642 abiotic simple model add simple wind profile

docs/source/virtual_ecosystem/implementation/abiotic_simple_implementation.md

@@ -56,7 +56,8 @@ display_markdown(
 
 The `abiotic_simple` model is a simple regression model that estimates microclimatic
 variables based on empirical relationships between leaf area index (LAI) and atmospheric
-temperature (T), relative humidity (RH), and vapour pressure deficit (VPD) to derive
+temperature (T), relative humidity (RH), vapour pressure deficit (VPD), and wind speed
+to derive
 logarithmic profiles of these variables from external climate data such as regional
 climate models or satellite observations. The model also provides information on
 atmospheric pressure and $\ce{CO_{2}}$ and soil temperatures at different depths.

tests/models/abiotic/test_abiotic_model.py

@@ -18,6 +18,7 @@
     (DEBUG, "abiotic model: required var 'topofcanopy_radiation' checked"),
     (DEBUG, "abiotic model: required var 'leaf_area_index' checked"),
     (DEBUG, "abiotic model: required var 'layer_heights' checked"),
+    (DEBUG, "abiotic model: required var 'wind_speed_ref' checked"),
 )
 
 SETUP_MANIPULATIONS = (
@@ -27,6 +28,7 @@
     (INFO, "Replacing data array for 'air_temperature'"),
     (INFO, "Replacing data array for 'relative_humidity'"),
     (INFO, "Adding data array for 'vapour_pressure_deficit'"),
+    (INFO, "Replacing data array for 'wind_speed'"),
     (INFO, "Replacing data array for 'atmospheric_pressure'"),
     (INFO, "Adding data array for 'atmospheric_co2'"),
     (INFO, "Replacing data array for 'soil_temperature'"),
@@ -120,6 +122,10 @@ def test_abiotic_model_initialization_no_data(caplog, fixture_core_components):
                 ERROR,
                 "abiotic model: init data missing required var 'layer_heights'",
             ),
+            (
+                ERROR,
+                "abiotic model: init data missing required var 'wind_speed_ref'",
+            ),
             (ERROR, "abiotic model: error checking vars_required_for_init, see log."),
         ),
     )

tests/models/abiotic_simple/test_abiotic_simple_model.py

@@ -227,6 +227,7 @@ def test_setup(dummy_climate_data_varying_canopy, fixture_core_components):
         "soil_temperature",
         "atmospheric_pressure",
         "atmospheric_co2",
+        "wind_speed",
     ]:
         assert var in model.data
 
@@ -240,6 +241,16 @@ def test_setup(dummy_climate_data_varying_canopy, fixture_core_components):
     ]
     xr.testing.assert_allclose(model.data["air_temperature"], exp_air_temp)
 
+    exp_wind = lyr_strct.from_template()
+    exp_wind[lyr_strct.index_filled_atmosphere] = [
+        [1, 1, 1, 1],
+        [0.993673, 0.995782, 0.997891, 0.997891],
+        [0.953925, 0.969284, np.nan, np.nan],
+        [0.885976, np.nan, np.nan, np.nan],
+        [0.434528, 0.623019, 0.811509, 0.811509],
+    ]
+    xr.testing.assert_allclose(model.data["wind_speed"], exp_wind)
+
     exp_soil_temp = lyr_strct.from_template()
     exp_soil_temp[lyr_strct.index_all_soil] = [
         [20.712458, 21.317566, 21.922674, 21.922674],

tests/models/abiotic_simple/test_microclimate.py

@@ -15,7 +15,6 @@ def test_log_interpolation(dummy_climate_data, fixture_core_components):
 
     # temperature
     result = log_interpolation(
-        data=dummy_climate_data,
         reference_data=dummy_climate_data["air_temperature_ref"].isel(time_index=0),
         leaf_area_index_sum=leaf_area_index_sum,
         layer_structure=lyr_strct,
@@ -33,7 +32,6 @@ def test_log_interpolation(dummy_climate_data, fixture_core_components):
 
     # relative humidity
     result_hum = log_interpolation(
-        data=dummy_climate_data,
         reference_data=dummy_climate_data["relative_humidity_ref"].isel(time_index=0),
         leaf_area_index_sum=leaf_area_index_sum,
         layer_structure=lyr_strct,
@@ -63,7 +61,6 @@ def test_varying_canopy_log_interpolation(
 
     # temperature
     result = log_interpolation(
-        data=data,
         reference_data=data["air_temperature_ref"].isel(time_index=0),
         leaf_area_index_sum=leaf_area_index_sum,
         layer_structure=lyr_strct,
@@ -211,6 +208,12 @@ def test_run_microclimate(dummy_climate_data, fixture_core_components):
     exp_pressure[lyr_strct.index_atmosphere] = 96
     xr.testing.assert_allclose(result["atmospheric_pressure"], exp_pressure)
 
+    exp_wind = lyr_strct.from_template()
+    exp_wind[lyr_strct.index_filled_atmosphere] = np.array(
+        [1.0, 0.993673, 0.953925, 0.885976, 0.434528]
+    )[:, None]
+    np.testing.assert_allclose(result["wind_speed"], exp_wind, rtol=1e-3, atol=1e-3)
+
 
 def test_run_microclimate_varying_canopy(
     dummy_climate_data_varying_canopy, fixture_core_components
@@ -251,6 +254,16 @@ def test_run_microclimate_varying_canopy(
     ]
     xr.testing.assert_allclose(result["soil_temperature"], exp_soil_temp)
 
+    exp_wind = lyr_strct.from_template()
+    exp_wind[lyr_strct.index_filled_atmosphere] = [
+        [1, 1, 1, 1],
+        [0.993673, 0.995782, 0.997891, 0.997891],
+        [0.953925, 0.969284, np.nan, np.nan],
+        [0.885976, np.nan, np.nan, np.nan],
+        [0.434528, 0.623019, 0.811509, 0.811509],
+    ]
+    xr.testing.assert_allclose(result["wind_speed"], exp_wind)
+
     exp_pressure = lyr_strct.from_template()
     exp_pressure[lyr_strct.index_atmosphere] = 96
     xr.testing.assert_allclose(result["atmospheric_pressure"], exp_pressure)

virtual_ecosystem/models/abiotic/abiotic_model.py

@@ -55,6 +55,7 @@ class AbioticModel(
         "topofcanopy_radiation",
         "leaf_area_index",
         "layer_heights",
+        "wind_speed_ref",
     ),
     vars_updated=(
         "air_temperature",

virtual_ecosystem/models/abiotic_simple/__init__.py

@@ -11,8 +11,8 @@
 
 * The :mod:`~virtual_ecosystem.models.abiotic_simple.microclimate` submodule
   contains a set functions and parameters that are used to calculate atmospheric
-  temperature, relative humidity, vapour pressure deficit, :math:`\ce{CO2}`, and
-  atmospheric pressure profiles as well as soil temperature profiles.
+  temperature, relative humidity, vapour pressure deficit, wind speed, :math:`\ce{CO2}`,
+  and atmospheric pressure profiles as well as soil temperature profiles.
 
 * The :mod:`~virtual_ecosystem.models.abiotic_simple.constants` submodule provides a
   set of dataclasses containing the constants required by the broader abiotic model

virtual_ecosystem/models/abiotic_simple/abiotic_simple_model.py

@@ -40,13 +40,15 @@ class AbioticSimpleModel(
         "soil_temperature",
         "atmospheric_pressure",
         "atmospheric_co2",
+        "wind_speed",
     ),
     vars_required_for_update=(
         "air_temperature_ref",
         "relative_humidity_ref",
         "vapour_pressure_deficit_ref",
         "atmospheric_pressure_ref",
         "atmospheric_co2_ref",
+        "wind_speed_ref",
         "leaf_area_index",
         "layer_heights",
     ),

virtual_ecosystem/models/abiotic_simple/constants.py

@@ -49,5 +49,13 @@ class AbioticSimpleBounds(ConstantsDataclass):
     leaf area index from :cite:t:`hardwick_relationship_2015`.
     """
 
+    wind_speed: tuple[float, float, float] = (0.001, 100.0, -0.1)
+    """Bounds and gradient for wind speed, [m s-1].
+
+    Gradient for linear regression to calculate wind speed as a function of
+    leaf area index. The value is choses arbitrarily and needs to be replaced with
+    observations.
+    """
+
     soil_temperature: tuple[float, float] = (-10.0, 50.0)
     """Bounds for soil temperature, [C]."""

virtual_ecosystem/models/abiotic_simple/microclimate.py

@@ -3,7 +3,8 @@
 atmospheric temperature, relative humidity, and vapour pressure deficit at ground level
 (1.5 m) given the above canopy conditions and leaf area index of intervening canopy. A
 within canopy profile is then interpolated using a logarithmic curve between the above
-canopy observation and ground level prediction.
+canopy observation and ground level prediction. The same method is applied to derive a
+vertical wind profile within the canopy.
 Soil temperature is interpolated between the surface layer and the soil temperature at
 1 m depth which equals the mean annual temperature.
 The module also provides a constant vertical profile of atmospheric pressure and
@@ -33,8 +34,8 @@ def run_microclimate(
     r"""Calculate simple microclimate.
 
     This function uses empirical relationships between leaf area index (LAI) and
-    atmospheric temperature, relative humidity, and vapour pressure deficit to derive
-    logarithmic profiles of these variables from external climate data such as
+    atmospheric temperature, relative humidity, vapour pressure deficit, and wind speed
+    to derive logarithmic profiles of these variables from external climate data such as
     regional climate models or satellite observations. Note that these sources provide
     data at different heights and with different underlying assumptions which lead to
     different biases in the model output. For below canopy values (1.5 m),
@@ -71,6 +72,7 @@ def run_microclimate(
     * vapour_pressure_deficit_ref [kPa]
     * atmospheric_pressure_ref [kPa]
     * atmospheric_co2_ref [ppm]
+    * wind_speed_ref [m s-1]
     * leaf_area_index [m m-1]
     * layer_heights [m]
 
@@ -84,8 +86,8 @@ def run_microclimate(
 
     Returns:
         Dict of DataArrays for air temperature [C], relative humidity [-], vapour
-        pressure deficit [kPa], soil temperature [C], atmospheric pressure [kPa], and
-        atmospheric :math:`\ce{CO2}` [ppm]
+        pressure deficit [kPa], soil temperature [C], atmospheric pressure [kPa],
+        atmospheric :math:`\ce{CO2}` [ppm], wind speed [m s-1]
     """
 
     output = {}
@@ -94,11 +96,15 @@ def run_microclimate(
     leaf_area_index_sum = data["leaf_area_index"].sum(dim="layers")
 
     # Interpolate atmospheric profiles
-    for var in ["air_temperature", "relative_humidity", "vapour_pressure_deficit"]:
+    for var in [
+        "air_temperature",
+        "relative_humidity",
+        "vapour_pressure_deficit",
+        "wind_speed",
+    ]:
         lower, upper, gradient = getattr(bounds, var)
 
         output[var] = log_interpolation(
-            data=data,
             reference_data=data[var + "_ref"].isel(time_index=time_index),
             leaf_area_index_sum=leaf_area_index_sum,
             layer_structure=layer_structure,
@@ -139,7 +145,6 @@ def run_microclimate(
 
 
 def log_interpolation(
-    data: Data,
     reference_data: DataArray,
     leaf_area_index_sum: DataArray,
     layer_structure: LayerStructure,
@@ -151,7 +156,6 @@ def log_interpolation(
     """LAI regression and logarithmic interpolation of variables above ground.
 
     Args:
-        data: Data object
         reference_data: Input variable at reference height
         leaf_area_index_sum: Leaf area index summed over all layers, [m m-1]
         layer_structure: The LayerStructure instance for the simulation.