- ClassifyMethods.classify_echo_fuzzy(dat, **kwargs)#
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 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)
- Keyword Arguments
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.
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].