def correct_radome_attenuation_empirical(gateset, frequency=5.64, hydrophobicity=0.165, n_r=2, stat=np.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 :cite:`Merceret2000`. Parameters ---------- gateset : :class:`numpy: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 : object A name of 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 : :class:`numpy: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. """ # Select rangebins inside the defined center-range n_r. center = gateset[..., :n_r].reshape(-1, n_r * gateset.shape[-2]) center_m = np.ma.masked_array(center, np.isnan(center)) # Calculate rainrate in the center-range based on statistical method stat # and with standard ZR-relation. rain_over_radome = zr.z_to_r(trafo.idecibel(stat(center_m, axis=-1))) # Estimate the empirical two-way transmission loss due to # radome-attenuation. k = 2 * hydrophobicity * rain_over_radome * np.tanh(frequency / 10.)**2 # Reshape the result to gateset-shape. k = np.repeat(k, gateset.shape[-1] * gateset.shape[-2]).reshape(gateset.shape) return k.filled(fill_value=np.nan)
def test_z_to_r(self): assert zr.z_to_r(trafo.idecibel(10.0)) == 0.1537645610180688
def test_z_to_r(self): self.assertEqual(zr.z_to_r(trafo.idecibel(10.)), 0.1537645610180688)
def test_z_to_r(self): self.assertEqual(zr.z_to_r(trafo.idecibel(10.)), 0.1537645610180688)