def get_radar_variables_Exponential_fwScattering(N0=None, Lambda=None, D_max=None): scatterer.psd = psd.ExponentialPSD(N0=N0, Lambda=Lambda, D_max=D_max) return [ radar.Ai(scatterer, h_pol=True), radar.Ai(scatterer, h_pol=False), radar.Kdp(scatterer) ]
def get_radar_variables_unnormalizedGamma_fwScattering(N0=None, Lambda=None, mu=None, D_max=None): scatterer.psd = psd.UnnormalizedGammaPSD(N0=N0, Lambda=Lambda, mu=mu, D_max=D_max) return [ radar.Ai(scatterer, h_pol=True), radar.Ai(scatterer, h_pol=False), radar.Kdp(scatterer) ]
def calcParameters(self,D0, Nw, mu): self.moments = {} self.scatterer.psd = GammaPSD(D0=D0, Nw=10**(Nw), mu=mu) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_back) self.moments['Zh'] = 10*np.log10(radar.refl(self.scatterer)) self.moments['Zdr'] = 10*np.log10(radar.Zdr(self.scatterer)) self.moments['delta_hv'] = radar.delta_hv(self.scatterer) self.moments['ldr_h'] = radar.ldr(self.scatterer) self.moments['ldr_v'] = radar.ldr(self.scatterer, h_pol=False) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) self.moments['Kdp'] = radar.Kdp(self.scatterer) self.moments['Ah'] = radar.Ai(self.scatterer) self.moments['Adr'] = self.moments['Ah']-radar.Ai(self.scatterer, h_pol=False) return self.moments
def scatter_off_2dvd_packed(dicc): def drop_ar(D_eq): if D_eq < 0.7: return 1.0 elif D_eq < 1.5: return 1.173 - 0.5165*D_eq + 0.4698*D_eq**2 - 0.1317*D_eq**3 - \ 8.5e-3*D_eq**4 else: return 1.065 - 6.25e-2*D_eq - 3.99e-3*D_eq**2 + 7.66e-4*D_eq**3 - \ 4.095e-5*D_eq**4 d_diameters = dicc['1'] d_densities = dicc['2'] mypds = interpolate.interp1d(d_diameters, d_densities, bounds_error=False, fill_value=0.0) scatterer = Scatterer(wavelength=tmatrix_aux.wl_C, m=refractive.m_w_10C[tmatrix_aux.wl_C]) scatterer.psd_integrator = PSDIntegrator() scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0 / drop_ar(D) scatterer.psd_integrator.D_max = 10.0 scatterer.psd_integrator.geometries = (tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw) scatterer.or_pdf = orientation.gaussian_pdf(20.0) scatterer.orient = orientation.orient_averaged_fixed scatterer.psd_integrator.init_scatter_table(scatterer) scatterer.psd = mypds # GammaPSD(D0=2.0, Nw=1e3, mu=4) radar.refl(scatterer) zdr = radar.Zdr(scatterer) z = radar.refl(scatterer) scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) kdp = radar.Kdp(scatterer) A = radar.Ai(scatterer) return z, zdr, kdp, A
def get_radar_variables_ThomPSD_fwScattering(N1=None, N2=None, Lambda1=None, Lambda2=None, mu=None, D_max=None): scatterer.psd = ThomPSD(N1=N1, N2=N2, Lambda1=Lambda1, Lambda2=Lambda2, mu=mu, D_max=D_max) return [ radar.Ai(scatterer, h_pol=True), radar.Ai(scatterer, h_pol=False), radar.Kdp(scatterer) ]
def test_radar(self): """Test that the radar properties are computed correctly """ tm = TMatrixPSD(lam=tmatrix_aux.wl_C, m=refractive.m_w_10C[tmatrix_aux.wl_C], suppress_warning=True) tm.psd = psd.GammaPSD(D0=2.0, Nw=1e3, mu=4) tm.psd_eps_func = lambda D: 1.0 / drop_ar(D) tm.D_max = 10.0 tm.or_pdf = orientation.gaussian_pdf(20.0) tm.orient = orientation.orient_averaged_fixed tm.geometries = (tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw) tm.init_scatter_table() radar_xsect_h = radar.radar_xsect(tm) Z_h = radar.refl(tm) Z_v = radar.refl(tm, False) ldr = radar.ldr(tm) Zdr = radar.Zdr(tm) delta_hv = radar.delta_hv(tm) rho_hv = radar.rho_hv(tm) tm.set_geometry(tmatrix_aux.geom_horiz_forw) Kdp = radar.Kdp(tm) A_h = radar.Ai(tm) A_v = radar.Ai(tm, False) radar_xsect_h_ref = 0.22176446239750278 Z_h_ref = 6383.7337897299258 Z_v_ref = 5066.721040036321 ldr_ref = 0.0021960626647629547 Zdr_ref = 1.2599339374097778 delta_hv_ref = -0.00021227778705544846 rho_hv_ref = 0.99603080460983828 Kdp_ref = 0.19334678024367824 A_h_ref = 0.018923976733777458 A_v_ref = 0.016366340549483317 for (val, ref) in zip( (radar_xsect_h, Z_h, Z_v, ldr, Zdr, delta_hv, rho_hv, Kdp, A_h, A_v), (radar_xsect_h_ref, Z_h_ref, Z_v_ref, ldr_ref, Zdr_ref, delta_hv_ref, rho_hv_ref, Kdp_ref, A_h_ref, A_v_ref)): test_relative(self, val, ref)
def scatter_off_2dvd_packed(d_diameters, d_densities): """ Computing the scattering properties of homogeneous nonspherical scatterers with the T-Matrix method. Parameters: =========== d_diameters: array Drop diameters in mm! (or else returns values won't be with proper units.) d_densities: array Drop densities. Returns: ======== dbz: array Horizontal reflectivity. zdr: array Differential reflectivity. kdp: array Specific differential phase (deg/km). atten_spec: array Specific attenuation (dB/km). """ # Function interpolation. mypds = interpolate.interp1d(d_diameters, d_densities, bounds_error=False, fill_value=0.0) SCATTERER.psd = mypds # GammaPSD(D0=2.0, Nw=1e3, mu=4) # Obtaining reflectivity and ZDR. dbz = 10 * np.log10(radar.refl(SCATTERER)) # in dBZ zdr = 10 * np.log10(radar.Zdr(SCATTERER)) # in dB # Specific attenuation and KDP. SCATTERER.set_geometry(tmatrix_aux.geom_horiz_forw) atten_spec = radar.Ai(SCATTERER) # in dB/km atten_spec_v = radar.Ai(SCATTERER, h_pol=False) # in dB/km kdp = radar.Kdp(SCATTERER) # in deg/km return dbz, zdr, kdp, atten_spec, atten_spec_v
def calc_radar_parameters(self, wavelength=tmatrix_aux.wl_X): ''' Calculates the radar parameters and stores them in the object. Defaults to X-Band wavelength and Thurai et al. 2007 axis ratio setup. Sets object radar parameters: Zh, Zdr, Kdp, Ai Parameter: wavelength = tmatrix supported wavelength. ''' self._setup_scattering(wavelength) print self.bin_edges/1000. print self.Nt.min(),self.Nt.max() for t in range(0, len(self.time)): BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges/1000., self.Nt[t]/1E9) self.scatterer.psd = BinnedDSD self.scatterer.set_geometry(tmatrix_aux.geom_horiz_back) self.Zdr[t] = 10 * np.log10(radar.Zdr(self.scatterer)) self.Zh[t] = 10 * np.log10(radar.refl(self.scatterer)) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) self.Kdp[t] = radar.Kdp(self.scatterer) self.Ai[t] = radar.Ai(self.scatterer)
def calculate_radar_parameters(self, dsr_func=DSR.bc, scatter_time_range=None, max_diameter=9.0): """ Calculates radar parameters for the Drop Size Distribution. Calculates the radar parameters and stores them in the object. Defaults to X-Band,Beard and Chuang 10C setup. Sets the dictionary parameters in fields dictionary: Zh, Zdr, Kdp, Ai(Attenuation) Parameters: ---------- wavelength: optional, pytmatrix wavelength Wavelength to calculate scattering coefficients at. dsr_func: optional, function Drop Shape Relationship function. Several are availab le in the `DSR` module. Defaults to Beard and Chuang scatter_time_range: optional, tuple Parameter to restrict the scattering to a time interval. The first element is the start time, while the second is the end time. """ if self.scattering_table_consistent is False: self._setup_scattering( SPEED_OF_LIGHT / self.scattering_params["scattering_freq"] * 1000.0, dsr_func, max_diameter, ) self._setup_empty_fields() if scatter_time_range is None: self.scatter_start_time = 0 self.scatter_end_time = self.numt else: if scatter_time_range[0] < 0: print("Invalid Start time specified, aborting") return self.scatter_start_time = scatter_time_range[0] self.scatter_end_time = scatter_time_range[1] if scatter_time_range[1] > self.numt: print("End of Scatter time is greater than end of file." + "Scattering to end of included time.") self.scatter_end_time = self.numt self.scatterer.set_geometry( tmatrix_aux.geom_horiz_back ) # We break up scattering to avoid regenerating table. for t in range(self.scatter_start_time, self.scatter_end_time): if np.sum(self.Nd["data"][t]) is 0: continue BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges["data"], self.Nd["data"][t]) self.scatterer.psd = BinnedDSD self.fields["Zh"]["data"][t] = 10 * np.log10( radar.refl(self.scatterer)) self.fields["Zdr"]["data"][t] = 10 * np.log10( radar.Zdr(self.scatterer)) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) for t in range(self.scatter_start_time, self.scatter_end_time): BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges["data"], self.Nd["data"][t]) self.scatterer.psd = BinnedDSD self.fields["Kdp"]["data"][t] = radar.Kdp(self.scatterer) self.fields["Ai"]["data"][t] = radar.Ai(self.scatterer) self.fields["Adr"]["data"][t] = radar.Ai( self.scatterer) - radar.Ai(self.scatterer, h_pol=False)
def calculate_radar_parameters(self, wavelength=tmatrix_aux.wl_X, dsr_func=DSR.bc, scatter_time_range=None): ''' Calculates radar parameters for the Drop Size Distribution. Calculates the radar parameters and stores them in the object. Defaults to X-Band,Beard and Chuang 10C setup. Sets the dictionary parameters in fields dictionary: Zh, Zdr, Kdp, Ai(Attenuation) Parameters: ---------- wavelength: optional, pytmatrix wavelength Wavelength to calculate scattering coefficients at. dsr_func: optional, function Drop Shape Relationship function. Several are availab le in the `DSR` module. Defaults to Beard and Chuang scatter_time_range: optional, tuple Parameter to restrict the scattering to a time interval. The first element is the start time, while the second is the end time. ''' self._setup_scattering(wavelength, dsr_func) self._setup_empty_fields() if scatter_time_range is None: self.scatter_start_time = 0 self.scatter_end_time = len(self.time) else: if scatter_time_range[0] < 0: print("Invalid Start time specified, aborting") return self.scatter_start_time = scatter_time_range[0] self.scatter_end_time = scatter_time_range[1] if scatter_time_range[1] > len(self.time): print( "End of Scatter time is greater than end of file. Scattering to end of included time." ) self.scatter_end_time = len(self.time) self.scatterer.set_geometry( tmatrix_aux.geom_horiz_back ) # We break up scattering to avoid regenerating table. for t in range(self.scatter_start_time, self.scatter_end_time): if np.sum(self.Nd[t]) is 0: continue BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges, self.Nd[t]) self.scatterer.psd = BinnedDSD self.fields['Zh']['data'][t] = 10 * \ np.log10(radar.refl(self.scatterer)) self.fields['Zdr']['data'][t] = 10 * \ np.log10(radar.Zdr(self.scatterer)) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) for t in range(self.scatter_start_time, self.scatter_end_time): self.fields['Kdp']['data'][t] = radar.Kdp(self.scatterer) self.fields['Ai']['data'][t] = radar.Ai(self.scatterer) self.fields['Ad']['data'][t] = radar.Ai(self.scatterer) - radar.Ai( self.scatterer, h_pol=False)
def calculate_radar_parameters(self, dsr_func=DSR.bc, scatter_time_range=None): ''' Calculates radar parameters for the Drop Size Distribution. Calculates the radar parameters and stores them in the object. Defaults to X-Band,Beard and Chuang 10C setup. Sets the dictionary parameters in fields dictionary: Zh, Zv, Zdr, Kdp, Ai, Av(hor. and vert. Attenuation), Adr (diff. attenuation), cross_correlation_ratio_hv (rhohv), LDR, Kdp Parameters: ---------- wavelength: optional, pytmatrix wavelength Wavelength to calculate scattering coefficients at. dsr_func: optional, function Drop Shape Relationship function. Several are available in the `DSR` module. Defaults to Beard and Chuang scatter_time_range: optional, tuple Parameter to restrict the scattering to a time interval. The first element is the start time, while the second is the end time. ''' self._setup_scattering(SPEED_OF_LIGHT / self.scattering_freq * 1000.0, dsr_func) self._setup_empty_fields() if scatter_time_range is None: self.scatter_start_time = 0 self.scatter_end_time = self.numt else: if scatter_time_range[0] < 0: print("Invalid Start time specified, aborting") return self.scatter_start_time = scatter_time_range[0] self.scatter_end_time = scatter_time_range[1] if scatter_time_range[1] > self.numt: print("End of Scatter time is greater than end of file. " + "Scattering to end of included time.") self.scatter_end_time = self.numt # We break up scattering to avoid regenerating table. self.scatterer.set_geometry(tmatrix_aux.geom_horiz_back) print('Calculating backward scattering parameters ...') for t in range(self.scatter_start_time, self.scatter_end_time): if np.sum(self.Nd['data'][t]) is 0: continue BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges['data'], self.Nd['data'][t]) self.scatterer.psd = BinnedDSD self.fields['Zh']['data'][t] = 10 * \ np.log10(radar.refl(self.scatterer)) self.fields['Zv']['data'][t] = 10 * \ np.log10(radar.refl(self.scatterer, h_pol=False)) self.fields['Zdr']['data'][t] = 10 * \ np.log10(radar.Zdr(self.scatterer)) self.fields['cross_correlation_ratio_hv']['data'][t] = \ radar.rho_hv(self.scatterer) self.fields['specific_differential_phase_hv']['data'][t] = \ radar.delta_hv(self.scatterer) self.fields['LDR']['data'][t] = 10 * \ np.log10(radar.ldr(self.scatterer)) self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) print('Calculating forward scattering parameters ...') for t in range(self.scatter_start_time, self.scatter_end_time): BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges['data'], self.Nd['data'][t]) self.scatterer.psd = BinnedDSD self.fields['Kdp']['data'][t] = radar.Kdp(self.scatterer) self.fields['Ai']['data'][t] = radar.Ai(self.scatterer) self.fields['Av']['data'][t] = radar.Ai(self.scatterer, h_pol=False) self.fields['Adr']['data'][t] = radar.Ai(self.scatterer) - \ radar.Ai(self.scatterer, h_pol=False) # Mask all values where no precipitation present or when ice present params_list = [ 'Zh', 'Zv', 'Zdr', 'Kdp', 'Ai', 'Av', 'Adr', 'cross_correlation_ratio_hv', 'LDR', 'specific_differential_phase_hv' ] l = np.empty(len(self.fields['Precip_Code']['data']), dtype=bool) j = 0 for i in self.fields['Precip_Code']['data']: l[j] = 'N' in i or 'G' in i j += 1 for param in params_list: self.fields[param]['data'] = \ np.ma.masked_where(l, self.fields[param]['data'])
Ai = 0.0 * D0s wl = tmatrix_aux.wl_W el = 0. scatterer = tmatrix.Scatterer( wavelength=wl, m=refractive.m_w_10C[wl], #kw_sqr=tmatrix_aux.K_w_sqr[wl], radius_type=tmatrix.Scatterer.RADIUS_MAXIMUM, #or_pdf=orientation.gaussian_pdf(std=1.0), #orient=orientation.orient_averaged_fixed, thet0=90.0 - el, thet=90.0 + el) Nw = 8000.0 # mm-2 m-3 mu = 5 scatterer.psd_integrator = psd.PSDIntegrator( D_max=8.0, geometries=(tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw)) scatterer.psd_integrator.init_scatter_table(scatterer) for i, D0 in enumerate(D0s): scatterer.psd = genGamma(Nw, D0, mu) #psd.GammaPSD(D0=D0, mu=mu, Nw=Nw) scatterer.set_geometry(tmatrix_aux.geom_horiz_back) Zhh[i] = 10.0 * np.log10(radar.refl(scatterer)) scatterer.set_geometry(tmatrix_aux.geom_horiz_forw) Ai[i] = radar.Ai(scatterer) plt.plot(D0s, Zhh) plt.plot(D0s, Zhh - 2.0 * Ai * 0.25) plt.ylim([11, 26]) plt.grid()