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)