def get_region_omi_gc_so2(filename, region_limit):
"""
"""
var_dict = read_bpch(filename, ['IJ-AVG-$_SO2'], squeeze=True)
latitude_e = var_dict['latitude_e']
longitude_e = var_dict['longitude_e']
i1 = get_center_index(latitude_e, region_limit[0])
i2 = get_center_index(latitude_e, region_limit[2])
j1 = get_center_index(longitude_e, region_limit[1])
j2 = get_center_index(longitude_e, region_limit[3])
out_dict = {}
latitude_e = latitude_e[i1:i2 + 2]
longitude_e = longitude_e[j1:j2 + 2]
longitude_e, latitude_e = np.meshgrid(longitude_e, latitude_e)
out_dict['Latitude_e'] = latitude_e
out_dict['Longitude_e'] = longitude_e
latitude = var_dict['latitude'][i1:i2 + 1]
longitude = var_dict['longitude'][j1:j2 + 1]
longitude, latitude = np.meshgrid(longitude, latitude)
out_dict['Latitude'] = latitude
out_dict['Longitude'] = longitude
var_dict['IJ-AVG-$_SO2'] = var_dict['IJ-AVG-$_SO2'] / 1e9
gc_v_ind = 0 # GEOS-Chem VCD
gc_v_so2 = var_dict['IJ-AVG-$_SO2'][gc_v_ind, i1:i2 + 1, j1:j2 + 1]
out_dict['gc_v_so2'] = gc_v_so2
omi_v_gc_ind = 5 # OMI VCD corrected by GC profile
omi_v_gc_so2 = var_dict['IJ-AVG-$_SO2'][omi_v_gc_ind, i1:i2 + 1, j1:j2 + 1]
out_dict['omi_v_gc_so2'] = omi_v_gc_so2
return out_dict
def compare_two_varilables(filename, varname1, varname2,
vmin=0.0, vmax=0.8,
diff_min=None, diff_max=None,
seperate_cbar=False,
region_limit=None,
sc_xlabel='',
sc_ylabel='',
sc_max=1.0,
verbose=True):
"""
"""
# read data
coord_varns = ['Latitude_e', 'Longitude_e']
some_varns = [varname1, varname2]
varnames = coord_varns + some_varns
data_dict = read_nc(filename, varnames, verbose=verbose)
# region_limit
if region_limit is not None:
lat_e_1D = data_dict['Latitude_e'][:,0]
lon_e_1D = data_dict['Longitude_e'][0,:]
# index
i1 = get_center_index(lat_e_1D, region_limit[0])
i2 = get_center_index(lat_e_1D, region_limit[2])
j1 = get_center_index(lon_e_1D, region_limit[1])
j2 = get_center_index(lon_e_1D, region_limit[3])
for varn in varnames:
if varn in coord_varns:
data_dict[varn] = data_dict[varn][i1:i2+2,j1:j2+2]
if varn in some_varns:
data_dict[varn] = data_dict[varn][i1:i2+1,j1:j2+1]
# plot
fig = plt.figure(figsize=(8,7))
plt.subplots_adjust(top=0.98)
ax_list = []
xtick = [100, 110, 120]
ytick = [30, 40, 50]
xtick_list = [xtick, xtick, xtick]
ytick_list = [ytick, [], ytick]
for i in range(len(xtick_list)):
ax = add_geoaxes(fig, int('22'+str(i+1)),
xtick=xtick_list[i], ytick=ytick_list[i])
ax_list.append(ax)
lat_e = data_dict['Latitude_e']
lon_e = data_dict['Longitude_e']
# colorbar height
h = 0.02
# variables
for i in range(len(some_varns)):
varn = some_varns[i]
var = data_dict[varn]
ax = ax_list[i]
var_out = cartopy_plot(lon_e, lat_e, var, ax=ax,
vmin=vmin, vmax=vmax,
cbar=seperate_cbar, cmap=deepcopy(WhGrYlRd_map))
add_China_province(ax)
# colorbar for variables
ax1 = ax_list[0]
ax2 = ax_list[1]
cax_v = h_2_ax(fig, ax1, ax2, y_off=-0.06, height=h)
plt.colorbar(var_out['mesh'], cax=cax_v, orientation='horizontal')
right_center_label(cax_v, '[DU]')
# difference
ax = ax_list[2]
var_diff = data_dict[varname2] - data_dict[varname1]
diff_out = cartopy_plot(lon_e, lat_e, var_diff, ax=ax,
vmin=diff_min, vmax=diff_max,
cbar=seperate_cbar, cmap=plt.get_cmap('seismic'))
add_China_province(ax)
# colorbar of difference
ax = ax_list[2]
cax_diff = h_1_ax(fig, ax, y_off=-0.06, height=h)
plt.colorbar(diff_out['mesh'], cax=cax_diff, orientation='horizontal')
right_center_label(cax_diff, '[DU]')
# scatter plot
ax_sc = fig.add_subplot(224)
ax_sc.set_aspect('equal')
ax_sc.set_xlabel(sc_xlabel)
ax_sc.set_ylabel(sc_ylabel)
ax_sc.set_xlim([0,1.0])
ax_sc.set_ylim([0,1.0])
label_ul = ['R', 'linear_eq', 'rmse', 'nmb', 'mb', 'N']
scatter(ax_sc, data_dict[varname1], data_dict[varname2], s=3,
label_ul=label_ul)
# region
if region_limit is not None:
for ax in ax_list:
ax.set_xlim(region_limit[1], region_limit[3])
ax.set_ylim(region_limit[0], region_limit[2])
verbose = True
figdir = '../figure/'
#
# End user parameters
#####################
# read GC, OMI data
gc_omi_vars = read_nc(gc_omi_file, gc_omi_varname_list, verbose=verbose)
# subset data
lat_e_1D = gc_omi_vars['Latitude_e'][:,0]
lon_e_1D = gc_omi_vars['Longitude_e'][0,:]
i1 = get_center_index(lat_e_1D, region_limit[0])
i2 = get_center_index(lat_e_1D, region_limit[2])
j1 = get_center_index(lon_e_1D, region_limit[1])
j2 = get_center_index(lon_e_1D, region_limit[3])
for varname in gc_omi_vars:
if varname in ['Latitude_e', 'Longitude_e']:
gc_omi_vars[varname] = gc_omi_vars[varname][i1:i2+2,j1:j2+2]
else:
gc_omi_vars[varname] = gc_omi_vars[varname][i1:i2+1,j1:j2+1]
print('Latitude_e:' )
print(gc_omi_vars['Latitude_e'][:,0])
print('Latitude:' )
print(gc_omi_vars['Latitude'][:,0])
print('Longitude_e:' )
print(gc_omi_vars['Longitude_e'][0,:])
print('Longitude:' )
def plot_NO2_combine(dir_mean,
dir_month,
start_year,
end_year,
year,
month,
verbose=False,
region_limit=[-90.0, -180.0, 90.0, 180.0],
dir_fig=None,
vmin_mean=0.0,
vmax_mean=0.5,
vmin_ano=-0.1,
vmax_ano=0.1,
vmin_ano_per=-50.0,
vmax_ano_per=50.0):
""" Plot mutli-year mean of monthly NO2, monthly NO2
anomaly and monthly NO2 relative anomaly.
Parameters
----------
dir_mean : str
Directory of mutli-year mean of monthly NO2
dir_month : str
Directory of monthly NO2
start_year : str
Used to generate filename and longname
end_year : str
Used to generate filename and longname
year : str
Used to generate filename and longname
month : str
Used to generate filename and longname
verbose : logical
output more information
region_limit : list
The region for plot. [min_lat, min_lon, max_lat, max_lon]
dir_fig : str or None
If it is str, save figure to the directory
"""
# used to convert unit
toDU = 2.69e16
# colorbar parameters
pad = 0.01
aspect = 15
# read monthly data
file_month = dir_month + 'OMI_NO2_' + year + '-' + month \
+ '_monthly.nc'
NO2_month = io.read_month_OMI_NO2_L3(file_month, verbose=verbose)
NO2_month = NO2_month / toDU
# read mutli-year mean of monthly NO2
NO2_mean = io.read_multi_year_month_OMI_NO2_L3(dir_mean,
start_year,
end_year,
month,
verbose=verbose)
NO2_mean = NO2_mean / toDU
# NO2 anoanmly
NO2_ano = NO2_month - NO2_mean
# NO2 realtive anoamly
NO2_ano_percent = NO2_ano / NO2_mean * 100.0
# generate grid
nlat_c, nlon_c = 720, 1440
lat_e, lon_e, lat_c, lon_c = generate_grid(nlat_c, nlon_c)
# get region data
i1 = get_center_index(lat_e, region_limit[0])
i2 = get_center_index(lat_e, region_limit[2])
j1 = get_center_index(lon_e, region_limit[1])
j2 = get_center_index(lon_e, region_limit[3])
NO2_mean = NO2_mean[i1:i2 + 1, j1:j2 + 1]
NO2_ano = NO2_ano[i1:i2 + 1, j1:j2 + 1]
NO2_ano_percent = NO2_ano_percent[i1:i2 + 1, j1:j2 + 1]
lat_e = lat_e[i1:i2 + 2]
lon_e = lon_e[j1:j2 + 2]
# begin plot
#fig = plt.figure(figsize=(4, 8))
fig = plt.figure()
xtick = np.arange(-180, 180.1, 20)
ytick = np.arange(-90, 90.1, 10)
ax_list = []
# mutli-year mean of monthly NO2
title1 = start_year + '-' + end_year + ', ' + month \
+ r', NO$_2$'
ax1 = add_geoaxes(fig, 311, xtick=[], ytick=ytick, title=title1)
ax_list.append(ax1)
pout1 = pcolormesh(ax1,
lon_e,
lat_e,
NO2_mean,
vmin=vmin_mean,
vmax=vmax_mean)
cbar1 = fig.colorbar(pout1['mesh'],
ax=ax1,
extend='max',
pad=pad,
aspect=aspect)
cbar1.set_label(r'[DU]')
# anomaly
title2 = year + ', ' + month + ', anomaly'
ax2 = add_geoaxes(fig, 312, xtick=[], ytick=ytick, title=title2)
ax_list.append(ax2)
pout2 = pcolormesh(ax2,
lon_e,
lat_e,
NO2_ano,
cmap=plt.get_cmap('seismic'),
vmin=vmin_ano,
vmax=vmax_ano)
cbar2 = fig.colorbar(pout2['mesh'],
ax=ax2,
extend='both',
pad=pad,
aspect=aspect)
cbar2.set_label(r'[DU]')
# relative anomaly
title3 = year + ', ' + month + ', relative anomaly'
ax3 = add_geoaxes(fig, 313, xtick=xtick, ytick=ytick, title=title3)
ax_list.append(ax3)
pout3 = pcolormesh(ax3,
lon_e,
lat_e,
NO2_ano_percent,
cmap=plt.get_cmap('seismic'),
vmin=vmin_ano_per,
vmax=vmax_ano_per)
cbar3 = fig.colorbar(pout3['mesh'],
ax=ax3,
extend='both',
pad=pad,
aspect=aspect)
cbar3.set_label(r'[%]')
# set axes
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
for ax in ax_list:
ax.add_feature(cfeature.BORDERS)
ax.add_feature(states_provinces, edgecolor='k', linewidth=0.5)
ax.add_feature(cfeature.COASTLINE, zorder=200)
ax.add_feature(cfeature.OCEAN, color='w', zorder=100)
ax.set_xlim((region_limit[1], region_limit[3]))
ax.set_ylim((region_limit[0], region_limit[2]))
plt.subplots_adjust(hspace=0.3)
# save figure
if dir_fig is not None:
figname = dir_fig + 'NO2_anomaly_' + year + '-' + month \
+ '_from_' + start_year + '-' + end_year + '.png'
plt.savefig(figname, format='png', dpi=300)
return fig
def plot_monthly_anomaly_for_6yrs(dir_mean,
dir_month,
start_year,
end_year,
year_list,
month,
verbose=False,
region_limit=[-90.0, -180.0, 90.0, 180.0],
left=0.05,
right=0.98,
top=0.9,
bottom=0.1,
wspace=0.05,
hspace=0.05,
y_off=-0.06,
dir_fig=None,
vmin_ano=-0.1,
vmax_ano=0.1,
xticks=np.arange(-180.0, 180.1, 20.0),
yticks=np.arange(-90.0, 90.1, 10.0),
name='',
vmin_ano_per=-50.0,
vmax_ano_per=50.0):
""" Plot monhtly anomaly for 6 years
"""
# used to convert unit
toDU = 2.69e16
# read monthly data
NO2_month_dict = {}
for year in year_list:
file_month = dir_month + 'OMI_NO2_' + year + '-' + month \
+ '_monthly.nc'
NO2_month = io.read_month_OMI_NO2_L3(file_month, verbose=verbose)
NO2_month = NO2_month / toDU
NO2_month_dict[year] = NO2_month
# read mutli-year mean of monthly NO2
NO2_mean = io.read_multi_year_month_OMI_NO2_L3(dir_mean,
start_year,
end_year,
month,
verbose=verbose)
NO2_mean = NO2_mean / toDU
# NO2 anomaly and relative anoamly
NO2_ano_dict = {}
NO2_ano_percent_dict = {}
for year in year_list:
# anomaly
NO2_ano = NO2_month_dict[year] - NO2_mean
NO2_ano_dict[year] = NO2_ano
# relative anomaly
NO2_ano_percent = NO2_ano / NO2_mean * 100.0
NO2_ano_percent_dict[year] = NO2_ano_percent
# generate grid
nlat_c, nlon_c = 720, 1440
lat_e, lon_e, lat_c, lon_c = generate_grid(nlat_c, nlon_c)
# get region data
i1 = get_center_index(lat_e, region_limit[0])
i2 = get_center_index(lat_e, region_limit[2])
j1 = get_center_index(lon_e, region_limit[1])
j2 = get_center_index(lon_e, region_limit[3])
lat_e = lat_e[i1:i2 + 2]
lon_e = lon_e[j1:j2 + 2]
# begin plot
nrow = 3
ncol = 4
figsize = (12, 6)
projPos = list(range(nrow * ncol))
layout_dict = multiFigure(nrow,
ncol,
left=left,
right=right,
top=top,
bottom=bottom,
wspace=wspace,
hspace=hspace,
figsize=figsize,
projPos=projPos)
fig = layout_dict['fig']
axes = layout_dict['axes']
ano_ax_ind = [0, 1, 4, 5, 8, 9]
ano_per_ax_ind = [2, 3, 6, 7, 10, 11]
pout1_list = []
pout2_list = []
for iyr in range(len(year_list)):
year = year_list[iyr]
# anomaly
NO2_ano = NO2_ano_dict[year][i1:i2 + 1, j1:j2 + 1]
pout1 = cartopy_plot(lon_e, lat_e, NO2_ano, ax=axes[ano_ax_ind[iyr]], \
vmin=vmin_ano, vmax=vmax_ano, cbar=False, \
title=year+month, cmap=plt.get_cmap('seismic'))
pout1_list.append(pout1)
# relative anomaly
NO2_ano_percent = NO2_ano_percent_dict[year][i1:i2 + 1, j1:j2 + 1]
pout2 = cartopy_plot(lon_e, lat_e, NO2_ano_percent, \
ax=axes[ano_per_ax_ind[iyr]], cbar=False, \
vmin=vmin_ano_per, vmax=vmax_ano_per, \
title=year+month, cmap=plt.get_cmap('seismic'))
pout2_list.append(pout2)
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
# ticks
panel_tick_label(axes, ncol, xticks=xticks, yticks=yticks)
# set limit
for ax in axes:
ax.set_xlim([lon_e[0], lon_e[-1]])
ax.set_ylim([lat_e[0], lat_e[-1]])
ax.add_feature(cfeature.BORDERS)
ax.add_feature(states_provinces, edgecolor='k', linewidth=0.5)
ax.add_feature(cfeature.COASTLINE, zorder=200)
ax.add_feature(cfeature.OCEAN, color='w', zorder=100)
# anomaly colorbar
cax1 = h_2_ax(fig, pout1_list[4]['ax'], pout1_list[5]['ax'], y_off=y_off)
cb1 = plt.colorbar(pout1_list[4]['mesh'], cax=cax1, \
orientation='horizontal', extend='both')
cb1.set_label('OMI NO2 anomaly [DU]')
# relative anomaly colorbar
cax2 = h_2_ax(fig, pout2_list[4]['ax'], pout2_list[5]['ax'], y_off=y_off)
cb2 = plt.colorbar(pout2_list[4]['mesh'], cax=cax2, \
orientation='horizontal', extend='both')
cb2.set_label('OMI NO2 relative anomaly [%]')
# save figure
if dir_fig is not None:
figname = dir_fig + name + 'NO2_anomaly_6yrs_' + month \
+ '_from_' + start_year + '-' + end_year + '.png'
plt.savefig(figname, format='png', dpi=300)