Eppt 2783 wind chill improver wrapper function for calculate wind chill

.mailmap

@@ -2,6 +2,7 @@ Aaron Hopkinson <[email protected]> <[email protected]
 Andrew Creswick <[email protected]> AndrewCreswick <[email protected]>
 Anna Booton <[email protected]> <[email protected]>
 Anja Schubert <[email protected]> <[email protected]>
+Anzer Khan <[email protected]> <[email protected]>
 Belinda Trotta <[email protected]> <[email protected]>
 Benjamin Ayliffe <[email protected]> <[email protected]>
 Benjamin Owen <[email protected]> <[email protected]>

CONTRIBUTING.md

@@ -59,6 +59,7 @@ below:
  - Simon Jackson (Met Office, UK)
  - Caroline Jones (Met Office, UK)
  - Peter Jordan (Met Office, UK)
+ - Anzer Khan (Met Office, UK)
  - Bruno P. Kinoshita (NIWA, NZ)
  - Phoebe Lambert (Met Office, UK)
  - Lucy Liu (Bureau of Meteorology, Australia)

improver/api/__init__.py

@@ -29,6 +29,7 @@
     "ApplyReliabilityCalibration": "improver.calibration.reliability_calibration",
     "BaseNeighbourhoodProcessing": "improver.nbhood.nbhood",
     "CalculateForecastBias": "improver.calibration.simple_bias_correction",
+    "CalculateWindChill": "improver.temperature.feels_like_temperature",
     "CalibratedForecastDistributionParameters": "improver.calibration.emos_calibration",
     "ChooseDefaultWeightsLinear": "improver.blending.weights",
     "ChooseDefaultWeightsNonLinear": "improver.blending.weights",

improver/temperature/feels_like_temperature.py

@@ -10,6 +10,7 @@
 from iris.cube import Cube
 from numpy import ndarray
 
+from improver import BasePlugin
 from improver.metadata.utilities import (
     create_new_diagnostic_cube,
     generate_mandatory_attributes,
@@ -19,70 +20,119 @@
 )
 
 
-def _calculate_wind_chill(temperature: ndarray, wind_speed: ndarray) -> ndarray:
+class CalculateWindChill(BasePlugin):
     """
-    Calculates the wind chill from 10 m wind speed and temperature based on
-    the wind chill temperature index from a linear regression equation detailed
-    in THE NEW WIND CHILL EQUIVALENT TEMPERATURE CHART, Osczevski and
-    Bluestein, 2005, table 2.
-
-    Args:
-        temperature:
-            Air temperature in degrees celsius
-        wind_speed:
-            Wind speed in kilometres per hour
-
-    Returns:
-        Wind chill temperatures in degrees celsius
-
-    References:
-        Osczevski, R. and Bluestein, M. (2005). THE NEW WIND CHILL EQUIVALENT
-        TEMPERATURE CHART. Bulletin of the American Meteorological Society,
-        86(10), pp.1453-1458.
-
-        Osczevski, R. and Bluestein, M. (2008). Comments on Inconsistencies in
-        the New Windchill Chart at Low Wind Speeds. Journal of Applied
-        Meteorology and Climatology, 47(10), pp.2737-2738.
-
-    Science background:
-    The 2005 Osczevski and Bluestein paper outlines the research and the
-    assumptions made, and the 2008 paper clarifies poorly explained sections
-    of the first paper.
-
-    A brief summary of their assumptions are given below:
-    The model aims to determine a worst-case scenario of wind chill. The wind
-    speed "threshold" of 4.8 kph (1.34 m/s) stated in the 2005 papers does not
-    refer to a threshold placed on the input windspeed data, which has no upper
-    limit, but is the walking speed of an average person. This is therefore
-    used as the minimum wind speed in their wind chill computer model, because
-    even where wind speed is zero, a person would still experience wind chill
-    from the act of walking (the model assumes that the person is walking into
-    the wind). Furthermore, the equation is not valid for very low wind speeds
-    and will return wind chill values higher than the air temperature if this
-    lower wind speed limit is not imposed. Even with this limit, the calculated
-    wind chill will be higher than the air temperature when the temperature is
-    above about 11.5C and the wind is 4.8 kph.
-    The model introduces a compensation factor where it assumes that
-    the wind speed at 1.5 m (face level) is 2/3 that measured at 10 m. It also
-    takes into account the thermal resistance of the skin on the human cheek
-    with the assumption that the face is the most exposed area of skin
-    during winter.
-
-    The equation outlined in their paper is also not the equation used in their
-    model (which was computationally expensive) but rather it is a linear
-    regression equation which mimics the output of their model where wind
-    speeds are greater than 3kph (0.8m/s) (clarified in the 2008 paper). The
-    assumption being that lower wind speeds are usually not measured or
-    reported accurately anyway.
+    Plugin to calculate wind chill temperature from air temperature and wind speed.
+    Based on Osczevski and Bluestein (2005).
     """
-    eqn_component = np.clip(wind_speed, 4.824, None) ** 0.16
-    wind_chill = (
-        13.12
-        + 0.6215 * temperature
-        - 11.37 * eqn_component
-        + 0.3965 * temperature * eqn_component
-    ).astype(np.float32)
-    return wind_chill
+
+    def __init__(self, model_id_attr: Optional[str] = None):
+        """Set up the plugin."""
+        self.model_id_attr = model_id_attr
+
+    def _calculate_wind_chill(
+        self, temperature: ndarray, wind_speed: ndarray
+    ) -> ndarray:
+        """
+        Calculates the wind chill from 10 m wind speed and temperature based on
+        the wind chill temperature index from a linear regression equation detailed
+        in THE NEW WIND CHILL EQUIVALENT TEMPERATURE CHART, Osczevski and
+        Bluestein, 2005, table 2.
+
+        Args:
+            temperature:
+                Air temperature in degrees celsius
+            wind_speed:
+                Wind speed in kilometres per hour
+
+        Returns:
+            Wind chill temperatures in degrees celsius
+
+        References:
+            Osczevski, R. and Bluestein, M. (2005). THE NEW WIND CHILL EQUIVALENT
+            TEMPERATURE CHART. Bulletin of the American Meteorological Society,
+            86(10), pp.1453-1458.
+
+            Osczevski, R. and Bluestein, M. (2008). Comments on Inconsistencies in
+            the New Windchill Chart at Low Wind Speeds. Journal of Applied
+            Meteorology and Climatology, 47(10), pp.2737-2738.
+
+        Science background:
+        The 2005 Osczevski and Bluestein paper outlines the research and the
+        assumptions made, and the 2008 paper clarifies poorly explained sections
+        of the first paper.
+
+        A brief summary of their assumptions are given below:
+        The model aims to determine a worst-case scenario of wind chill. The wind
+        speed "threshold" of 4.8 kph (1.34 m/s) stated in the 2005 papers does not
+        refer to a threshold placed on the input windspeed data, which has no upper
+        limit, but is the walking speed of an average person. This is therefore
+        used as the minimum wind speed in their wind chill computer model, because
+        even where wind speed is zero, a person would still experience wind chill
+        from the act of walking (the model assumes that the person is walking into
+        the wind). Furthermore, the equation is not valid for very low wind speeds
+        and will return wind chill values higher than the air temperature if this
+        lower wind speed limit is not imposed. Even with this limit, the calculated
+        wind chill will be higher than the air temperature when the temperature is
+        above about 11.5C and the wind is 4.8 kph.
+        The model introduces a compensation factor where it assumes that
+        the wind speed at 1.5 m (face level) is 2/3 that measured at 10 m. It also
+        takes into account the thermal resistance of the skin on the human cheek
+        with the assumption that the face is the most exposed area of skin
+        during winter.
+
+        The equation outlined in their paper is also not the equation used in their
+        model (which was computationally expensive) but rather it is a linear
+        regression equation which mimics the output of their model where wind
+        speeds are greater than 3kph (0.8m/s) (clarified in the 2008 paper). The
+        assumption being that lower wind speeds are usually not measured or
+        reported accurately anyway.
+        """
+        eqn_component = np.clip(wind_speed, 4.824, None) ** 0.16
+        wind_chill = (
+            13.12
+            + 0.6215 * temperature
+            - 11.37 * eqn_component
+            + 0.3965 * temperature * eqn_component
+        ).astype(np.float32)
+        return wind_chill
+
+    def process(self, temperature: Cube, wind_speed: Cube) -> Cube:
+        """
+        Calculate the wind chill temperature using Iris cubes.
+
+        Args:
+            temperature:
+                Cube of air temperature.
+            wind_speed:
+                Cube of 10m wind speed.
+
+        Returns:
+            Cube of wind chill temperature (same shape and grid as inputs).
+        """
+        orig_temp_units = temperature.units
+        t_cube = temperature.copy()
+        t_cube.convert_units("degC")
+        w_cube = wind_speed.copy()
+        w_cube.convert_units("km h-1")
+
+        wind_chill_data = self._calculate_wind_chill(t_cube.data, w_cube.data)
+
+        attributes = generate_mandatory_attributes(
+            [temperature, wind_speed],
+            model_id_attr=self.model_id_attr,
+        )
+        wind_chill_cube = create_new_diagnostic_cube(
+            "wind_chill_temperature",
+            "degC",
+            temperature,
+            attributes,
+            data=wind_chill_data,
+        )
+        # Match input temperature units
+        wind_chill_cube.convert_units(orig_temp_units)
+
+        return wind_chill_cube
 
 
 def _calculate_apparent_temperature(
@@ -211,8 +261,9 @@ def calculate_feels_like_temperature(
         t_celsius, w_cube.data, rh_cube.data, p_cube.data
     )
 
-    w_cube.convert_units("km h-1")
-    wind_chill = _calculate_wind_chill(t_celsius, w_cube.data)
+    wind_chill_plugin = CalculateWindChill(model_id_attr=model_id_attr)
+    wind_chill_cube = wind_chill_plugin.process(t_cube, w_cube)
+    wind_chill = wind_chill_cube.data
 
     feels_like_temperature = _feels_like_temperature(
         t_celsius, apparent_temperature, wind_chill

improver_tests/temperature/feels_like_temperature/test_feels_like_temperature.py

@@ -7,15 +7,83 @@
 import unittest
 
 import numpy as np
+import pytest
 from iris.tests import IrisTest
 
 from improver.synthetic_data.set_up_test_cubes import set_up_variable_cube
 from improver.temperature.feels_like_temperature import (
+    CalculateWindChill,
     _calculate_apparent_temperature,
-    _calculate_wind_chill,
     calculate_feels_like_temperature,
 )
 
+MANDATORY_ATTRIBUTES = {
+    "source": "Met Office Unified Model",
+    "institution": "Met Office",
+    "title": "UKV Model Forecast on UK 2 km Standard Grid",
+}
+
+
+@pytest.fixture
+def temperature_cube():
+    """Simple temperature cube (273.15 K = 0°C)."""
+    data = np.full((3, 3), 273.15, dtype=np.float32)
+    return set_up_variable_cube(
+        data,
+        name="air_temperature",
+        units="K",
+        standard_grid_metadata="uk_det",
+        attributes=MANDATORY_ATTRIBUTES,
+    )
+
+
+@pytest.fixture
+def wind_speed_cube():
+    """Simple wind-speed cube (10 m/s)."""
+    data = np.full((3, 3), 10.0, dtype=np.float32)
+    return set_up_variable_cube(
+        data,
+        name="wind_speed",
+        units="m s-1",
+        standard_grid_metadata="uk_det",
+        attributes=MANDATORY_ATTRIBUTES,
+    )
+
+
+def test__calculate_wind_chill_values():
+    """Direct test of the internal wind chill equation method."""
+    temperature = np.full((1, 3), 1.7)
+    wind_speed = np.full((1, 3), 3) * 60 * 60 / 1000.0
+    expected = np.full((1, 3), -1.4754, dtype=np.float32)
+
+    plugin = CalculateWindChill()
+    result = plugin._calculate_wind_chill(temperature, wind_speed)
+
+    np.testing.assert_almost_equal(result, expected, decimal=4)
+
+
+def test_process_outputs_expected_cube(temperature_cube, wind_speed_cube):
+    """Test that process() returns correct data, preserves metadata, and
+    keeps coordinates identical to the input cube."""
+    plugin = CalculateWindChill()
+    result = plugin.process(temperature_cube, wind_speed_cube)
+
+    assert result.name() == "wind_chill_temperature"
+    assert str(result.units) == str(temperature_cube.units)
+
+    for key, val in MANDATORY_ATTRIBUTES.items():
+        assert key in result.attributes
+        assert result.attributes[key] == val
+
+    input_coords = [coord.name() for coord in temperature_cube.coords(dim_coords=True)]
+    output_coords = [coord.name() for coord in result.coords(dim_coords=True)]
+    all_coords = set(input_coords + output_coords)
+    for coord_name in all_coords:
+        assert temperature_cube.coord(coord_name) == result.coord(coord_name)
+    expected_data = np.full((3, 3), 266.09708, dtype=np.float32)
+    assert result.data.shape == temperature_cube.data.shape
+    np.testing.assert_allclose(result.data, expected_data, rtol=1e-6)
+
 
 class Test__calculate_apparent_temperature(IrisTest):
     """Test the apparent temperature function."""
@@ -33,18 +101,6 @@ def test_values(self):
         self.assertArrayAlmostEqual(result, expected_result, decimal=4)
 
 
-class Test__calculate_wind_chill(IrisTest):
-    """Test the wind chill function."""
-
-    def test_values(self):
-        """Test output values when from the wind chill equation."""
-        temperature = np.full((1, 3), 1.7)
-        wind_speed = np.full((1, 3), 3) * 60 * 60 / 1000.0
-        expected_result = np.full((1, 3), -1.4754, dtype=np.float32)
-        result = _calculate_wind_chill(temperature, wind_speed)
-        self.assertArrayAlmostEqual(result, expected_result, decimal=4)
-
-
 class Test_calculate_feels_like_temperature(IrisTest):
     """Test the feels like temperature function."""