Reclass: Addition of New Radar Echo Classification Function

pyart/retrieve/echo_class.py

@@ -997,75 +997,75 @@ def conv_strat_raut(
     override_checks=False,
 ):
     """
-        A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions.
-
-        This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field,
-        focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020).
-
-        Parameters
-        ----------
-        grid : Grid
-            Grid object containing radar data.
-        refl_field : str
-            Field name for reflectivity data in the Py-ART grid object.
-        zr_a : float, optional
-            Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion.
-            The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however,
-            they must be adjusted based on the type of radar used.
-            Default is 200.
-        zr_b : float, optional
-            Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6.
-        core_wt_threshold : float, optional
-            Threshold for wavelet components to separate convective cores from mix-intermediate type.
-            Default is 5. Recommended values are between 4 and 6.
-        conv_wt_threshold : float, optional
-            Threshold for significant wavelet components to separate all convection from stratiform.
-            Default is 1.5. Recommended values are between 1 and 2.
-        conv_scale_km : float, optional
-            Approximate scale break (in km) between convective and stratiform scales.
-            Scale break may vary over different regions and seasons
-            (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the
-            algorithm is insensitive to small variations in the scale break due to the
-            dyadic nature of the scaling. The actual scale break used in the calculation of wavelets
-            is returned in the output dictionary by parameter `scale_break_used`.
-            Default is 25 km. Recommended values are between 16 and 32 km.
-        min_reflectivity : float, optional
-            Minimum reflectivity threshold. Reflectivities below this value are not classified.
-            Default is 5 dBZ. This value must be greater than or equal to '0'.
-        conv_min_refl : float, optional
-            Reflectivity values lower than this threshold will be always considered as non-convective.
-            Default is 25 dBZ. Recommended values are between 25 and 30 dBZ.
-        conv_core_threshold : float, optional
-            Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold).
-            Default is 42 dBZ.
-            Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value.
-        override_checks : bool, optional
-            If set to True, the function will bypass the sanity checks for above parameter values.
-            This allows the user to use custom values for parameters, even if they fall outside
-            the recommended ranges. The default is False.
-
-        Returns
-    -------
+    A fast method to classify radar echoes into convective cores, mixed convection, and stratiform regions.
+
+    This function uses à trous wavelet transform (ATWT) for multiresolution (scale) analysis of radar field,
+    focusing on precipitation structure over reflectivity thresholds for classification (Raut et al 2008, 2020).
+
+    Parameters
+    ----------
+    grid : Grid
+        Grid object containing radar data.
+    refl_field : str
+        Field name for reflectivity data in the Py-ART grid object.
+    zr_a : float, optional
+        Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion.
+        The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however,
+        they must be adjusted based on the type of radar used.
+        Default is 200.
+    zr_b : float, optional
+        Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6.
+    core_wt_threshold : float, optional
+        Threshold for wavelet components to separate convective cores from mix-intermediate type.
+        Default is 5. Recommended values are between 4 and 6.
+    conv_wt_threshold : float, optional
+        Threshold for significant wavelet components to separate all convection from stratiform.
+        Default is 1.5. Recommended values are between 1 and 2.
+    conv_scale_km : float, optional
+        Approximate scale break (in km) between convective and stratiform scales.
+        Scale break may vary over different regions and seasons
+        (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the
+        algorithm is insensitive to small variations in the scale break due to the
+        dyadic nature of the scaling. The actual scale break used in the calculation of wavelets
+        is returned in the output dictionary by parameter `scale_break_used`.
+        Default is 25 km. Recommended values are between 16 and 32 km.
+    min_reflectivity : float, optional
+        Minimum reflectivity threshold. Reflectivities below this value are not classified.
+        Default is 5 dBZ. This value must be greater than or equal to '0'.
+    conv_min_refl : float, optional
+        Reflectivity values lower than this threshold will be always considered as non-convective.
+        Default is 25 dBZ. Recommended values are between 25 and 30 dBZ.
+    conv_core_threshold : float, optional
+        Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold).
+        Default is 42 dBZ.
+        Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value.
+    override_checks : bool, optional
+        If set to True, the function will bypass the sanity checks for above parameter values.
+        This allows the user to use custom values for parameters, even if they fall outside
+        the recommended ranges. The default is False.
+
+    Returns
+-------
 
-        dict:
-        A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary
-        contains the classification data and associated metadata. The classification categories are as follows:
-            - 3: Convective Cores: associated with strong updrafts and active collision-coalescence.
-            - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores.
-            - 1: Stratiform: remaining areas with more uniform and less intense precipitation.
-            - 0: Unclassified: for reflectivity below the minimum threshold.
+    dict:
+    A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary
+    contains the classification data and associated metadata. The classification categories are as follows:
+        - 3: Convective Cores: associated with strong updrafts and active collision-coalescence.
+        - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores.
+        - 1: Stratiform: remaining areas with more uniform and less intense precipitation.
+        - 0: Unclassified: for reflectivity below the minimum threshold.
 
 
-        References
-        ----------
-        Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from
-        infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330.
+    References
+    ----------
+    Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from
+    infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330.
 
-        Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution
-        space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067.
+    Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution
+    space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067.
 
-        Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique
-        for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415.
+    Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique
+    for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415.
     """
 
     # Check if the grid is a Py-ART Grid object