wradlib.classify.ClassifyMethods.classify_echo_fuzzy#

ClassifyMethods.classify_echo_fuzzy(dat, **kwargs)[source]#

Fuzzy echo classification and clutter identification based on polarimetric moments.

The implementation is based on [Vulpiani et al., 2012]. At the moment, it only distinguishes between meteorological and non-meteorological echos.

For Clutter Phase Alignment (CPA) see [Hubbert et al., 2009a] and [Hubbert et al., 2009b]

For each decision variable and radar bin, the algorithm uses trapezoidal functions in order to define the membership to the non-meteorological echo class. Based on pre-defined weights, a linear combination of the different degrees of membership is computed. The echo is assumed to be non-meteorological in case the linear combination exceeds a threshold.

At the moment, the following decision variables are considered:

  • Texture of differential reflectivity (zdr) (mandatory)

  • Texture of correlation coefficient (rho) (mandatory)

  • Texture of differential propagation phase (phidp) (mandatory)

  • Doppler velocity (dop) (mandatory)

  • Static clutter map (map) (mandatory)

  • Correlation coefficient (rho2) (additional)

  • Depolarization Ratio (dr), computed from correlation coefficient & differential reflectivity (additional)

  • clutter phase alignment (cpa) (additional)

Parameters
Keyword Arguments

  • weights (dict) – dictionary of floats. Defines the weights of the decision variables. Default is: zdr: 0.4, rho: 0.4, phi: 0.1, dop: 0.1, map: 0.5, rho2: 0.4, dr: 0.4, cpa: 0.4.

  • trpz (dict) – dictionary of lists of floats. Contains the arguments of the trapezoidal membership functions for each decision variable. Default is: zdr: [0.7, 1.0, 9999, 9999], rho: [0.1, 0.15, 9999, 9999], phi: [15, 20, 10000, 10000], dop: [-0.2, -0.1, 0.1, 0.2], map: [1, 1, 9999, 9999], rho2: [-9999, -9999, 0.95, 0.98], dr: [-20, -12, 9999, 9999], cpa: [0.6, 0.9, 9999, 9999].

Returns

  • prob (xarray.DataArray) – DataArray indicating probability of meteorological echos based on the fuzzy classification.

  • mask (xarray.DataArray) – DataArray indicating where all the polarimetric moments had missing values which could be used as an additional information criterion.

See also

texture, depolarization