from statistic import hist_and_plot
pamtra = read_variables(
path='/work/develop/pamtraICON/comparison/data/pamtra/',
hydroset='all_hydro',
suffix='pamtra_icon.h5',
varlist=['Hgt', 'Z10', 'Z35', 'Z94'],
minhour=6.0)
radar = read_variables(
path='/work/develop/pamtraICON/comparison/data/radar/',
hydroset='',
suffix='radar.h5',
varlist=['Hgt', 'Z10', 'Z35', 'Z94', 'quality_x', 'quality_w'],
minhour=6.0)
radarw = slice_data(radar, 'quality_w', maxvalue=8192)
radarx = slice_data(radar, 'quality_x', maxvalue=8192)
#radarxw = slice_data(radarw, 'quality_x', maxvalue=8192)
#%% 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]',
radar['RR'] = (pluvio.resample('1s').nearest().loc[radar.unixtime] * 60 /
accMins).values
pamtra['RR'] = (icon.resample('1s').nearest().loc[pamtra.unixtime] * 60 /
accMins).values
rad = rad.loc[radar.unixtime]
rpu = rpu.loc[radar.unixtime]
pam = pam.loc[pamtra.unixtime]
#for col in rpu.columns:
# radar[col] = rpu[col].values
for col in rad.columns:
radar[col] = rad[col].values
for col in pam.columns:
pamtra[col] = pam[col].values
pamtra = slice_data(pamtra, 'Z10', minvalue=-7)
#radarw = slice_data(radar, 'quality_w', maxvalue=8192)
#radarx = slice_data(radar, 'quality_x', maxvalue=8192)
radarw = slice_data(radar, 'quality_w', maxvalue=4096)
radarx = slice_data(radar, 'quality_x', maxvalue=4096)
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',
radar = read_variables(path='/work/develop/pamtraICON/comparison/data/radar/',
hydroset='',
suffix='radar_regrid.h5',
minhour=6.0,
varlist=[
'Z10', 'Z35', 'Z94', 'T', 'unixtime', 'V10avg',
'V35avg', 'V94avg', 'quality_x', 'quality_w'
])
radar.unixtime = pd.to_datetime(radar.unixtime.astype(np.int64), unit='s')
pamtra.unixtime = pd.to_datetime(pamtra.unixtime.astype(np.int64), unit='s')
ice.unixtime = pd.to_datetime(ice.unixtime.astype(np.int64), unit='s')
snow.unixtime = pd.to_datetime(snow.unixtime.astype(np.int64), unit='s')
#rad = slice_data(radar, 'V35avg', minvalue=-2, maxvalue=1)
radar = slice_data(radar, 'Z35', minvalue=-40, maxvalue=30)
#radarw = slice_data(radar, 'quality_w', maxvalue=8192)
#radarx = slice_data(radar, 'quality_x', maxvalue=8192)
#radarxw = slice_data(radarw, 'quality_x', maxvalue=8192)
rad = rad.loc[radar.unixtime]
pam = pam.loc[pamtra.unixtime]
for col in rad.columns:
radar[col] = rad[col].values
for col in pam.columns:
pamtra[col] = pam[col].values
ice[col] = pam[col].values
snow[col] = pam[col].values
lognormrule = True
density = True
#
#snow = read_variables(path='/work/develop/pamtraICON/comparison/data/pamtra/',
# hydroset='only_snow', suffix='pamtra_icon.h5', pamtra=True,
# varlist=['Z10', 'Z35', 'Z94', 'T',
# 'V10', 'V35', 'V94'], minhour=6.0)
radar = read_variables(path='/work/develop/pamtraICON/comparison/data/radar/',
hydroset='',
suffix='radar_regrid.h5',
minhour=6.0,
varlist=[
'Z10', 'Z35', 'Z94', 'T', 'V10avg', 'V35avg',
'V94avg', 'quality_x', 'quality_w'
])
radarw = slice_data(radar, 'quality_w', maxvalue=8192)
radarx = slice_data(radar, 'quality_x', maxvalue=8192)
data = netCDF4.Dataset('../data/idealized_hydro.nc')
data_simple = netCDF4.Dataset('../data/idealized_hydro_simple.nc')
datavar = data.variables
Zvar = data_simple.variables['Ze']
Ze = Zvar[:]
Ze[:, 0, :, 2, 0, 0] = Ze[:, 0, :, 2, 0, 0] #+ 10.*np.log10(0.93/0.72)
V = datavar['Radar_MeanDopplerVel'][:]
V[V < 0] = np.nan
Zc = Ze[0, 0, :, :, 0, 0]
Zi = Ze[1, 0, :, :, 0, 0]
Zr = Ze[2, 0, :, :, 0, 0]
Zs = Ze[3, 0, :, :, 0, 0]
Vs = V[3, 0, :, :, 0, 0]
Vg = V[4, 0, :, :, 0, 0]
Vh = V[5, 0, :, :, 0, 0]
Sc = S[0, 0, :, :, 0, 0]
Si = S[1, 0, :, :, 0, 0]
Sr = S[2, 0, :, :, 0, 0]
Ss = S[3, 0, :, :, 0, 0]
Sg = S[4, 0, :, :, 0, 0]
Sh = S[5, 0, :, :, 0, 0]
campaign = 'tripex'
minhour = 6.0
pamtra = read_prepare(hydroset=campaign + '_all_hydro_', minhour=minhour)
pamtraTl0 = slice_data(pamtra, 'T', maxvalue=0)
lognormrule = True
#%% CFAD Sensitivity
xlim = [-60, 50]
ylim = [0, 12000]
h1, x1, y1 = hist_and_plot(pamtra,
'sensitivity cone X',
yvar='Hgt',
xvar='Z10',
xlabel='Zx [dBZ]',
ylabel='Hgt [m]',
xlim=xlim,
ylim=ylim,
lognorm=lognormrule,
savename='tripex/3f/pamtraCFAD_Zx_Hgt.png',
'V35avg', 'V94avg', 'W10', 'W35', 'W94', 'unixtime',
'quality_x', 'quality_w'
])
radar.unixtime = pd.to_datetime(radar.unixtime.astype(np.int64), unit='s')
pamtra.unixtime = pd.to_datetime(pamtra.unixtime.astype(np.int64), unit='s')
radar['RR'] = (pluvio.resample('1s').nearest().loc[radar.unixtime] * 60 /
accMins).values
pamtra['RR'] = (icon.resample('1s').nearest().loc[pamtra.unixtime] * 60 /
accMins).values
#radarw = slice_data(radar, 'quality_w', maxvalue=8192)
#radarx = slice_data(radar, 'quality_x', maxvalue=8192)
pamtra = slice_data(pamtra, 'Z35', -15.0)
radar = slice_data(radar, 'Z35', -15.0)
logrule = True
density = False
CFAD = True
inverty = True
bins = 100
stats = ['mean', 'median', 'quartile', 'decile']
## High precipitation
minRR = 1.0
maxRR = 91.0
pre = 'HIG'
mdv, sw, sk = qN2moments(pamtra['QR'], pamtra['QNR'])
pamtra['MDVp'] = mdv
Sr = S[2, 0, :, :, 0, 0]
Ss = S[3, 0, :, :, 0, 0]
Sg = S[4, 0, :, :, 0, 0]
Sh = S[5, 0, :, :, 0, 0]
campaign = 'tripex'
minhour = 6.0
pamtra = read_prepare(hydroset=campaign + '_all_hydro_',
minhour=minhour,
suffix='')
pamtra[pamtra == 9999] = np.nan
lognormrule = True
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,
'Nk Nw',
yvar='N35',
xvar='N94',
xlim=[-100, 100],
ylim=[-100, 100],
xlabel='Nk',
ylabel='Nw',
lognorm=lognormrule,
savename='pamtra3f_Nkw.png',
inverty=False,
figax=None,
bins=100,
density=False,
CFAD=False)
data = 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)
pam.index = pd.to_datetime(pam.index.astype(np.int64), unit='s')
rpu.index = pd.to_datetime(rpu.index.astype(np.int64), unit='s')
rad['RR'] = pluvio.resample('1s').nearest().loc[rad.index] * 60 / accMins
pam['RR'] = icon.resample('1s').nearest().loc[pam.index] * 60 / accMins
rpu['RR'] = pluvio.resample('1s').nearest().loc[rpu.index] * 60 / accMins
logrule = True
density = False
CFAD = True
inverty = True
bins = (60, 30)
stats = ['mean', 'median', 'quartile', 'decile']
vminmax = [0.1, 40]
pam = slice_data(pam, 'RR', minvalue=-0.001)
rad = slice_data(rad, 'RR', minvalue=-0.001)
rpu = slice_data(rpu, 'RR', minvalue=-0.001)
pam = slice_data(pam, 'maxDWRxk', maxvalue=13.5)
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',
Sc = S[0, 0, :, :, 0, 0]
Si = S[1, 0, :, :, 0, 0]
Sr = S[2, 0, :, :, 0, 0]
Ss = S[3, 0, :, :, 0, 0]
Sg = S[4, 0, :, :, 0, 0]
Sh = S[5, 0, :, :, 0, 0]
Zc[:, 0] -= 0.5
Zr[:, 0] -= 0.5
#pamtra = read_prepare(filename='../data/tripex_all_hydro_data_pamtra_icon.h5')
#pamtra[pamtra==9999.0] = np.nan
lognormrule = True
#radar = read_radar(filename='..data/'+campaign+'_data_radar'++'.h5', minhour=minhour)
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,