hydroset='', suffix='radar_regrid.h5', minhour=6.0, varlist=['Z10', 'Z35', 'Z94', 'T', 'W10', 'W35', 'W94', 'quality_x', 'quality_w']) radarw = slice_data(radar, 'quality_w', maxvalue=8192) radarx = slice_data(radar, 'quality_x', maxvalue=8192) logrule = True density = False CFAD = True inverty = True bins = 100 stats = ['mean', 'median', 'quartile'] r = hist_and_plot(pamtra, 'Simulated CFAD T - SW', yvar='T', xvar='W35', xlabel='Spectral Width Ka [m/s]', ylabel='T [K]', vminmax=[0.1, 40], xlim=[0, 3], ylim=[-30, 10], lognorm=logrule, savename='pamtra_T_SWk.png', inverty=inverty, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(radar, 'Measured CFAD T- SW', yvar='T', xvar='W35', xlabel='Spectral Width Ka [m/s]', ylabel='T [K]', vminmax=[0.1, 40], xlim=[0, 3], ylim=[-30, 10], lognorm=logrule, savename='radar_T_SWk.png', inverty=inverty, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD)
#%% Z CFAD Height lognormrule = True density = True bins = 50 stats = ['mean', 'median', 'quartile'] r = hist_and_plot(pamtra, 'CFAD Zx hgt', yvar='Hgt', xvar='Z10', xlabel='Zx [dBZ]', ylabel='Hgt [m]', vminmax=[0.1, 100], xlim=[-60, 50], ylim=[0, 12000], lognorm=lognormrule, savename='pamtraCFAD_Zx_H.png', inverty=False, figax=None, stats=stats, bins=(bins, icon150heights[::-1]), density=density, CFAD=True) r = hist_and_plot(pamtra, 'CFAD Zk hgt', yvar='Hgt', xvar='Z35', xlabel='Zk [dBZ]', ylabel='Hgt [m]',
logrule = True density = True CFAD = True bins = (np.arange(-5, 15, 0.35), np.arange(-45, 1, 0.6)) vminmax = [0.1, 20] stats = ['mean', 'median', 'quartile', 'decile'] r = hist_and_plot(pamtra, 'Simulated CFAD T - DWRxk', yvar='T', xvar='DWRxk', xlabel='DWRxk [dB]', ylabel='T [deg C]', vminmax=vminmax, xlim=[-5, 15], ylim=[-30, 0], lognorm=logrule, savename='pamtra_T_DWRxk.png', inverty=True, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(radarx, 'Measured CFAD T - DWRxk', yvar='T', xvar='DWRxk', xlabel='DWRxk [dB]', ylabel='T [deg C]',
bins = 100 stats = ['mean', 'median', 'quartile'] pamtra = slice_data(pamtra, 'DWRkw', maxvalue=4) ice = slice_data(ice, 'DWRkw', maxvalue=4) snow = slice_data(snow, 'DWRkw', maxvalue=4) radar = slice_data(radar, 'DWRkw', maxvalue=4) r = hist_and_plot( slice_data(pamtra, 'T', maxvalue=-2), 'Simulated MDVk - Zk', yvar='V35', xvar='Z35', xlabel='Zk [dBZ]', ylabel='MDV Ka [m/s]', #vminmax=[0.1, 100], xlim=[-40, 30], ylim=[-2, 1], lognorm=lognormrule, savename='pamtra_Vk_Zk.png', inverty=False, figax=None, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot( slice_data(ice, 'T', maxvalue=-2), 'Simulated MDVk - Zk only ICE', yvar='V35', xvar='Z35', xlabel='Zk [dBZ]', ylabel='MDV Ka [m/s]', #vminmax=[0.1, 100],
N[~np.isfinite(N)] = np.nan df['N'] = N df.dropna(inplace=True, subset=['Ze']) df.to_hdf('joyrad94snr.h5', key='stat', mode='a', append=True) print('done') df = pd.read_hdf('joyrad94snr.h5', key='stat') df['SNR'] = df['Ze'] - df['N'] hist_and_plot(df.dropna(subset=['SNR']), 'SNR Joyrad94', yvar='Hgt', xvar='SNR', xlabel='SNRW [dB]', ylabel='Hgt [m]', xlim=[-30, 60], ylim=[0, 12000], lognorm=True, savename='SNRw.png', inverty=False, figax=None, bins=100, density=False, CFAD=False) hist_and_plot(df.dropna(subset=['N']), 'Noise Joyrad94', yvar='Hgt', xvar='N', xlabel='NoiseW [dB]', ylabel='Hgt [m]', xlim=[-60, -10],
density = False CFAD = False inverty = True bins = 100 lw = 3 vminmax = [1e2, 7e5] pamtra = slice_data(pamtra, 'Z10', -14) r = hist_and_plot(slice_data(pamtra, 'T', -20, -5), 'Simulated -20<T<-5', yvar='V35', xvar='DWRxk', xlabel='DWRxk [dB]', ylabel='MDV [m/s]', vminmax=vminmax, xlim=[-5, 15], ylim=[-3, 0], lognorm=logrule, savename='pamtra_Vk_DWRxk.png', inverty=inverty, figax=None, bins=bins, density=density, CFAD=CFAD) plt.gca().set_prop_cycle(color=colors[1:]) plt.plot(Zi[:, 0] - Zi[:, 1], -Vi[:, 0], label='ice crystals', lw=lw) plt.plot(Zr[:, 0] - Zr[:, 1], -Vr[:, 0], label='raindrops', lw=lw) plt.plot(Zs[:, 0] - Zs[:, 1], -Vs[:, 0], label='snowflakes', lw=lw) plt.plot(Zg[:, 0] - Zg[:, 1], -Vg[:, 0], label='graupel', lw=lw) plt.plot(Zh[:, 0] - Zh[:, 1], -Vh[:, 0], label='hail', lw=lw) plt.savefig('pamtra_Vk_DWRxk.png', dpi=300)
pamtra[col] = pam[col].values radarw = slice_data(radar, 'quality_w', maxvalue=8192) radarx = slice_data(radar, 'quality_x', maxvalue=8192) logrule = True density = False CFAD = True inverty = True bins = 100 stats = ['mean', 'median', 'quartile', 'decile'] r = hist_and_plot(pamtra, 'Simulated CFAD T - MDV', yvar='T', xvar='V35', xlabel='MDV ka [m/s]', ylabel='T [K]', vminmax=[0.1, 30], xlim=[-5, 1], ylim=[-30, 10], lognorm=logrule, savename='pamtra_T_Vk.png', inverty=inverty, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(radar, 'Measured CFAD T- MDV', yvar='T', xvar='V35m5', xlabel='MDV ka [m/s]', ylabel='T [K]', vminmax=[0.1, 30], xlim=[-5, 1], ylim=[-30, 10], lognorm=logrule, savename='radar_T_Vk.png', inverty=inverty, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(pamtra, 'Simulated CFAD T - SW', yvar='T', xvar='W35',
pamtra['SWa'] = qN2SWa(pamtra['QR'], pamtra['QNR']) pamtra['SKa'] = qN2SKa(pamtra['QR'], pamtra['QNR']) f, ((ax11, ax12), (ax21, ax22), (ax31, ax32)) = plt.subplots(3, 2, figsize=(10.5, 9.)) r = hist_and_plot(slice_data(pamtra, 'RR', minvalue=minRR, left=True), 'Simulated MDV Ka', yvar='T', xvar='V35', xlabel='MDV [m/s]', ylabel='T [deg C]', vminmax=[0.1, 30], xlim=[-10, 0], ylim=[0, 10], lognorm=logrule, savename=pre + 'pamRad_T_VSD.png', inverty=inverty, figax=(f, ax11), stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(slice_data(radar, 'RR', minvalue=minRR, left=True), 'Measured MDV Ka', yvar='T', xvar='V35avg', xlabel='MDV [m/s]', ylabel='T [deg C]',
radar = read_radar(campaign=campaign, minhour=minhour, avg='_avg') radarw = slice_data(radar, 'quality_w', maxvalue=8192) radarx = slice_data(radar, 'quality_x', maxvalue=8192) radarxw = slice_data(radarw, 'quality_x', maxvalue=8192) #%% Triple frequency xlim = [-5, 20] ylim = [-5, 20] lw = 3 hist_and_plot(pamtra, '3f plot all', yvar='DWRxk', xvar='DWRkw', xlabel='DWR Ka W [dB]', ylabel='DWR X Ka [dB]', xlim=xlim, ylim=ylim, lognorm=lognormrule, savename='ISTP/pamtra3f_all.png', inverty=False, figax=None, bins=100, density=False, CFAD=False) plt.plot(Zc[:, 1] - Zc[:, 2], Zc[:, 0] - Zc[:, 1], label='cloud droplets', lw=lw) plt.plot(Zi[:, 1] - Zi[:, 2], Zi[:, 0] - Zi[:, 1], label='ice crystals', lw=lw) plt.plot(Zr[:, 1] - Zr[:, 2], Zr[:, 0] - Zr[:, 1], label='raindrops', lw=lw) plt.plot(Zs[:, 1] - Zs[:, 2], Zs[:, 0] - Zs[:, 1], label='snowflakes', lw=lw) plt.plot(Zg[:, 1] - Zg[:, 2], Zg[:, 0] - Zg[:, 1], label='graupel', lw=lw)
suffix='pamtra_icon.h5', varlist=varlist, minhour=6.0) #pamtra[pamtra==9999.0] = np.nan lognormrule = True data = 1.0 * pamtra #slice_data(pamtra, 'DWRxk', maxvalue=-2) hxk, xxk, yxk = hist_and_plot(data, 'Nx Nk', yvar='N10', xvar='N35', xlim=[-100, 100], ylim=[-100, 100], xlabel='Nx', ylabel='Nk', lognorm=lognormrule, savename='pamtra3f_Nxk.png', inverty=False, figax=None, bins=100, density=False, CFAD=False) hkw, xkw, ykw = hist_and_plot(data, 'Nk Nw', yvar='N35', xvar='N94', xlim=[-100, 100], ylim=[-100, 100], xlabel='Nk',
rad = slice_data(rad, 'maxDWRxk', maxvalue=13.5) rpu = slice_data(rpu, 'maxDWRxk', maxvalue=13.5) pam = slice_data(pam, 'maxDWRkw', maxvalue=13.5) rad = slice_data(rad, 'maxDWRkw', maxvalue=13.5) rpu = slice_data(rpu, 'maxDWRkw', maxvalue=13.5) r = hist_and_plot(pam, 'Simulated Cloud Top Temperature - max DWRkw', yvar='CTT', xvar='maxDWRkw', xlabel='max DWRkw [dB]', ylabel='CTT [deg C]', vminmax=vminmax, xlim=[0, 20], ylim=[-60, -10], lognorm=logrule, savename='pam_CTT_DWRkw.png', inverty=inverty, figax=None, stats=stats, bins=bins, density=density, CFAD=CFAD) r = hist_and_plot(rad, 'Measured Regridded Cloud Top Temperature - max DWRkw', yvar='CTT', xvar='maxDWRkw', xlabel='max DWRkw [dB]', ylabel='CTT [deg C]',
radarw = slice_data(radar, 'quality_w', maxvalue=8192) radarx = slice_data(radar, 'quality_x', maxvalue=8192) radarxw = slice_data(radarw, 'quality_x', maxvalue=8192) #%% Triple frequency xlim = [-10, 20] ylim = [-10, 20] lw = 3 r = hist_and_plot(slice_data(pamtra, 'Z10', minvalue=-2), '3f plot all', yvar='DWRxk', xvar='DWRkw', xlabel='DWR Ka W [dB]', ylabel='DWR X Ka [dB]', xlim=xlim, ylim=ylim, lognorm=lognormrule, savename='pamtra3f_all.png', inverty=False, figax=None, bins=100, density=True, CFAD=False) plt.plot(Zc[:, 1] - Zc[:, 2], Zc[:, 0] - Zc[:, 1], label='cloud droplets', lw=lw) plt.plot(Zi[:, 1] - Zi[:, 2], Zi[:, 0] - Zi[:, 1], label='ice crystals', lw=lw) plt.plot(Zr[:, 1] - Zr[:, 2], Zr[:, 0] - Zr[:, 1], label='raindrops', lw=lw) plt.plot(Zs[:, 1] - Zs[:, 2], Zs[:, 0] - Zs[:, 1], label='snowflakes', lw=lw) plt.plot(Zg[:, 1] - Zg[:, 2], Zg[:, 0] - Zg[:, 1], label='graupel', lw=lw)
pamtra['QNS'] = np.log10(pamtra['QNS']) pamtra['QI'] = np.log10(pamtra['QI']) pamtra['QS'] = np.log10(pamtra['QS']) pamtra[~np.isfinite(pamtra)] = np.nan data = 1.0 * pamtra #slice_data(pamtra, 'DWRxk', maxvalue=-2) #data = slice_data(pamtra, 'DWRxk', maxvalue=-2) h, x, y = hist_and_plot(data, '3f plot all', yvar='QI', xvar='QNI', xlim=[-300, 0], ylim=[-300, 0], xlabel='QI', ylabel='QNI', lognorm=lognormrule, savename='pamtra3f_ice.png', inverty=False, figax=None, bins=100, density=False, CFAD=False) h, x, y = hist_and_plot(data, '3f plot all', yvar='QS', xvar='QNS', xlim=[-300, 0], ylim=[-300, 0], xlabel='QS',