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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from netCDF4 import Dataset,num2date
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import cartopy.crs as ccrs
import cartopy.feature as cpf
mpl.use('Agg')
def read_MNH_output_conf(file):
nctime=file.variables['time'][0]
t_unit=file.variables['time'].units
valid_time=num2date(nctime,t_unit)
lat=file.variables['ni'][:]
lon=file.variables['nj'][:]
tmp1=file.variables['THT']
tmp2=tmp1[0,:,:,:]
nx=tmp2.shape[2]
ny=tmp2.shape[1]
nz=tmp2.shape[0]
return valid_time,lat,lon
def read_MNH_output_var(variables,file,fields,units):
MesoNH_names={'ZS':'orography', 'ACPRT':'acc_precip', 'ACPRR':'acc_rain', 'ACPRS':'acc_snow', 'ACPRG':'acc_graupel', 'ACPRH':'acc_hail', 'INPRT':'precip_rate', 'INPRR':'rain_rate', 'INPRS':'snow_rate', 'INPRG':'graupel_rate', 'INPRH':'hail_rate', 'PABST':'P', 'THT':'theta', 'RVT':'rv', 'RCT':'rc', 'RRT':'rr', 'RIT':'ri', 'RST':'rs', 'RGT':'rg', 'RHT':'rh', 'CCLOUDT':'Nc', 'CRAINT':'Nr', 'CICET':'Ni', 'UT':'u', 'VT':'v', 'WT':'w'}
computable=['alt','T','T_K','RH_liq','RH_ice','LWC','IWC','Dc','Dr']
for variable in variables:
if not (variable in fields):
if variable in ['ZS']:
var=file.variables[variable]
fields[MesoNH_names[variable]]=var[:,:]
units[MesoNH_names[variable]]=var.units
elif variable in ['ACPRT', 'ACPRR', 'ACPRS', 'ACPRG', 'ACPRH', 'INPRT', 'INPRR', 'INPRS', 'INPRG', 'INPRH']:
try:
var=file.variables[variable]
fields[MesoNH_names[variable]]=var[0,:,:]
units[MesoNH_names[variable]]=var.units
except:
var=file.variables['THT']
fields[MesoNH_names[variable]]=var[0,0,:,:]*0.
units[MesoNH_names[variable]]='none'
elif variable in MesoNH_names:
try:
var=file.variables[variable]
fields[MesoNH_names[variable]]=var[0,:,:,:]
units[MesoNH_names[variable]]=var.units
except:
var=file.variables['THT']
fields[MesoNH_names[variable]]=var[0,:,:,:]*0.
units[MesoNH_names[variable]]='none'
elif variable == 'allMNH':
read_MNH_output_var(MesoNH_names.keys(),file,fields,units)
elif variable == 'hydrometeors':
read_MNH_output_var(['RCT', 'RRT', 'RIT', 'RST', 'RGT', 'CCLOUDT', 'CRAINT', 'CICET'],file,fields,units)
elif variable == 'wind':
read_MNH_output_var(['UT','VT','WT'],file,fields,units)
elif variable == 'precipitation':
read_MNH_output_var(['ACPRT', 'ACPRR', 'ACPRS', 'ACPRG', 'ACPRH', 'INPRT', 'INPRR', 'INPRS', 'INPRG', 'INPRH'],file,fields,units)
elif variable == 'alt':
tmp1=file.variables['RCT']
tmp2=tmp1[0,:,:,:]
nx=tmp2.shape[2]
ny=tmp2.shape[1]
nz=tmp2.shape[0]
Z=file.variables["ZHAT"][:]
ZHAT_M=np.zeros((nz),dtype='f')
for k in range(0,nz-1):
ZHAT_M[k]=(Z[k]+Z[k+1])/2.
ZHAT_M[nz-1]=2*Z[nz-1]-Z[nz-2] # a verifier
alt=np.zeros((nz,ny,nx),dtype='f')
zs=file.variables['ZS'][:,:]
ZCOEF=np.zeros((ny,nx),dtype='f')
ZCOEF[:,:]=1-zs[:,:]/ZHAT_M[nz-1]
for k in range(0,nz-1):
alt[k,:,:]=ZHAT_M[k]*ZCOEF[:,:]+zs[:,:]
fields['alt']=alt[:,:,:]
units['alt']='m'
del Z, ZHAT_M, zs, ZCOEF,nx,ny,nz,tmp1,tmp2
elif variable == 'T':
read_MNH_output_var(['THT', 'PABST'],file,fields,units)
fields['T']=fields['theta']*(fields['P']/100000.)**(2./7.)-273.15
units['T']='C'
elif variable == 'T_K':
read_MNH_output_var(['THT', 'PABST'],file,fields,units)
fields['T_K']=fields['theta']*(fields['P']/100000.)**(2./7.)
units['T_K']='K'
elif variable == 'RH_liq':
read_MNH_output_var(['T_K', 'PABST', 'RVT'],file,fields,units)
e_sat_w=np.exp(60.223 - 6822.4/fields['T_K'] - 5.1393*np.log(fields['T_K']))
r_sat_w=0.62198 * e_sat_w / (fields['P'] - e_sat_w)
rh_w=100 * fields['rv'] / r_sat_w
fields['RH_liq']=rh_w[:,:,:]
units['RH_liq']='%'
elif variable == 'RH_ice':
read_MNH_output_var(['T_K', 'PABST', 'RVT'],file,fields,units)
e_sat_i=np.exp(32.621 - 6295.4/fields['T_K'] - 0.56313*np.log(fields['T_K']))
r_sat_i=0.62198 * e_sat_i / (fields['P'] - e_sat_i)
rh_i=100 * fields['rv'] / r_sat_i
rh_i[np.where(fields['T']>0.)]=float('nan')
fields['RH_ice']=rh_i[:,:,:]
units['RH_ice']='%'
elif variable == 'LWC':
read_MNH_output_var(['RCT', 'RRT'],file,fields,units)
fields['LWC']=fields['rc']+fields['rr']
units['LWC']=units['rc']
elif variable == 'IWC':
read_MNH_output_var(['RIT', 'RST', 'RGT'],file,fields,units)
fields['IWC']=fields['ri']+fields['rs']+fields['rg']
units['IWC']=units['ri']
elif variable == 'Dc':
read_MNH_output_var(['RCT', 'CCLOUDT'],file,fields,units)
fields['Dc']=1.e6*(6*fields['rc']/1000/3.14159/fields['Nc'])**0.33
units['Dc']='µm'
elif variable == 'Dr':
read_MNH_output_var(['RRT', 'CRAINT'],file,fields,units)
fields['Dr']=1000.*(6*fields['rr']/1000/3.14159/fields['Nr'])**0.33
units['Dr']='mm'
elif variable == 'all':
computable.append('allMNH')
read_MNH_output_var(computable,file,fields,units)
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
new_Greys=truncate_colormap(plt.get_cmap('Greys'), 0.5, 1.)
new_Greens=truncate_colormap(plt.get_cmap('Greens'), 0.5, 1.)
new_Oranges=truncate_colormap(plt.get_cmap('Oranges'), 0.5, 1.)
new_Reds=truncate_colormap(plt.get_cmap('Reds'), 0.5, 1.)
new_Blues=truncate_colormap(plt.get_cmap('Blues'), 0.5, 1.)
new_Purples=truncate_colormap(plt.get_cmap('Purples'), 0.5, 1.)
mpl.rcParams["font.size"] = 3
xmin=300
xmax=600
ymin=50
ymax=350
exp=["02_run_LIMA111111","02_run_LIMA222222_JW_CIBU","02_run_LIMA222110","02_run_ICE4"]
#exp=["02_run_LIMA111111"]
for folder in '../',:
for simu in exp:
output_file = PdfPages('SUPERCELL-'+simu[7:]+'.pdf')
########################
# Plot 1 : mixing ratios
########################
for ech in '28', :
print(simu+' '+ech)
MNH_file=folder+simu+'/LIMAA.1.EXP01.0'+ech+'.nc'
# Read data from the Meso-NH file
print(simu)
fic=Dataset(MNH_file,'r')
valid_time,lat,lon = read_MNH_output_conf(fic)
print(valid_time)
f={}
u={}
read_MNH_output_var(['RCT', 'RRT', 'RIT', 'RST', 'RGT', 'RHT', 'alt', 'T'],fic,f,u)
y=np.unravel_index(np.argmax(f['rc'][50,:,:]),f['rc'][50,:,:].shape)[0]
# Horizontal cross sections
levels=[1.E-12,1.E-10,1.E-8,1.E-6,2.E-6,5.E-6,1.E-5,2.E-5,5.E-5,1.E-4,2.E-4,5.E-4,1.E-3,2.E-3,5.E-3,1.E-2]
fig,ax=plt.subplots(4,6,sharex=True,sharey=True)
i=0
for lev in 20,35,50,65:
for field in 'rc','rr','ri','rs','rg','rh':
var2D=f[field][lev,ymin:ymax,xmin:xmax]
cs=ax[i//6,i-6*(i//6)].contourf(var2D,cmap='jet',levels=levels,extend='max',norm=mpl.colors.LogNorm())
cs.cmap.set_under('w')
cs.changed()
ax[i//6,i-6*(i//6)].plot([0,xmax-xmin],[y-ymin,y-ymin],color = "Black",alpha=0.5,linestyle='dotted',linewidth=0.5)
ax[i//6,i-6*(i//6)].set_title(field+', lvl='+str(lev)+', t='+str(int(ech)*5)+"min",fontsize=5)
ax[i//6,i-6*(i//6)].set_aspect('equal')
i=i+1
plt.subplots_adjust(left=0.05, bottom=0.1, right=1.05, top=0.9)
cb=fig.colorbar(cs,format='%.1e',shrink=0.5,ax=ax.ravel().tolist())
cb.set_label(u['rc'], labelpad=0, y=1.05, rotation=0)
# Save fig, close plot
plt.savefig(output_file,format='pdf')
plt.close()
########################
# Plot 2 : cross section
########################
fig,ax=plt.subplots(1,1,sharex=True,sharey=True)
levels=np.logspace(-3,1.,50)
#x.set_yscale('log')
alt=f['alt'][:,y,xmin]
plt.ylim(ymax=20000)
x=np.arange(0,xmax-xmin,1)
ax.contourf(x,alt,f['ri'][:,y,xmin:xmax]*1000,cmap=new_Greys,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['ri'][:,y,xmin:xmax]*1000,colors="Grey",alpha=0.5,levels=[0.001])
ax.contour(x,alt,f['ri'][:,y,xmin:xmax]*1000,colors="Grey",alpha=0.5,levels=[0.1],linestyles='dashed')
ax.contourf(x,alt,f['rs'][:,y,xmin:xmax]*1000,cmap=new_Greens,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['rs'][:,y,xmin:xmax]*1000,colors="Green",alpha=0.5,levels=[0.001])
ax.contour(x,alt,f['rs'][:,y,xmin:xmax]*1000,colors="Green",alpha=0.5,levels=[0.1],linestyles='dashed')
ax.contourf(x,alt,f['rg'][:,y,xmin:xmax]*1000,cmap=new_Oranges,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['rg'][:,y,xmin:xmax]*1000,colors="Orange",alpha=1.,levels=[0.001])
ax.contour(x,alt,f['rg'][:,y,xmin:xmax]*1000,colors="Orange",alpha=1.,levels=[0.1],linestyles='dashed')
ax.contourf(x,alt,f['rh'][:,y,xmin:xmax]*1000,cmap=new_Reds,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['rh'][:,y,xmin:xmax]*1000,colors="Red",alpha=1.,levels=[0.001])
ax.contour(x,alt,f['rh'][:,y,xmin:xmax]*1000,colors="Red",alpha=1.,levels=[0.1],linestyles='dashed')
ax.contourf(x,alt,f['rc'][:,y,xmin:xmax]*1000,cmap=new_Blues,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['rc'][:,y,xmin:xmax]*1000,colors="Blue",alpha=0.5,levels=[0.001])
ax.contour(x,alt,f['rc'][:,y,xmin:xmax]*1000,colors="Blue",alpha=0.5,levels=[0.1],linestyles='dashed')
ax.contourf(x,alt,f['rr'][:,y,xmin:xmax]*1000,cmap=new_Purples,alpha=0.5,levels=levels,extend="max")
ax.contour(x,alt,f['rr'][:,y,xmin:xmax]*1000,colors="Purple",alpha=0.5,levels=[0.001])
ax.contour(x,alt,f['rr'][:,y,xmin:xmax]*1000,colors="Purple",alpha=0.5,levels=[0.1],linestyles='dashed')
ax.contour(x,alt,f['T'][:,y,xmin:xmax],colors="Black",alpha=0.5,levels=[0],linestyles='dotted')
#ax.invert_yaxis()
ax.set_title("Cross section of hydrometeors mixing ratios"+', t='+str(int(ech)*5)+"min",fontsize=10)
#Fake plot for legend
ax.plot(1,1,color = "Blue",label = "cloud",alpha=0.5)
ax.plot(1,1,color = "Purple",label = "rain",alpha=0.5)
ax.plot(1,1,color = "Grey",label = "ice",alpha=0.5)
ax.plot(1,1,color = "Green",label = "snow",alpha=0.5)
ax.plot(1,1,color = "Orange",label = "graupel",alpha=0.5)
ax.plot(1,1,color = "Red",label = "hail",alpha=0.5)
ax.plot(1,1,color = "Black",label = "0.001 g.kg$^{-1}$",alpha=0.5)
ax.plot(1,1,color = "Black",label = "0.1 g.kg$^{-1}$",alpha=0.5,linestyle='dashed')
ax.plot(1,1,color = "Black",label = "T=0°C",alpha=0.5,linestyle='dotted')
ax.legend(fontsize = 7, loc="lower right")
# Save fig, close plot and MNH file
plt.savefig(output_file,format='pdf')
plt.close()
fic.close()
##########################################
# Plot 3 : rain intensities & accumulation
##########################################0
cmap = mpl.cm.get_cmap('gist_rainbow_r')
fig=plt.figure()
ax =fig.add_subplot(1, 2, 1)
# plot sequence of intensities
i=0
#for ech in '06', :
for ech in '06', '08', '10', '12', '14', '16', '18', '20', '22', '24', '26', '28', '30':
print(folder+' '+simu+' '+ech)
MNH_file=folder+simu+'/LIMAA.1.EXP01.0'+ech+'.nc'
# Read data from the Meso-NH file
fic=Dataset(MNH_file,'r')
valid_time,lat,lon = read_MNH_output_conf(fic)
f={}
u={}
read_MNH_output_var(['INPRT'],fic,f,u)
# Horizontal cross sections
var2D=f['precip_rate'][:,:]
ax.contour(var2D,levels=[15.],colors=[cmap(i/12)])
ax.plot(1,1,color=cmap(i/12),label=str(int(ech)*5)+"min")
i=i+1
fic.close()
ax.legend(fontsize = 6, loc="upper left",ncol=3)
ax.set_title("15mm h$^{-1}$ precipitation rate",fontsize=10)
ax.set_aspect('equal')
# plot accumulated precipitation
MNH_file=folder+simu+'/LIMAA.1.EXP01.036.nc'
fic=Dataset(MNH_file,'r')
valid_time,lat,lon = read_MNH_output_conf(fic)
f={}
u={}
read_MNH_output_var(['ACPRR','ACPRS','ACPRG','ACPRH'],fic,f,u)
i=0
levels=[0.2,0.5,1,2,5,10,15,20,25,30]
for var in 'acc_rain', 'acc_snow', 'acc_graupel', 'acc_hail' :
ax=fig.add_subplot(2, 4, 3+(i//2)*2+i)
ax.contour(f['acc_rain'][:,:],levels=[2],colors=['grey'],linewidths=[0.5])
cf=ax.contourf(f[var][:,:],levels=levels,extend='max',cmap='jet')
ax.set_title(var,fontsize=10)
ax.set_aspect('equal')
cb=plt.colorbar(cf,orientation='horizontal')
i=i+1
# Save fig, close plot and MNH file
plt.savefig(output_file,format='pdf')
plt.close()
###################
# Close output file
###################
output_file.close()