-
Notifications
You must be signed in to change notification settings - Fork 0
/
plot_rrfscmaq_emission.py
executable file
·330 lines (269 loc) · 9.63 KB
/
plot_rrfscmaq_emission.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
###################################################################### CHJ #####
## Name : plot_rrfscmaq_emission.py
## Language : Python 3.7
## Usage : Plot an input emission file for rrfs_cmaq
## Input files : GBBEPx_CRES.emissions.nc
## NOAA/NWS/NCEP/EMC
## History ===============================
## V000: 2021/06/28: Chan-Hoo Jeon : Preliminary version
## V001: 2021/07/01: Chan-Hoo Jeon : Change to scatter plot
## V002: 2021/07/02: Chan-Hoo Jeon : Add grid_spec
## V003: 2021/07/05: Chan-Hoo Jeon : lon/lat check
## V004: 2023/02/21: Chan-Hoo Jeon : Add AQM_NA_13km
###################################################################### CHJ #####
import os, sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
from netCDF4 import Dataset
from scipy.io import netcdf
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy
from mpl_toolkits.axes_grid1 import make_axes_locatable
# HPC machine ('hera','orion')
machine='hera'
print(' You are on', machine)
#### Machine-specific input data ==================================== CHJ =====
# cartopy.config: Natural Earth data for background
# out_fig_dir: directory where the output files are created
# mfdt_kwargs: mfdataset argument
if machine=='hera':
cartopy.config['data_dir']='/scratch2/NCEPDEV/fv3-cam/Chan-hoo.Jeon/tools/NaturalEarth'
out_fig_dir="/scratch2/NCEPDEV/fv3-cam/Chan-hoo.Jeon/tools/fv3sar_pre_plot/Fig/"
mfdt_kwargs={'parallel':False}
path_fix="/scratch2/NCEPDEV/naqfc/RRFS_CMAQ/nexus/fix/"
elif machine=='orion':
cartopy.config['data_dir']='/home/chjeon/tools/NaturalEarth'
out_fig_dir="/work/noaa/fv3-cam/chjeon/tools/Fig/"
mfdt_kwargs={'parallel':False,'combine':'by_coords'}
else:
sys.exit('ERROR: Required input data are NOT set !!!')
plt.switch_backend('agg')
# Case-dependent input =============================================== CHJ =====
# Domain name
domain_nm='AQM_NA_13km'
dnm_in="/scratch2/NCEPDEV/naqfc/RRFS_CMAQ/RAVE_fire/"
fnm_input='Hourly_Emissions_regrid_NA_13km_20230217_t06z_h72.nc'
grid_spec='grid_spec_793.nc'
# Variables
#vars_data=["CO2","CO","SO2","OC","BC","PM2.5","NOx","NH3","MeanFRP"]
vars_data=["MeanFRP"]
# Select time step
ihr=1
# basic forms of title and file name
out_title_base='AQM::Emission::'+domain_nm+'::'
out_fname_base='rrfscmaq_emission_'+domain_nm+'_'
# Resolution of background natural earth data ('10m' or '50m' or '110m')
back_res='50m'
# Main part (will be called at the end) ======================= CHJ =====
def main():
# ============================================================= CHJ =====
global ds,lon_o,lat_o
global extent,c_lon,c_lat
print(' ===== grid_spec =========================================')
fname=os.path.join(path_fix,grid_spec)
try: grd=Dataset(fname,'r')
except: raise Exception('Could NOT find the file',fname)
print(grd)
lon_o=grd.variables['grid_lont'][:]
lat_o=grd.variables['grid_latt'][:]
lonc_o=grd.variables['grid_lon'][:]
latc_o=grd.variables['grid_lat'][:]
print(' ===== Input data ========================================')
# open the data file
fname=os.path.join(dnm_in,fnm_input)
# Check the variables in netcdf file
try: ds=Dataset(fname,'r')
except: raise Exception('Could NOT find the file',fname)
print(ds)
# =============================================================================
# lon_max=np.max(lon)
# Longitude 0:360 => -180:180
# if lon_max>180:
# lon=(lon+180)%360-180
# Highest and lowest longitudes and latitudes for plot extent
lon_min=np.min(lon_o)
lon_max=np.max(lon_o)
lat_min=np.min(lat_o)
lat_max=np.max(lat_o)
print(' lon_min=',lon_min,', lon_max=',lon_max)
print(' lat_min=',lat_min,', lat_max=',lat_max)
# Plot extent
esp=1
extent=[lon_min-esp,lon_max+esp,lat_min-esp,lat_max+esp]
c_lon=np.mean(extent[:2])
c_lat=np.mean(extent[2:])
# Variables
for svar in vars_data:
data_plot(svar)
# ===== plot ================================================== CHJ =====
def data_plot(svar):
# ============================================================= CHJ =====
print(' ===== '+svar+' ===== data ===============================')
# Extract data array
sfld=ds.variables[svar][:]
ndim_svar=sfld.ndim
if ndim_svar==2:
(nys,nxs)=sfld.shape
print(' 2D: nys=',nys,' nxs=',nxs)
sfld2d=sfld
elif ndim_svar==3:
(nts,nys,nxs)=sfld.shape
print(' time+2D: nts=',nts,' nys=',nys,' nxs=',nxs)
sfld2d=sfld[ihr,:,:]
sfld2d=np.squeeze(sfld2d)
print(sfld.shape)
print(sfld2d.shape)
# Check if dimensions of grid and vars are matched
if sfld2d.shape==lon_o.shape:
print('Data and grid are matched !!!')
else:
sys.exit('ERROR: Size of data does NOT match with that of grid !!!')
# extract non-zero cells
lon_pts=lon_o[sfld2d>0]
lat_pts=lat_o[sfld2d>0]
sfld_pts=sfld2d[sfld2d>0]
print(lon_pts.shape)
print(lat_pts.shape)
print(sfld_pts.shape)
out_title_fld=out_title_base+svar
out_fname=out_fname_base+svar
nm_svar=svar
cs_cmap='jet'
lb_ext='neither'
tick_ln=1.5
tick_wd=0.45
tlb_sz=3
scat_sz=2
cmap_range='round'
cmap_fix_min=0.0
cmap_fix_max=10.0
if svar=="CO2":
# n_rnd=8
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=3e-7
elif svar=="CO":
# n_rnd=9
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=1e-8
elif svar=="SO2":
# n_rnd=10
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=3e-9
elif svar=="OC":
# n_rnd=9
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=1e-8
elif svar=="BC":
# n_rnd=11
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=3e-10
elif svar=="PM2.5":
# n_rnd=9
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=6e-9
elif svar=="NOx":
# n_rnd=10
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=1.5e-9
elif svar=="NH3":
# n_rnd=10
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=1.25e-9
elif svar=="MeanFRP":
# n_rnd=2
cmap_range='fixed'
cmap_fix_min=0.0
cmap_fix_max=50.0
else:
n_rnd=7
# Max and Min of the field
fmax=np.max(sfld2d)
fmin=np.min(sfld2d)
print(' fld_max=',fmax)
print(' flx_min=',fmin)
# Make the colormap range symmetry
print(' cmap range=',cmap_range)
if cmap_range=='symmetry':
tmp_cmp=max(abs(fmax),abs(fmin))
cs_min=round(-tmp_cmp,n_rnd)
cs_max=round(tmp_cmp,n_rnd)
elif cmap_range=='round':
cs_min=round(fmin,n_rnd)
cs_max=round(fmax,n_rnd)
elif cmap_range=='real':
cs_min=fmin
cs_max=fmax
elif cmap_range=='fixed':
cs_min=cmap_fix_min
cs_max=cmap_fix_max
else:
sys.exit('ERROR: wrong colormap-range flag !!!')
print(' cs_max=',cs_max)
print(' cs_min=',cs_min)
# Plot field
if domain_nm[:7]=='RRFS_NA':
fig,ax=plt.subplots(1,1,subplot_kw=dict(projection=ccrs.Orthographic(
central_longitude=-107,central_latitude=53)))
else:
fig,ax=plt.subplots(1,1,subplot_kw=dict(projection=ccrs.Robinson(c_lon)))
ax.set_extent(extent, ccrs.PlateCarree())
back_plot(ax)
ax.set_title(out_title_fld,fontsize=9)
cs=ax.scatter(lon_pts,lat_pts,transform=ccrs.PlateCarree(),c=sfld_pts,cmap=cs_cmap,
vmin=cs_min,vmax=cs_max,s=scat_sz)
divider=make_axes_locatable(ax)
ax_cb=divider.new_horizontal(size="3%",pad=0.1,axes_class=plt.Axes)
fig.add_axes(ax_cb)
cbar=plt.colorbar(cs,cax=ax_cb,extend=lb_ext)
cbar.ax.tick_params(labelsize=6)
cbar.set_label(nm_svar,fontsize=6)
# Output figure
ndpi=300
out_file(out_fname,ndpi)
# Background plot ========================================== CHJ =====
def back_plot(ax):
# ========================================================== CHJ =====
fline_wd=0.5 # line width
falpha=0.3 # transparency
# natural_earth
# land=cfeature.NaturalEarthFeature('physical','land',back_res,
# edgecolor='face',facecolor=cfeature.COLORS['land'],
# alpha=falpha)
lakes=cfeature.NaturalEarthFeature('physical','lakes',back_res,
edgecolor='blue',facecolor='none',
linewidth=fline_wd,alpha=falpha)
coastline=cfeature.NaturalEarthFeature('physical','coastline',
back_res,edgecolor='blue',facecolor='none',
linewidth=fline_wd,alpha=falpha)
states=cfeature.NaturalEarthFeature('cultural','admin_1_states_provinces',
back_res,edgecolor='black',facecolor='none',
linewidth=fline_wd,linestyle=':',alpha=falpha)
borders=cfeature.NaturalEarthFeature('cultural','admin_0_countries',
back_res,edgecolor='red',facecolor='none',
linewidth=fline_wd,alpha=falpha)
# ax.add_feature(land)
ax.add_feature(lakes)
ax.add_feature(states)
ax.add_feature(borders)
ax.add_feature(coastline)
# Output file ============================================= CHJ =====
def out_file(out_file,ndpi):
# ========================================================= CHJ =====
# Output figure
plt.savefig(out_fig_dir+out_file+'.png',dpi=ndpi,bbox_inches='tight')
plt.close('all')
# Main call ================================================ CHJ =====
if __name__=='__main__':
main()