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',