def test_z_to_r_enhanced(self):
res_rr, res_si = zr.z_to_r_enhanced(trafo.idecibel(self.img))
res_rr2, res_si2 = zr.z_to_r_enhanced(trafo.idecibel(self.img),
algo='mdfilt', mode='mirror')
res_rr3, res_si3 = zr.z_to_r_enhanced(trafo.idecibel(self.img),
algo='mdcorr', xmode='mirror',
ymode='mirror')
rr = np.array([[3.64633237e-02, 1.77564547e-01, 1.77564547e-01,
3.17838962e-02, 3.17838962e-02, 1.62407903e-02,
2.37427600e+01, 2.37427600e+01, 2.37427600e+01,
1.62407903e-02, 3.17838962e-02],
[1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02, 1.62407903e-02,
1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02],
[1.62407903e-02, 8.64681611e+00, 8.64681611e+00,
1.62407903e-02, 3.17838962e-02, 3.17838962e-02,
4.41635812e-02, 4.41635812e-02, 4.41635812e-02,
3.17838962e-02, 3.17838962e-02],
[1.62407903e-02, 3.69615367e-02, 3.69615367e-02,
3.69615367e-02, 7.23352513e-02, 7.23352513e-02,
7.23352513e-02, 3.17838962e-02, 3.17838962e-02,
3.17838962e-02, 3.17838962e-02],
[3.64633237e-02, 3.64633237e-02, 3.64633237e-02,
3.17838962e-02, 3.17838962e-02, 1.62407903e-02,
1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02]])
si = np.array([[4.71428575, 4.58333337, 4.58333337, 2.75000002,
0., 11.25000003, -1., -1.,
-1., 11.25000003, 0.],
[13.99999989, 12.2499999, 12.2499999, 8.1666666,
0., 7.83333337, 11.75000005, 11.75000005,
11.75000005, 7.83333337, 0.],
[16.28571408, -1., -1., 9.91666655,
1.25000001, 1.41666669, 1.75000004, 1.50000004,
0.83333337, 0.50000002, 0.],
[11.57142844, 9.91666655, 10.33333322, 7.99999994,
2.50000003, 2.50000003, 2.25000003, 1.41666669,
0.50000002, 0.33333335, 0.],
[4.57142861, 4.00000004, 4.00000004, 3.08333336,
1.25000001, 8.75000003, 12.50000004, 12.08333337,
11.25000003, 7.50000002, 0.]])
np.testing.assert_allclose(rr, res_rr)
np.testing.assert_allclose(rr, res_rr2)
np.testing.assert_allclose(rr, res_rr3)
self.assertTrue(np.allclose(si, res_si))
def calc_attenuation_backward(gateset, a, b, gate_length,
a_ref, tdiff, maxiter):
"""Gate-by-Gate backward correction as described in
:cite:`Kraemer2008`"""
k = np.zeros(gateset.shape)
k[..., -1] = a_ref
for gate in range(gateset.shape[-1] - 2, 0, -1):
kright = np.zeros(gateset.shape[:-1]) + a_ref / gateset.shape[-1]
toprocess = np.ones(gateset.shape[:-1], dtype=np.bool)
for j in range(maxiter):
kleft = a * (idecibel(gateset[..., gate][toprocess] +
k[..., gate + 1][toprocess] -
kright[toprocess])) ** b * 2.0 * gate_length
diff = np.abs(kleft - kright)
kright[toprocess] = kleft
toprocess[diff < tdiff] = False
if ~np.any(toprocess):
break
if j == maxiter - 1:
raise AttenuationIterationError
k[..., gate] = k[..., gate + 1] - kright
# k = np.cumsum(k, axis=-1)
return k
def depolarization(zdr, rho):
"""Compute the depolarization ration.
Compute the depolarization ration using differential
reflectivity :math:`Z_{DR}` and crosscorrelation coefficient
:math:`Rho_{HV}` of a radar sweep (:cite:`Kilambi2018`,
:cite:`Melnikov2013`, :cite:`Ryzhkov2017`).
Parameters
----------
zdr : float or :class:`numpy:numpy.ndarray`
differential reflectivity
rho : float or :class:`numpy:numpy.ndarray`
crosscorrelation coefficient
Returns
------
depolarization : :class:`numpy:numpy.ndarray`
array of depolarization ratios with the same shape as input data,
numpy broadcasting rules apply
"""
zdr = trafo.idecibel(np.asanyarray(zdr))
m = 2 * np.asanyarray(rho) * zdr**0.5
return trafo.decibel((1 + zdr - m) / (1 + zdr + m))
def calc_attenuation_forward(gateset, a=1.67e-4, b=0.7, l=1.):
"""Gate-by-Gate forward correction as described in Kraemer :cite:`Kraemer2008`"""
pia = np.zeros(gateset.shape)
for gate in range(gateset.shape[-1] - 1):
k = a * idecibel(gateset[..., gate] + pia[..., gate])**b * 2.0 * l
pia[..., gate + 1] = pia[..., gate] + k
return pia
def calc_attenuation_forward(gateset, a=1.67e-4, b=0.7, gate_length=1.):
"""Gate-by-Gate forward correction as described in :cite:`Kraemer2008`
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.
Data has to be provided in decibel representation of reflectivity
[dBZ].
a : float
proportionality factor of the k-Z relation (:math:`k=a \\cdot Z^{b}`).
Per default set to 1.67e-4.
b : float
exponent of the k-Z relation ( :math:`k=a \\cdot Z^{b}` ). Per default
set to 0.7.
gate_length : float
length of a range gate [km]. Per default set to 1.0.
Returns
-------
pia : :class:`numpy:numpy.ndarray`
Array with the same shape as ``gateset`` containing the calculated path
integrated attenuation [dB] for each range gate.
"""
pia = np.zeros(gateset.shape)
for gate in range(gateset.shape[-1] - 1):
k = a * trafo.idecibel(gateset[..., gate] + pia[..., gate]) ** b \
* 2.0 * gate_length
pia[..., gate + 1] = pia[..., gate] + k
return pia
def test_z_to_r_enhanced(self):
res_rr, res_si = zr.z_to_r_enhanced(trafo.idecibel(self.img),
polar=True)
res_rr2, res_si2 = zr.z_to_r_enhanced(trafo.idecibel(self.img[0]),
polar=True)
rr = np.array([[3.64633237e-02, 1.77564547e-01, 1.77564547e-01,
3.17838962e-02, 3.17838962e-02, 1.62407903e-02,
2.37427600e+01, 2.37427600e+01, 2.37427600e+01,
1.62407903e-02, 3.17838962e-02],
[1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02, 1.62407903e-02,
1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02],
[1.62407903e-02, 8.64681611e+00, 8.64681611e+00,
1.62407903e-02, 3.17838962e-02, 3.17838962e-02,
4.41635812e-02, 4.41635812e-02, 4.41635812e-02,
3.17838962e-02, 3.17838962e-02],
[1.62407903e-02, 3.69615367e-02, 3.69615367e-02,
3.69615367e-02, 7.23352513e-02, 7.23352513e-02,
7.23352513e-02, 3.17838962e-02, 3.17838962e-02,
3.17838962e-02, 3.17838962e-02],
[3.64633237e-02, 3.64633237e-02, 3.64633237e-02,
3.17838962e-02, 3.17838962e-02, 1.62407903e-02,
1.62407903e-02, 1.62407903e-02, 1.62407903e-02,
1.62407903e-02, 3.17838962e-02]])
si = np.array([[4.71428575, 4.58333337, 4.58333337, 2.75000002,
0., 11.25000003, -1., -1.,
-1., 11.25000003, 0.],
[13.99999989, 12.2499999, 12.2499999, 8.1666666,
0., 7.83333337, 11.75000005, 11.75000005,
11.75000005, 7.83333337, 0.],
[16.28571408, -1., -1., 9.91666655,
1.25000001, 1.41666669, 1.75000004, 1.50000004,
0.83333337, 0.50000002, 0.],
[11.57142844, 9.91666655, 10.33333322, 7.99999994,
2.50000003, 2.50000003, 2.25000003, 1.41666669,
0.50000002, 0.33333335, 0.],
[4.57142861, 4.00000004, 4.00000004, 3.08333336,
1.25000001, 8.75000003, 12.50000004, 12.08333337,
11.25000003, 7.50000002, 0.]])
np.testing.assert_almost_equal(si, res_si[0], decimal=6)
np.testing.assert_array_almost_equal(rr, res_rr[0], decimal=6)
np.testing.assert_almost_equal(si, res_si2, decimal=6)
np.testing.assert_array_almost_equal(rr, res_rr2, decimal=6)
def calc_attenuation_backward(gateset, a, b, l, a_ref, tdiff, maxiter):
"""Gate-by-Gate backward correction as described in Kraemer :cite:`Kraemer2008`"""
k = np.zeros(gateset.shape)
k[...,-1] = a_ref
for gate in range(gateset.shape[-1]-2, 0, -1):
kright = np.zeros(gateset.shape[:-1])+a_ref/gateset.shape[-1]
toprocess = np.ones(gateset.shape[:-1], dtype=np.bool)
for j in range(maxiter):
kleft = a * (idecibel(gateset[...,gate][toprocess]
+ k[...,gate+1][toprocess]
- kright[toprocess]))**b * 2.0 * l
diff = np.abs(kleft-kright)
kright[toprocess] = kleft
toprocess[diff<tdiff] = False
if ~np.any(toprocess):
break
if j == maxiter-1:
raise AttenuationIterationError
k[...,gate] = k[...,gate+1] - kright
#k = np.cumsum(k, axis=-1)
return k
def test_z2r(self):
self.assertEqual(zr.z2r(trafo.idecibel(10.)), 0.1537645610180688)
def test_z_to_r(self):
self.assertEqual(zr.z_to_r(trafo.idecibel(10.)), 0.1537645610180688)
def test_idecibel(self):
assert np.allclose(trafo.idecibel(self.dec), self.lin)
def test_z_to_r(self):
assert zr.z_to_r(trafo.idecibel(10.0)) == 0.1537645610180688
def test_idecibel(self):
self.assertTrue(np.allclose(trafo.idecibel(self.dec), self.lin))
blon, blat, dpr_typ_b = cut_the_swath(gprof_lon,gprof_lat,dpr_typ,eu=0)
blon, blat, dpr_top_b = cut_the_swath(gprof_lon,gprof_lat,dpr_top,eu=0)
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
gpm_x, gpm_y = wradlib.georef.reproject(blon, blat, projection_target=proj_stereo , projection_source=proj_wgs)
grid_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
####################################### Neu
#rwdata2[rwdata2 <= 15] = -9999
rwdata2 = idecibel(rwdata2)
############################################## INTERLOLATION RY
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
xy = np.vstack((x.ravel(), y.ravel())).transpose()
#mask = ~np.isnan(rwdata)
result = wrl.ipol.interpolate(xy, grid_gpm_xy, rwdata.reshape(900*900,1), wrl.ipol.Idw, nnearest=8)
def correctRadomeAttenuationEmpirical(gateset,
frequency=5.64,
hydrophobicity=0.165,
n_r=2,
stat=np.mean):
"""Estimate two-way wet radome losses as an empirical
function of frequency and rainfall rate for both standard and
hydrophobic radomes based on the approach of :cite:`Merceret2000`.
Parameters
----------
gateset : array
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 : integer
The radius of rangebins within the rain-intensity is
statistically evaluated as the representative rain-intensity
over radome.
stat : class
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 : array
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 = z2r(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
blon, blat, gprof_pp_b = cut_the_swath(gprof_lon,
gprof_lat,
gprof_pp,
eu=0)
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
gpm_x, gpm_y = wradlib.georef.reproject(blon,
blat,
projection_target=proj_stereo,
projection_source=proj_wgs)
grid_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
rwdata = idecibel(rwdata)
## INTERLOLATION
## --------------
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
xy = np.vstack((x.ravel(), y.ravel())).transpose()
mask = ~np.isnan(rwdata)
result = wrl.ipol.interpolate(xy,
grid_gpm_xy,
rwdata.reshape(900 * 900, 1),
def test_idecibel(self):
self.assertTrue(np.allclose(trafo.idecibel(self.dec), self.lin))
## Cut the GPM Swath
## ------------------
blon, blat, gprof_pp_b = cut_the_swath(gprof_lon,gprof_lat,gprof_pp,eu=0)
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
gpm_x, gpm_y = wradlib.georef.reproject(blon, blat, projection_target=proj_stereo , projection_source=proj_wgs)
grid_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
rwdata = idecibel(rwdata)
## INTERLOLATION
## --------------
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
xy = np.vstack((x.ravel(), y.ravel())).transpose()
mask = ~np.isnan(rwdata)
result = wrl.ipol.interpolate(xy, grid_gpm_xy, rwdata.reshape(900*900,1), wrl.ipol.Idw, nnearest=4)
proj_stereo = wrl.georef.create_osr("dwd-radolan")
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
gpm_x, gpm_y = wradlib.georef.reproject(
blon,
blat,
projection_target=proj_stereo,
projection_source=proj_wgs)
grid_xy = np.vstack((gpm_x.ravel(), gpm_y.ravel())).transpose()
####################################### Neu
#rwdata2[rwdata2 <= 15] = -9999
rwdata2 = idecibel(rwdata2)
############################################## INTERLOLATION RY
gk3 = wradlib.georef.epsg_to_osr(31467)
grid_gpm_xy = np.vstack(
(gpm_x.ravel(), gpm_y.ravel())).transpose()
xy = np.vstack((x.ravel(), y.ravel())).transpose()
#mask = ~np.isnan(rwdata)
result = wrl.ipol.interpolate(xy,
grid_gpm_xy,
rwdata.reshape(900 * 900, 1),
def correctRadomeAttenuationEmpirical(gateset, frequency=5.64,
hydrophobicity=0.165, n_r=2,
stat=np.mean):
"""Estimate two-way wet radome losses as an empirical
function of frequency and rainfall rate for both standard and
hydrophobic radomes based on the approach of Francis J. Merceret
and Jennifer G. Ward :cite:`Merceret2000`.
Parameters
----------
gateset : array
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 : integer
The radius of rangebins within the rain-intensity is
statistically evaluated as the representative rain-intensity
over radome.
stat : class
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 : array
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 = z2r(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
r = attrs['SCAN0']['r']
az = attrs['SCAN0']['az']
lon_ppi = attrs['VOL']['Longitude']
lat_ppi = attrs['VOL']['Latitude']
alt_ppi = attrs['VOL']['Height']
data,attributes=wrl.io.read_GAMIC_hdf5('/automount/radar-archiv/scans/2014/2014-10/2014-10-07/ppi_1p5deg/2014-10-07--02:37:44,00.mvol')
Ah,ZH,ZH_corr,phidp,phidp_est,deltaphidp,r1,r2 = ZPHI_Method(data,attributes)
R = ZH_corr
from wradlib.trafo import idecibel, decibel
R = idecibel(R)
#R = R + pia
R[151:165]=np.nan
#R[np.where(rho<0.95)]=np.nan
R[rho<=0.8]=np.nan
# DPR Einlesen
# ------------
gpmku = h5py.File(pfad_radar_Ku, 'r')
gpmku_HS=gpmku['NS']['SLV']
ku_lat=np.array(gpmku['NS']['Latitude']) #(7934, 24)
ku_lon=np.array(gpmku['NS']['Longitude']) #(7934, 24)
ku_pp=np.array(gpmku_HS['zFactorCorrectedNearSurface'])
# Dpr zuschneiden
#-----------------
lon0, lat0, radius = blon, blat, 100
rr = np.sqrt((dpr_lat - lat0)**2 + (dpr_lon - lon0)**2)
position = rr < radius
pp = dpr_pp.copy()
pp[np.where(rr > radius)] = np.nan
from wradlib.trafo import idecibel
from wradlib.trafo import decibel
R = idecibel(R)
radar_location = (lon_ppi, lat_ppi, alt_ppi)
elevation = 1.5
azimuths = az
ranges = r
polargrid = np.meshgrid(ranges, azimuths)
lon, lat, alt = wradlib.georef.polar2lonlatalt_n(polargrid[0], polargrid[1],
elevation, radar_location)
lon, lat = wradlib.georef.reproject(lon,
lat,
projection_target=proj_stereo,
projection_source=proj_wgs)
grid_xy = np.vstack((dpr_lon.ravel(), dpr_lat.ravel())).transpose()
