /dev/null
+++ b/tests/test_threea.py
@@ -0,0 +1,38 @@
+from datetime import datetime
+
+from damage_engine.sources import threea
+
+
+class FakeClient:
+    def __init__(self, answer):
+        self.answer, self.calls = answer, []
+
+    def call(self, method, path, body=None):
+        self.calls.append((method, path, body))
+        return self.answer
+
+
+def test_fetch_daily_asks_for_data_column_not_timestamp(monkeypatch):
+    answer = [
+        {"CodiceStazione": 3, "Data": "2026-10-20 00:00:00.000", "Pioggia": "4.2", "EvapoPot": 1.1},
+        {"CodiceStazione": 3, "Data": "2026-10-19 00:00:00.000", "Pioggia": 0, "EvapoPot": 1.3},
+        {"CodiceStazione": 3, "Data": "2026-10-18 00:00:00.000", "Pioggia": None, "EvapoPot": None},
+    ]
+    fake = FakeClient(answer)
+    monkeypatch.setattr(threea, "client", lambda: fake)
+    df = threea.fetch_daily([3], datetime(2026, 10, 18), datetime(2026, 10, 20), ["Pioggia", "EvapoPot"])
+    method, path, body = fake.calls[0]
+    assert path == "/getWeatherDaily"
+    assert body["columns"] == ["CodiceStazione", "Data", "Pioggia", "EvapoPot"]
+    assert "Timestamp" not in body["columns"]
+    assert list(df["Data"].dt.day) == [19, 20]               # empty row dropped, sorted by date
+    assert df["Pioggia"].tolist() == [0.0, 4.2]
+
+
+def test_fetch_hourly_still_uses_timestamp(monkeypatch):
+    fake = FakeClient([{"CodiceStazione": 3, "Timestamp": "2026-10-20 06:00:00", "Pioggia": 1}])
+    monkeypatch.setattr(threea, "client", lambda: fake)
+    df = threea.fetch_hourly([3], datetime(2026, 10, 20), datetime(2026, 10, 20, 6), ["Pioggia"])
+    assert fake.calls[0][1] == "/getWeatherHourly"
+    assert fake.calls[0][2]["columns"][:2] == ["CodiceStazione", "Timestamp"]
+    assert len(df) == 1