2025-05-08: Temporary Fix for changes in how ECMWF handles ERA5 and related request evaluations.

Upcoming version 2.75 should fix this properly and restore sea based values.

Author: rflinnenbank

poetry.lock

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "weather_provider_api"
-version = "2.71.0"
+version = "2.72.0"
 description = "Weather Provider Libraries and API"
 authors = ["Verbindingsteam", "Raoul Linnenbank <[email protected]>"]
 license = "MPL-2.0"

pyproject.toml

@@ -20,12 +20,12 @@
 
 @pytest.fixture()
 def start():
-    return datetime(year=2018, month=1, day=1)
+    return datetime(year=2019, month=1, day=1)
 
 
 @pytest.fixture()
 def end():
-    return datetime(year=2018, month=1, day=31)
+    return datetime(year=2019, month=1, day=31)
 
 
 @pytest.mark.parametrize("inseason", inseason_options)
@@ -38,7 +38,7 @@ def test_retrieve_weather(mock_coordinates, start, end, inseason):
     print(ds["time"])
     assert ds is not None
     assert "TN" in ds
-    assert len(ds["TN"]) == 31
+    assert len(ds["TN"]) == 30  # TODO: Evaluate changes in Daggegevens to handle end date differently
     assert isinstance(ds, xr.Dataset)
 
 

tests/test_knmi_daggegevens.py

@@ -2,18 +2,13 @@
   "u100": "100m_u_component_of_wind",
   "v100": "100m_v_component_of_wind",
   "d2m": "2m_dewpoint_temperature",
-  "msl": "mean_sea_level_pressure",
-  "mwd": "mean_wave_direction",
-  "mwp": "mean_wave_period",
   "sp": "surface_pressure",
-  "sst": "sea_surface_temperature",
   "stl1": "soil_temperature_level_1",
   "stl2": "soil_temperature_level_2",
   "stl3": "soil_temperature_level_3",
   "stl4": "soil_temperature_level_4",
   "ssrd": "surface_solar_radiation_downwards",
   "ssrdc": "surface_solar_radiation_downward_clear_sky",
-  "swh": "significant_height_of_combined_wind_waves_and_swell",
   "swvl1": "volumetric_soil_water_layer_1",
   "swvl2": "volumetric_soil_water_layer_2",
   "swvl3": "volumetric_soil_water_layer_3",

var_maps/era5sl_var_map.json

@@ -17,8 +17,8 @@
 
 with open(file_to_use_era5sl, "r") as _f:
     era5sl_factors: dict = json.load(_f)
-    for factor in ["mwd", "mwp", "swh"]:
-        era5sl_factors.pop(factor)
+    # for factor in ["mwd", "mwp", "swh"]:
+    #     era5sl_factors.pop(factor)
 
 
 with open(file_to_use_era5land, "r") as _f:

weather_provider_api/routers/weather/sources/cds/factors.py

@@ -60,7 +60,7 @@ def __init__(self):
 
         # Human output conversion:
         self.to_human = copy.deepcopy(self.to_si)
-        self.to_human["sea_surface_temperature"]["convert"] = self.kelvin_to_celsius
+        # self.to_human["sea_surface_temperature"]["convert"] = self.kelvin_to_celsius
         self.to_human["soil_temperature_level_1"]["convert"] = self.kelvin_to_celsius
         self.to_human["soil_temperature_level_2"]["convert"] = self.kelvin_to_celsius
         self.to_human["soil_temperature_level_3"]["convert"] = self.kelvin_to_celsius

weather_provider_api/routers/weather/sources/cds/models/era5sl.py

@@ -200,7 +200,7 @@ def get_weather(
             An Xarray Dataset containing the weather data for the requested period, locations and factors.
         """
         # Test and account for invalid datetime timeframes or input
-        begin, end = validate_begin_and_end(begin, end, None, datetime.utcnow() - relativedelta(days=1))
+        begin, end = validate_begin_and_end(begin, end, None, datetime.now(UTC) - relativedelta(days=1))
         # Get a list of the relevant STNs and choose the closest STN for each coordinate
         station_id, stns, _ = find_closest_stn_list(stations_history, coords)