"""Towerpy: an open-source toolbox for processing polarimetric radar data."""
import copy
import numpy as np
from scipy.interpolate import interp1d
from ..utils import unit_conversion as tpuc
from ..datavis import rad_display as rdd
[docs]
class Attn_Refl_Relation:
r"""
A class to compute the :math:`A_{H,V}(Z_{H,V})`.
Attributes
----------
elev_angle : float
Elevation angle at which the scan was taken, in deg.
file_name : str
Name of the file containing radar data.
scandatetime : datetime
Date and time of scan.
site_name : str
Name of the radar site.
"""
def __init__(self, radobj=None):
[docs]
self.elev_angle = getattr(radobj, 'elev_angle',
None) if radobj else None
[docs]
self.file_name = getattr(radobj, 'file_name',
None) if radobj else None
[docs]
self.scandatetime = getattr(radobj, 'scandatetime',
None) if radobj else None
[docs]
self.site_name = getattr(radobj, 'site_name',
None) if radobj else None
[docs]
def ah_zh(self, rad_vars, var2calc='ZH [dBZ]', rband='C', temp=20.,
coeff_a=None, coeff_b=None, zh_lower_lim=20., zh_upper_lim=50.,
rhohv_lim=0.95, copy_ofr=True, data2correct=None,
plot_method=False):
r"""
Compute the :math:`A_H-Z_H` relation.
Parameters
----------
rad_vars : dict
Radar object containing at least the specific attenuation
:math:`(A_H)` in dB/km, or the calibrated horizontal reflectivity
:math:`(Z_H)` in dBZ, that will be used for calculations.
var2calc : str
Radar variable to be computed. The string has to be one of
'AH [dB/km]' or 'ZH [dBZ]'. The default is 'ZH [dBZ]'.
rband: str
Frequency band according to the wavelength of the radar.
The string has to be one of 'C' or 'X'. The default is 'C'.
temp: float
Temperature, in :math:`^{\circ}C`, used to derive the coefficients
according to [1]_. The default is 20.
coeff_a, coeff_b: float
Override the default coefficients of the :math:`A_H(Z_H)`
relationship. The default are None.
zh_lower_lim, zh_upper_lim : floats
Thresholds in :math:`Z_H` for the :math:`A_H(Z_H)` relationship.
Default is :math:`20 < Z_H < 50 dBZ`.
rhohv_lim : float
Threshold in :math:`\rho_{HV}` to filter out invalid values.
Default is 0.95.
copy_ofr : bool, optional
If True, original values are used to populate out-of-range values,
i.e., values below or above zh_limits. The default is True.
data2correct : dict, optional
Dictionary that will be updated with the computed variable.
The default is None.
plot_method : bool, optional
Plot the :math:`A_H-Z_H` relation. The default is False.
Returns
-------
vars : dict
AH [dB/km]:
Specific attenuation at horizontal polarisation.
ZH [dBZ]:
Reflectivity at horizontal polarisation not affected by partial
beam blockage, radar miscalibration or the impact of wet radom.
coeff_a, coeff_b:
Interpolated coefficients of the :math:`A_H(Z_H)` relation.
Math
----
.. [Eq.1]
.. math:: A_H = aZ_h^b
where :math:`Z_h = 10^{0.1*Z_H}`, :math:`Z_H` in dBZ and
:math:`A_H` in dB/km.
Notes
-----
Standard values according to [1]_
References
----------
.. [1] Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., & Trömel, S.
(2015). Use of Specific Attenuation for Rainfall Measurement at X-Band
Radar Wavelengths. Part I: Radar Calibration and Partial Beam Blockage
Estimation. Journal of Hydrometeorology, 16(2), 487-502.
https://doi.org/10.1175/JHM-D-14-0066.1
"""
if coeff_a is None and coeff_b is None:
# Default values for the temp
temps = np.array((0, 10, 20, 30), dtype=np.float64)
# Default values for C- and X-band radars
coeffs_a = {'X': np.array((1.62e-4, 1.15e-4, 7.99e-5, 5.5e-5),
dtype=np.float64),
'C': np.array((4.27e-5, 2.89e-5, 2.09e-5, 1.59e-5),
dtype=np.float64)}
coeffs_b = {'X': np.array((0.74, 0.78, 0.82, 0.86),
dtype=np.float64),
'C': np.array((0.73, 0.75, 0.76, 0.77),
dtype=np.float64)}
# Interpolate the temp, and coeffs to set the coeffs
icoeff_a = interp1d(temps, coeffs_a.get(rband))
icoeff_b = interp1d(temps, coeffs_b.get(rband))
coeff_a = icoeff_a(temp)
coeff_b = icoeff_b(temp)
if var2calc == 'ZH [dBZ]':
# Copy the original dict to keep variables unchanged
rvars = copy.deepcopy(rad_vars)
# Computes Zh (linear units)
r_ahzhl = (((1 / coeff_a)**(1/coeff_b))
* rvars['AH [dB/km]'] ** (1 / coeff_b))
r_ahzh = {}
# Computes ZH (dBZ)
r_ahzh['ZH [dBZ]'] = tpuc.x2xdb(r_ahzhl)
# Filter values using a lower limit
r_ahzh['ZH [dBZ]'][rvars['ZH [dBZ]'] < zh_lower_lim] = np.nan
# Filter values using an upper limit
r_ahzh['ZH [dBZ]'][rvars['ZH [dBZ]'] > zh_upper_lim] = np.nan
# Filter invalid values
r_ahzh['ZH [dBZ]'][rvars['rhoHV [-]'] < rhohv_lim] = np.nan
if copy_ofr and 'ZH [dBZ]' in rad_vars.keys():
# Filter invalid values
# Use original values to populate with out-of-range values.
ind = np.isneginf(r_ahzh['ZH [dBZ]'])
r_ahzh['ZH [dBZ]'][ind] = rvars['ZH [dBZ]'][ind]
ind = np.isnan(r_ahzh['ZH [dBZ]'])
r_ahzh['ZH [dBZ]'][ind] = rvars['ZH [dBZ]'][ind]
zh_diff = (tpuc.xdb2x(rad_vars['ZH [dBZ]'])
/ tpuc.xdb2x(r_ahzh['ZH [dBZ]']))
zh_diff[np.isinf(zh_diff)] = np.nan
zh_diffdbZ = tpuc.x2xdb(zh_diff)
r_ahzh['diff [dBZ]'] = zh_diffdbZ
if var2calc == 'AH [dB/km]':
# Copy the original dict to keep variables unchanged
rvars = copy.deepcopy(rad_vars)
# Filter values using a lower limit
rvars['ZH [dBZ]'][rad_vars['ZH [dBZ]'] < zh_lower_lim] = np.nan
# Filter values using an upper limit
rvars['ZH [dBZ]'][rad_vars['ZH [dBZ]'] > zh_upper_lim] = np.nan
# Filter invalid values
rvars['ZH [dBZ]'][rvars['rhoHV [-]'] < rhohv_lim] = np.nan
r_ahzh = {}
r_ahzh['AH [dB/km]'] = (
coeff_a * (tpuc.xdb2x(rvars['ZH [dBZ]']) ** coeff_b))
# Filter invalid values
# ind = np.isneginf(r_ahzh['AH [dB/km]'])
# r_ahzh['AH [dB/km]'][ind] = rad_vars['ZH [dBZ]'][ind]
# # Filter invalid values
# ind = np.isnan(r_ahzh['ZH [dBZ]'])
# r_ahzh['ZH [dBZ]'][ind] = rad_vars['ZH [dBZ]'][ind]
if copy_ofr and 'AH [dB/km]' in rad_vars.keys():
# Filter invalid values
# Use original values to populate with out-of-range values.
ind = np.isneginf(r_ahzh['AH [dB/km]'])
r_ahzh['AH [dB/km]'][ind] = rvars['AH [dB/km]'][ind]
ind = np.isnan(r_ahzh['AH [dB/km]'])
r_ahzh['AH [dB/km]'][ind] = rvars['AH [dB/km]'][ind]
ah_diff = rad_vars['AH [dB/km]'] - r_ahzh['AH [dB/km]']
ah_diff[np.isinf(ah_diff)] = np.nan
r_ahzh['diff [dB/km]'] = ah_diff
self.vars = r_ahzh
self.coeff_a = coeff_a
self.coeff_b = coeff_b
if data2correct is not None:
# Copy the original dict to keep variables unchanged
data2cc = copy.deepcopy(data2correct)
data2cc.update(r_ahzh)
self.vars = data2cc
if plot_method:
if var2calc == 'ZH [dBZ]':
rdd.plot_zhah(rad_vars, r_ahzh, temp, coeff_a, coeff_b,
coeffs_a.get(rband), coeffs_b.get(rband), temps,
zh_lower_lim, zh_upper_lim)
[docs]
def av_zv(self, rad_vars, var2calc='ZV [dBZ]', rband='C', temp=10.,
coeff_a=None, coeff_b=None, zv_lower_lim=20., zv_upper_lim=50.,
rhohv_lim=0.95, copy_ofr=True, data2correct=None,
plot_method=False):
r"""
Compute the :math:`A_V-Z_V` relation.
Parameters
----------
rad_vars : dict
Radar object containing at least the specific attenuation
:math:`(A_V)` in dB/km, or the calibrated vertical reflectivity
:math:`(Z_V)` in dBZ, that will be used for calculations.
var2calc : str
Radar variable to be computed. The string has to be one of
'AV [dB/km]' or 'ZV [dBZ]'. The default is 'ZV [dBZ]'.
rband: str
Frequency band according to the wavelength of the radar.
The string has to be one of 'C' or 'X'. The default is 'C'.
temp: float
Temperature, in :math:`^{\circ}C`, used to derive the coefficients
according to [1]_. The default is 10.
coeff_a, coeff_b: float
Override the default coefficients of the :math:`A_V(Z_V)`
relationship. The default are None.
zv_lower_lim, zv_upper_lim : floats
Thresholds in :math:`Z_V` for the :math:`A_V(Z_V)` relationship.
Default is :math:`20 < Z_V < 50 dBZ`.
rhohv_lim : float
Threshold in :math:`\rho_{HV}` to filter out invalid values.
Default is 0.95.
copy_ofr : bool, optional
If True, original values are used to populate out-of-range values,
i.e., values below or above zv_limits. The default is True.
data2correct : dict, optional
Dictionary that will be updated with the computed variable.
The default is None.
plot_method : bool, optional
Plot the :math:`A_V-Z_V` relation. The default is False.
Returns
-------
vars : dict
AV [dB/km]:
Specific attenuation at vertical polarisation.
ZV [dBZ]:
Reflectivity at vertical polarisation not affected by partial
beam blockage, radar miscalibration or the impact of wet radom.
coeff_a, coeff_b:
Interpolated coefficients of the :math:`A_V(Z_V)` relation.
Math
----
.. [Eq.1]
.. math:: A_V = aZ_v^b
where :math:`Z_v = 10^{0.1*Z_V}`, :math:`Z_V` in dBZ and
:math:`A_V` in dB/km.
Notes
-----
Standard values according to [1]_
References
----------
.. [1] Diederich, M., Ryzvkov, A., Simmer, C., Zvang, P., & Trömel, S.
(2015). Use of Specific Attenuation for Rainfall Measurement at X-Band
Radar Wavelengths. Part I: Radar Calibration and Partial Beam Blockage
Estimation. Journal of Hydrometeorology, 16(2), 487-502.
https://doi.org/10.1175/JHM-D-14-0066.1
"""
if coeff_a is None and coeff_b is None:
# Default values for the temp
temps = np.array((0, 10, 20, 30), dtype=np.float64)
# Default values for C- and X-band radars
coeffs_a = {'X': np.array((1.35e-4, 9.47e-5, 6.5e-5, 4.46e-5),
dtype=np.float64),
'C': np.array((3.87e-5, 2.67e-5, 1.97e-5, 1.53e-5),
dtype=np.float64)}
coeffs_b = {'X': np.array((0.78, 0.82, 0.86, 0.89),
dtype=np.float64),
'C': np.array((0.75, 0.77, 0.78, 0.78),
dtype=np.float64)}
# Interpolate the temp, and coeffs to set the coeffs
icoeff_a = interp1d(temps, coeffs_a.get(rband))
icoeff_b = interp1d(temps, coeffs_b.get(rband))
coeff_a = icoeff_a(temp)
coeff_b = icoeff_b(temp)
if var2calc == 'ZV [dBZ]':
# Copy the original dict to keep variables unchanged
rvars = copy.deepcopy(rad_vars)
# Computes Zv
r_avzvl = (((1 / coeff_a)**(1/coeff_b))
* rvars['AV [dB/km]'] ** (1 / coeff_b))
r_avzv = {}
# Computes ZV
r_avzv['ZV [dBZ]'] = tpuc.x2xdb(r_avzvl)
# Filter values using a lower limit
r_avzv['ZV [dBZ]'][rvars['ZV [dBZ]'] < zv_lower_lim] = np.nan
# Filter values using an upper limit
r_avzv['ZV [dBZ]'][rvars['ZV [dBZ]'] > zv_upper_lim] = np.nan
# Filter invalid values
r_avzv['ZV [dBZ]'][rvars['rhoHV [-]'] < rhohv_lim] = np.nan
if copy_ofr and 'ZV [dBZ]' in rad_vars.keys():
# Filter invalid values
# Use original values to populate with out-of-range values.
ind = np.isneginf(r_avzv['ZV [dBZ]'])
r_avzv['ZV [dBZ]'][ind] = rvars['ZV [dBZ]'][ind]
ind = np.isnan(r_avzv['ZV [dBZ]'])
r_avzv['ZV [dBZ]'][ind] = rvars['ZV [dBZ]'][ind]
zv_diff = (tpuc.xdb2x(rad_vars['ZV [dBZ]'])
/ tpuc.xdb2x(r_avzv['ZV [dBZ]']))
zv_diff[np.isinf(zv_diff)] = np.nan
zv_diffdbZ = tpuc.x2xdb(zv_diff)
r_avzv['diff [dBZ]'] = zv_diffdbZ
if var2calc == 'AV [dB/km]':
# Copy the original dict to keep variables unchanged
rvars = copy.deepcopy(rad_vars)
# Filter values using a lower limit
rvars['ZV [dBZ]'][rad_vars['ZV [dBZ]'] < zv_lower_lim] = np.nan
# Filter values using an upper limit
rvars['ZV [dBZ]'][rad_vars['ZV [dBZ]'] > zv_upper_lim] = np.nan
# Filter invalid values
rvars['ZV [dBZ]'][rvars['rhoHV [-]'] < rhohv_lim] = np.nan
r_avzv = {}
r_avzv['AV [dB/km]'] = (
coeff_a * (tpuc.xdb2x(rvars['ZV [dBZ]']) ** coeff_b))
# Filter invalid values
# ind = np.isneginf(r_avzv['AV [dB/km]'])
# r_avzv['AV [dB/km]'][ind] = rad_vars['ZV [dBZ]'][ind]
# # Filter invalid values
# ind = np.isnan(r_avzv['ZV [dBZ]'])
# r_avzv['ZV [dBZ]'][ind] = rad_vars['ZV [dBZ]'][ind]
if copy_ofr and 'AV [dB/km]' in rad_vars.keys():
# Filter invalid values
# Use original values to populate with out-of-range values.
ind = np.isneginf(r_avzv['AV [dB/km]'])
r_avzv['AV [dB/km]'][ind] = rvars['AV [dB/km]'][ind]
ind = np.isnan(r_avzv['AV [dB/km]'])
r_avzv['AV [dB/km]'][ind] = rvars['AV [dB/km]'][ind]
av_diff = rad_vars['AV [dB/km]'] - r_avzv['AV [dB/km]']
av_diff[np.isinf(av_diff)] = np.nan
r_avzv['diff [dB/km]'] = av_diff
self.vars = r_avzv
self.coeff_a = coeff_a
self.coeff_b = coeff_b
if data2correct is not None:
# Copy the original dict to keep variables unchanged
data2cc = copy.deepcopy(data2correct)
data2cc.update(r_avzv)
self.vars = data2cc
if plot_method:
rdd.plot_zhah(rad_vars, temp, coeff_a, coeff_b,
coeffs_a.get(rband), coeffs_b.get(rband), temps)
