for run in runs_dict:
runs_dict[run] = stc.coarse_grain(stc.clean_cube(runs_dict[run]))
# runs_dict[run]=stc.clean_cube(runs_dict[run])
model_lons, model_lats = stc.unrotated_grid(stc.clean_cube(cube_DM10))
X, Y = np.meshgrid(model_lons, model_lats)
X = stc.coarse_grain(X)
Y = stc.coarse_grain(Y)
levels = np.arange(0.00, 0.5, 0.05).tolist()
same_bins = np.linspace(0.00, 0.5, 100)
levels = np.linspace(runs_dict['Satellite'].min(),
runs_dict['Satellite'].max(), 15)
stc.plot_map(runs_dict, levels, lat=X, lon=Y, variable_name='3rd LWP mm')
stc.plot_PDF(runs_dict, same_bins, variable_name='3rd LWP mm')
#%%
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
times = dataset.variables['time'][0, ]
times[times == missing] = 0
LWP = dataset.variables['cloud'][0, ]
LWP[LWP == missing] = 0
lon = dataset.variables['longitude']
lat = dataset.variables['latitude']
def draw_screen_poly(lats, lons, m):
x, y = m(lons, lats)
xy = zip(x, y)
coord = np.zeros([len(sat_lon), 2])
coord[:, 0] = sat_lon
coord[:, 1] = sat_lat
cm = plt.cm.RdBu_r
#model_lons=np.linspace(-5,20,500)
X, Y = np.meshgrid(model_lons, model_lats)
#Xo,Yo=np.meshgrid(lon_old,lat_old)
#data_old= sc.interpolate.griddata(coord_model, cube_oldm.data.flatten(), (X,Y), method='linear')
#grid_z0 = sc.interpolate.griddata(coord, sat_SW, (X,Y), method='nearest')
grid_z1 = sc.interpolate.griddata(coord, sat_LW, (X, Y), method='linear')
runs_dict['Satellite'] = grid_z1
runs_dict['ALL_ICE_PROC'] = cube[12].data
runs_dict['BASE (CS)'] = cube_csb[13].data
runs_dict['MEYERS (CS)'] = cube_csbm[13].data
runs_dict['MEYERS'] = cube_m[13].data
runs_dict['3_ORD_LESS'] = cube_3ord[13].data
runs_dict['2_ORD_MORE'] = cube_2m[13].data
#grid_z2 = sc.interpolate.griddata(coord, sat_SW, (X,Y), method='cubic')
#grid_z2[grid_z2<0]=0
#grid_z2[grid_z2==np.nan]=0
levels = np.linspace(cube[12].data.min(), cube[12].data.max(), 15)
stc.plot_map(runs_dict,
levels,
lat=X,
lon=Y,
variable_name='TOA longwave Wm-2')
stc.plot_PDF(runs_dict,
np.linspace(cube[12].data.min() / 1.5, cube[12].data.max() * 1.5,
50),
variable_name='TOA longwave Wm-2')
cube_m = iris.load(
ukl.Obtain_name(
'/nfs/a201/eejvt/CASIM/SO_KALLI/NO_CLOUD_SQUEME/MEYERS/L1/', 'CTT'))[0]
#%%
coord = np.zeros([len(sat_lon), 2])
coord[:, 0] = sat_lon
coord[:, 1] = sat_lat
cm = plt.cm.RdBu_r
model_lons, model_lats = stc.unrotated_grid(cube)
X, Y = np.meshgrid(model_lons, model_lats)
#%%
reload(stc)
grid_z1 = sc.interpolate.griddata(coord, sat_data, (X, Y), method='linear')
grid_z1[np.isnan(grid_z1)] = 0
runs_dict = OrderedDict()
runs_dict['Satellite'] = grid_z1
runs_dict['ALL_ICE_PROC'] = cube[12].data
#runs_dict['BASE (CS)']=cube_csb[13].data
runs_dict['MEYERS (CS)'] = cube_csbm[13].data
runs_dict['MEYERS'] = cube_m[13].data
#runs_dict['3_ORD_LESS']=cube_3ord[13].data
runs_dict['2_ORD_MORE'] = cube_2m[13].data
runs_dict['2_ORD_LESS'] = cube_2l[13].data
variable = 'CCT'
levels = np.linspace(260, 273, 15)
stc.plot_map(runs_dict, levels, lat=X, lon=Y, variable_name=variable)
bins = np.linspace(250, 273, 100)
stc.plot_PDF(runs_dict, bins, variable_name=variable)
runs_dict['GLO_MEAN']=jl.congrid(cube_GLO_MEAN[it].data,size_grid)
runs_dict['GLO_MIN']=jl.congrid(cube_GLO_MIN[it].data,size_grid)
runs_dict['GP_HAM_DMDUST']=jl.congrid(cube_GP_HAM_DMDUST[it].data,size_grid)
runs_dict['MEYERS']=jl.congrid(cube_MEYERS[it].data,size_grid)
#runs_dict['GLOMAP_PROFILE']=cube_gloprof[13].data
#runs_dict['LARGE_DOM']=cube_large_dom[13].data
#grid_z2 = sc.interpolate.griddata(coord, sat_SW, (X,Y), method='cubic')
#grid_z2[grid_z2<0]=0
#grid_z2[grid_z2==np.nan]=0
levels=np.linspace(0,runs_dict['Satellite'].max(),15)#runs_dict['Satellite'].min()
levels=np.linspace(130,800,15)#runs_dict['Satellite'].min()
levels_bin=np.linspace(0,runs_dict['Satellite'].max(),30)#runs_dict['Satellite'].min()
levels_bin=np.linspace(130,800,50)#runs_dict['Satellite'].min()
stc.plot_map(runs_dict,levels,lat=X,lon=Y,variable_name='(THIRD) TOA shortwave reduced Wm-2')
stc.plot_PDF(runs_dict,levels_bin,variable_name='(THIRD) TOA shortwave reduced Wm-2')
else:
size_grid=(50,50)
# X=jl.congrid(X,size_grid)
# Y=jl.congrid(Y,size_grid)
runs_dict['Satellite']=grid_z1
runs_dict['DM10']=cube_DM10[it].data
runs_dict['GLO_HIGH']=cube_GLO_HIGH[it].data
#runs_dict['MEYERS (CS)']=cube_csbm[13].data
#runs_dict['MEYERS']=cube_m[13].data
#runs_dict['3_ORD_LESS']=cube_3ord[13].data
#runs_dict['2_ORD_LESS']=cube_2l[13].data
#runs_dict['2_ORD_MORE']=cube_2m[13].data
#runs_dict['SM_100_COOPER']=cube_SM_100_COOPER[13].data
#runs_dict['SM_T40']=cube_SM_T40[13].data
runs_dict['SM_LCOND_FALSE'] = cube_SM_LCOND_FALSE[13].data
runs_dict['SM_NOBIGG'] = cube_SM_NOBIGG[13].data
runs_dict['NOICE'] = cube_noice[13].data
#runs_dict['2_ORD_LESS']=cube_2l[13].data
runs_dict['2_ORD_MORE'] = cube_2m[13].data
#runs_dict['GLOPROF']=cube_gloprof[13].data
#runs_dict['GP_HIGH_CSED']=cube_gl_csed[13].data
#runs_dict['GP_LOW_CSED']=cube_gl_low_csed[13].data
#runs_dict['GP_HAM']=cube_gpham[13].data
#runs_dict['GLOMAP_PROFILE']=cube_gloprof[13].data
#runs_dict['LARGE_DOM']=cube_large_dom[13].data
#grid_z2 = sc.interpolate.griddata(coord, sat_SW, (X,Y), method='cubic')
#grid_z2[grid_z2<0]=0
#grid_z2[grid_z2==np.nan]=0
levels = np.linspace(0, runs_dict['Satellite'].max(),
15) #runs_dict['Satellite'].min()
levels = np.linspace(120, 700, 15) #runs_dict['Satellite'].min()
levels_bin = np.linspace(120, runs_dict['Satellite'].max(),
30) #runs_dict['Satellite'].min()
levels_bin = np.linspace(120, 700, 80) #runs_dict['Satellite'].min()
stc.plot_map(runs_dict,
levels,
lat=X,
lon=Y,
variable_name='TOA shortwave Wm-2')
stc.plot_PDF(runs_dict, levels_bin, variable_name='TOA shortwave Wm-2')
#runs_dict['SM_T40']=cube_SM_T40[13].data
runs_dict['SM_LCOND_FALSE'] = cube_SM_LCOND_FALSE[13].data
runs_dict['SM_NOBIGG'] = cube_SM_NOBIGG[13].data
runs_dict['NOICE'] = cube_noice[13].data
#runs_dict['2_ORD_LESS']=cube_2l[13].data
runs_dict['2_ORD_MORE'] = cube_2m[13].data
for run in runs_dict:
runs_dict[run] = stc.coarse_grain(stc.clean_cube(runs_dict[run]))
# runs_dict[run]=stc.clean_cube(runs_dict[run])
model_lons, model_lats = stc.unrotated_grid(stc.clean_cube(cube))
X, Y = np.meshgrid(model_lons, model_lats)
X = stc.coarse_grain(X)
Y = stc.coarse_grain(Y)
#runs_dict['GLOPROF']=cube_gloprof[13].data
#runs_dict['GP_HIGH_CSED']=cube_gl_csed[13].data
#runs_dict['GP_LOW_CSED']=cube_gl_low_csed[13].data
#runs_dict['GP_HAM']=cube_gpham[13].data
levels = np.arange(0, 0.45, 0.05).tolist()
same_bins = np.linspace(0, 0.5, 100)
#levels=np.linspace(runs_dict['Satellite (AMSR2)'].min(),runs_dict['Satellite (AMSR2)'].max(),15)
stc.plot_map(runs_dict, levels, lat=X, lon=Y, variable_name='(first) LWP mm')
stc.plot_PDF(runs_dict, same_bins, variable_name=' (first) LWP mm')
#%%
for i in range(10):
plt.close()