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pyart.retrieve.conv_strat_raut#

pyart.retrieve.conv_strat_raut(grid, refl_field, cappi_level=0, zr_a=200, zr_b=1.6, core_wt_threshold=5, conv_wt_threshold=1.5, conv_scale_km=25, min_reflectivity=5, conv_min_refl=25, conv_core_threshold=42, override_checks=False)[source]#

A computationally efficient method to classify radar echoes into convective cores, mixed convection, and stratiform regions for gridded radar reflectivity field.

This function uses à trous wavelet transform (ATWT) for multiresolution (i.e. scale) analysis of radar field, focusing on precipitation structure over reflectivity thresholds for robust echo classification (Raut et al 2008, 2020).

Parameters:

  • grid (PyART 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.

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., 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.

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