wradlib.atten.correct_radome_attenuation_empirical(gateset, *, frequency=5.64, hydrophobicity=0.165, n_r=2, stat=<function mean>)[source]#

Estimate two-way wet radome losses.

Empirical function of frequency and rainfall rate for both standard and hydrophobic radomes based on the approach of [Merceret et al., 2000].

  • gateset (numpy.ndarray) – Multidimensional array, where the range gates (over which iteration has to be performed) are supposed to vary along the last array-dimension and the azimuths are supposed to vary along the next to last array-dimension. Data has to be provided in decibel representation of reflectivity [dBZ].

  • frequency (float) –

    Radar-frequency [GHz]:

    Standard frequencies in X-band range between 8.0 and 12.0 GHz,

    Standard frequencies in C-band range between 4.0 and 8.0 GHz,

    Standard frequencies in S-band range between 2.0 and 4.0 GHz.

    Be aware that the empirical fit of the formula was just done for C- and S-band. The use for X-band is probably an undue extrapolation.

    Per default set to 5.64 as used by the German Weather Service radars.

  • hydrophobicity (float) – Empirical parameter based on the hydrophobicity of the radome material.

    • 0.165 for standard radomes,

    • 0.0575 for hydrophobic radomes.

    Per default set to 0.165.

  • n_r (int) – The radius of rangebins within the rain-intensity is statistically evaluated as the representative rain-intensity over radome.

  • stat (callable) – A numpy function for statistical aggregation of the central rangebins defined by n_r.

    Potential options: numpy.mean, numpy.median, numpy.max, numpy.min.


k (numpy.ndarray) – Array with the same shape as gateset containing the calculated two-way transmission loss [dB] for each range gate. In case the input array (gateset) contains NaNs the corresponding beams of the output array (k) will be set as NaN, too.