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MINOR:corrected tab in docstring
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@RBhupi
RBhupi committed Dec 18, 2023
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128 changes: 64 additions & 64 deletions pyart/retrieve/echo_class.py
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Expand Up @@ -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
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