-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathprocess_pr_ev_mean.py
186 lines (129 loc) · 4.8 KB
/
process_pr_ev_mean.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
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import glob
import netCDF4 as nc
import datetime as dt
import sys
import gsw as sw
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import cftime
import coast
import xarray as xr
# In[ ]:
var = 'evspsbl' # thetao, so, uo, vo, evap, siage, sivol, prec, siu, siv,
i_o = 'A' # SI or O for sea ice or ocean
freq = 'mon' # mon or day
time_s = 'highres-future' # 'highres-future' or 'hist-1950'
def make_path(var, i_o, freq, time_s):
if 'future' in time_s:
ddir = 'MOHC'
else:
ddir = 'NERC'
root = '/badc/cmip6/data/CMIP6/HighResMIP/' + ddir + '/HadGEM3-GC31-HH/' + time_s + '/r1i1p1f1/'
return root + i_o + freq + '/' + var + '/gn/latest/' + var + '_' + i_o + freq + '_HadGEM3-GC31-HH_' + time_s + '_r1i1p1f1_gn_*.nc'
fn_nemo_dat_evap1 = make_path(var, i_o, freq, 'hist-1950')
fn_nemo_dat_prec1 = make_path('pr', i_o, freq, 'hist-1950')
fn_nemo_dat_evap2 = make_path(var, i_o, freq, time_s)
fn_nemo_dat_prec2 = make_path('pr', i_o, freq, time_s)
domain_root = '/gws/nopw/j04/nemo_vol5/acc/eORCA12-N512/domain/'
fn_nemo_dom1 = domain_root + 'eORCA12_coordinates.nc'
fn_nemo_dom = domain_root + 'mesh_mask_eORCA12_v2.4.nc'
fn_nemo_bathy = domain_root + 'eORCA12_bathymetry_v2.4.nc'
fn_config_t_grid = './config/gc31_nemo_grid_t.json'
out_file = './Processed/'
# In[ ]:
# Define start and end date for decade mean
if 1:
st_date = dt.datetime(1990, 1, 1)
en_date = dt.datetime(2000, 1, 1)
else:
st_date = dt.datetime(2040, 1, 1)
en_date = dt.datetime(2050, 1, 1)
# In[ ]:
now = dt.datetime.now()
flist_evap = sorted(glob.glob(fn_nemo_dat_evap1))
flist_prec = sorted(glob.glob(fn_nemo_dat_prec1))
flist_evap.extend(sorted(glob.glob(fn_nemo_dat_evap2)))
flist_prec.extend(sorted(glob.glob(fn_nemo_dat_prec2)))
v_map = {}
v_map['e1t'] = 'e1t'
v_map['e2t'] = 'e2t'
v_map['e3t_0'] = 'e3t_0'
v_map['tmask'] = 'tmask'
v_map['lat'] = 'lat'
v_map['lon'] = 'lon'
v_map['depth'] = 'lev'
v_map['time'] = 'time'
v_map['temp'] = 'thetao'
v_map['sal'] = 'so'
v_map['evap'] = 'evspsbl'
v_map['prec'] = 'pr'
with nc.Dataset(flist_evap[0], 'r') as nc_fid:
evap = nc_fid.variables[v_map['evap']][0, ...]
with nc.Dataset(flist_prec[0], 'r') as nc_fid:
prec = nc_fid.variables[v_map['prec']][0, ...]
lat = nc_fid.variables[v_map['lat']][:]
lon = nc_fid.variables[v_map['lon']][:]
# Subset data
# In[ ]:
# Meshgrid lat and lon
lon, lat = np.meshgrid(lon, lat)
# In[ ]:
lon_bnds, lat_bnds = (0, 360), (60, 90)
yi1 = np.min(np.nonzero((lat >= lat_bnds[0]))[0])
lat = lat[yi1:, :]
lon = lon[yi1:, :]
evap = evap[yi1:, :]
prec = prec[yi1:, :]
# Time slice. Notes dates are 360 day years so use cftime
# In[ ]:
# change this to decrease resolution but decrease run time
sub = 1
lat = lat[::sub, ::sub]
lon = lon[::sub, ::sub]
evap = evap[::sub, ::sub]
prec = prec[::sub, ::sub]
print(lon.shape)
date_from = np.zeros((len(flist_evap)), dtype=object)
date_to = np.zeros((len(flist_evap)), dtype=object)
for i in range(len(flist_evap)):
part = flist_evap[i].split('_')[-1].split('.')[0].split('-')
date_from[i] = dt.datetime.strptime(part[0], '%Y%m')
date_to[i] = dt.datetime.strptime(part[1], '%Y%m')
date_use = np.nonzero((date_from >= st_date) & (date_to < en_date))[0]
date_from = date_from[date_use]
str_year = str(st_date.year) + '-' + str(en_date.year)
# output a decacal monthly mean
mn = np.array([t.month for t in date_from])
yr = np.array([t.year for t in date_from])
yr_uni = np.unique(yr)
ref = 'days since 1950-01-01'
date = np.zeros((len(flist_evap)), dtype=object)
evap_time = np.ma.zeros((12, lon.shape[0], lon.shape[1]))
prec_time = np.ma.zeros((12, lon.shape[0], lon.shape[1]))
for m in range(1, 13):
for y in yr_uni:
ind = np.nonzero(yr == y)[0][0] + date_use[0]
with nc.Dataset(flist_evap[ind], 'r') as nc_fid:
evap = nc_fid.variables[v_map['evap']][m-1, yi1::sub, ::sub] # time, lev, j, i
time = nc_fid.variables[v_map['time']][m-1]
with nc.Dataset(flist_prec[ind], 'r') as nc_fid:
prec = nc_fid.variables[v_map['prec']][m-1, yi1::sub, ::sub]
evap = np.ma.masked_where((evap==1e20), evap)
prec = np.ma.masked_where((prec==1e20), prec)
evap_time[m-1, :, :] = evap_time[m-1, :, :] + evap
prec_time[m-1, :, :] = prec_time[m-1, :, :] + prec
date[i] = cftime.num2date(time, ref, calendar='360_day')
print(date[i])
evap_time = evap_time / len(yr_uni)
prec_time = prec_time / len(yr_uni)
evap_time = evap_time.filled(-1e20)
prec_time = prec_time.filled(-1e20)
np.savez(out_file + 'pr_ev_mn_' + str_year + '.npz', evap_map=evap_time, prec_map=prec_time, lat=lat, lon=lon)