fig = plt.figure(figsize=(15, 15))
#fig = plt.figure(figsize=(len(ordered_list)*5, 12))
n_runs = len(ordered_list)
max_val = sample_cube[:, :, :].data.max()
min_val = sample_cube[:, :, :].data.min()
min_val = np.min(sample_cube[:, :, :].data[np.nonzero(
sample_cube[:, :, :].data)])
vertical_levels = np.linspace(min_val, max_val, 9).tolist()
diff_levels = np.linspace(-max_val, max_val, 18).tolist()
cube_list = []
#lats=sample_cube.coord('grid_latitude').points-52
#lons=sample_cube.coord('grid_longitude').points[:]-180
#lons=np.arange(-0.02*250,250*0.02,0.02)[0:]
lons, lats = stc.unrotated_grid(sample_cube)
#lats=np.arange(-0.02*250-52,250*0.02-52,0.02)
for run in ordered_list:
cube = run_dict[run].cube
try:
cube.remove_coord('surface_altitude')
except:
a = 8754784
cube_list.append(cube)
import copy
sub_plots = []
time_coord = copy.copy(sample_cube.coord('time'))
time_coord.convert_units('seconds since 1970-01-01 00:00:0.0')
first = []
cube_m = iris.load(
ukl.Obtain_name(
'/nfs/a201/eejvt/CASIM/SO_KALLI/NO_CLOUD_SQUEME/MEYERS/All_time_steps/',
'm01s02i205_toa_outgoing_longwave_flux'))[0]
#cube=cube.extract(iris.Constraint(time=jl.find_nearest_vector(cube.coord('time').points,t13)))
#cube_csb=cube_csb[13,:,:]
#cube=cube[12,:,:]
#cube_nh=cube_nh[13,:,:]
#cube_3ord=cube_3ord[13,:,:]
#cube_2m=cube_2m[13,:,:]
##cube_con=cube_con[13,:,:]
#cube_oldm=cube_oldm[13,:,:]
#%%
model_lons, model_lats = stc.unrotated_grid(cube)
#times_range=np.argwhere((times_ceres >= tdi) & (times_ceres <=tde))
times_range = np.logical_and([times_ceres >= tdi], [times_ceres <= tde])[0]
sat_lon = lon[times_range]
sat_lat = lat[times_range]
sat_LW = LW[times_range]
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')
Xsat[np.isnan(LWP)] = np.nan
Ysat[np.isnan(LWP)] = np.nan
Xsat_flat = Xsat.flatten()
Ysat_flat = Ysat.flatten()
LWP_flat = LWP.flatten()
Xsat_flat = Xsat_flat[np.logical_not(np.isnan(Xsat_flat))]
Ysat_flat = Ysat_flat[np.logical_not(np.isnan(Ysat_flat))]
LWP_flat = LWP_flat[np.logical_not(np.isnan(LWP_flat))]
def check_nan(array):
return np.isnan(array).any()
model_lons, model_lats = stc.unrotated_grid(cube_DM10)
max_lon, min_lon = model_lons.max(), model_lons.min()
max_lat, min_lat = model_lats.max(), model_lats.min()
coord = np.zeros([len(Xsat_flat), 2])
coord[:, 0] = Xsat_flat
coord[:, 1] = Ysat_flat
cm = plt.cm.RdBu_r
#model_lons=np.linspace(-5,20,500)
X, Y = np.meshgrid(model_lons, model_lats)
grid_z1 = sc.interpolate.griddata(coord, LWP_flat, (X, Y), method='linear')
plt.imshow(grid_z1)
it = 15
runs_dict = OrderedDict()
runs_dict['Satellite'] = grid_z1
#runs_dict['GLOBAL']=cube_global[it].data
code = 'm01s01i208'
cloud_top = iris.load(
ukl.Obtain_name(sim_path + 'TRY2/ALL_ICE_PROC/' + sub_folder, code))[0]
cloud_top = iris.load(
ukl.Obtain_name(sim_path + 'TRY2/LARGE_DOMAIN/' + sub_folder, code))[0]
mb = netCDF4.Dataset(
path + 'modis/' + 'MYD06_L2.A2014343.1325.006.2014344210847.nc', 'r')
mb.variables['Cloud_Mask_1km']
#plt.imshow(mb.variables['Cloud_Fraction'])
sat_dat = mb.variables['Cloud_Fraction'][:, ].flatten()
sat_dat = mb.variables['Cloud_Mask_1km'][:, ].flatten()
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(cloud_top)
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
plt.figure()
plt.subplot(121)
plt.imshow(grid_z1)
#plt.xlim(0,600)
plt.subplot(122)
plt.imshow(cloud_top.data[12, ])
plt.show()
#%%
sim_path = '/nfs/a201/eejvt/CASIM/SO_KALLI/'