wradlib.atten.correct_radome_attenuation_empirical¶
- wradlib.atten.correct_radome_attenuation_empirical(gateset, frequency=5.64, hydrophobicity=0.165, n_r=2, stat=<function mean>)¶
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].
- Parameters
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: np.mean, np.median, np.max, np.min.
- Returns
k (
numpy.ndarray
) – Array with the same shape asgateset
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.