cesm_racmo23_t2m_ann.py 8.09 KB
Newer Older
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
import os
import Ngl
import numpy as np
import numpy.ma as ma

from netCDF4 import Dataset

from livvkit.util import elements as el


describe = """CESM_RACMO23_t2m_ann plot."""


def make_plot(config=None, out_path='.',
              racmo_path='/lustre/atlas1/cli115/world-shared/4ue/racmo23_GRN_monthly/',
              cism_path='/lustre/atlas1/cli115/world-shared/4ue/',
              cesm_path='/lustre/atlas1/cli115/world-shared/4ue/b.e10.BG20TRCN.f09_g16.002/'):
    # ---------------- Data source in TITAN ------------------------
    f_cism = os.path.join(cism_path, 'Greenland_5km_v1.1_SacksRev_c110629.nc')
    f_cesm_lnd_climo_ann = os.path.join(cesm_path, 'postproc/lnd/climos/b.e10.BG20TRCN.f09_g16.002_ANN_climo.nc')
    f_cesm_atm_climo_ann = os.path.join(cesm_path, 'postproc/atm/climos/b.e10.BG20TRCN.f09_g16.002_ANN_climo.nc')
    f_racmo_t2m_ann = os.path.join(racmo_path, 'climos/racmo23_GRN_monthly.t2m.1980-1999.ANN.nc')
    f_racmo_t2m_ann_remapped = os.path.join(racmo_path,
                                            'remapped_racmo/racmo23_GRN_monthly.t2m.1980-1999.remap2cesm.ANN.nc')
    f_racmo_mask = os.path.join(racmo_path, 'RACMO23_masks_ZGRN11.nc')
    # --------------------------------------------------------------

    img_list = []

    # read f_cism, the elevation data
    ncid0 = Dataset(f_cism)
    usrf = ncid0.variables['usrf'][0, :, :]
    lat = ncid0.variables['lat'][0, :, :]
    lon = ncid0.variables['lon'][0, :, :]

    # read f_cesm_atm_climo_ann and file2, CESM variable
    ncid1 = Dataset(f_cesm_atm_climo_ann)
    w_cesm = ncid1.variables['TREFHT'][0, :, :]
    lat1 = ncid1.variables['lat'][:]
    lon1 = ncid1.variables['lon'][:]

    # --- original RACMO data without remapping [312,306]
    ncid2 = Dataset(f_racmo_t2m_ann)
    w_racmo_l = ncid2.variables['t2m'][0, :, :]
    # lat2 = ncid2.variables['lat'][:]
    # lon2 = ncid2.variables['lon'][:]

    # read f_racmo_mask, the lat/lon for RACMO data
    ncid4 = Dataset(f_racmo_mask)
    lat4 = ncid4.variables['lat'][:]
    lon4 = ncid4.variables['lon'][:]
    racmo_elev = ncid4.variables['Topography'][0, 0, :, :]

    # RACMO data after remapping [192,288]
    ncid22 = Dataset(f_racmo_t2m_ann_remapped)
    w_racmo = ncid22.variables['t2m'][0, :, :]
    lat22 = ncid22.variables['lat'][:]
    lon22 = ncid22.variables['lon'][:]

    w_cesm = w_cesm - 273.15  # convert to celcius
    w_racmo = w_racmo - 273.15
    w_racmo_l = w_racmo_l - 273.15
    diff = w_cesm - w_racmo

    # locate the greenland area using the GreenLand mask
    ncid8 = Dataset(f_cesm_lnd_climo_ann)
    gris_mask = ncid8.variables['gris_mask'][0, :, :]
    gris_mask = ma.masked_equal(gris_mask, 0)

    # mask out non RACMO regions, icemask var in f_cesm_lnd_climo_ann
    diff_mask = ma.masked_array(diff, mask=gris_mask.mask)

    # print np.max(diff_mask)
    # print np.min(diff_mask)

    # ------- PLOT --------
    # Open a workstation for drawing the plots
    wkres = Ngl.Resources()
    wkres.wkColorMap = "BlueWhiteOrangeRed"
80
    # wkres.wkOrientation = "portrait"  # "portrait" or "landscape"
81
    wks_type = "png"
82
    wks_img = str(os.path.join(out_path, "CESM_RACMO23_t2m_ANN"))
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
    wks = Ngl.open_wks(wks_type, wks_img, wkres)

    # --- for the map -------
    # Define plotting area, Greenland
    mres = Ngl.Resources()
    mres.nglDraw = False  # Don't draw individual plots
    mres.nglFrame = False  # Don't advance frame.

    mres.pmTickMarkDisplayMode = "Never"  # Turn off map tickmarks.
    mres.mpGridAndLimbOn = False  # Turn off grid and limb lines.
    mres.mpProjection = "Aitoff"
    mres.mpLimitMode = "LatLon"  # limit map via lat/lon, to zoom in
    mres.mpCenterLatF = 70.  # map area
    mres.mpCenterLonF = -44.
    mres.mpMinLatF = 57.
    mres.mpMaxLatF = 85.
    mres.mpMinLonF = -55.
    mres.mpMaxLonF = -30.
    mres.mpOutlineOn = False
    mres.mpFillOn = False
    mres.mpPerimOn = True  # add box around map

    # --- for the CESM contour -------
    res1 = Ngl.Resources()
    res1.nglDraw = False  # Don't draw individual plots
    res1.nglFrame = False  # Don't advance frame.
    res1.cnLineLabelsOn = False
    res1.cnFillOn = True
    res1.cnLinesOn = False
    res1.cnLineLabelsOn = False
    res1.cnFillMode = "RasterFill"
    res1.trGridType = "TriangularMesh"
    res1.cnLevelSelectionMode = "ExplicitLevels"
    res1.cnLevels = np.arange(-44, 12, 4)
    res1.lbLabelBarOn = True  # Turn on labelbar.
    res1.lbLabelFontHeightF = 0.04

    res1.sfXArray = lon1
    res1.sfYArray = lat1

    # --- for the RACMO contour -------
    res2 = Ngl.Resources()
    res2.nglDraw = False  # Don't draw individual plots
    res2.nglFrame = False  # Don't advance frame.
    res2.cnLineLabelsOn = False
    res2.cnFillOn = True
    res2.cnLinesOn = False
    res2.cnLineLabelsOn = False
    res2.cnFillMode = "RasterFill"
    res2.trGridType = "TriangularMesh"
    res2.cnLevelSelectionMode = "ExplicitLevels"
    res2.cnLevels = np.arange(-44, 12, 4)
    res2.lbLabelBarOn = True  # Turn on labelbar.
    res2.lbOrientation = "Vertical"  # Verticle labelbar
    res2.lbLabelFontHeightF = 0.04  # Make fonts smaller.

    res2.sfXArray = lon4
    res2.sfYArray = lat4

    # --- for the diff=CESM-Remapped_RACMO contour -------
    res22 = Ngl.Resources()
    res22.nglDraw = False  # Don't draw individual plots
    res22.nglFrame = False  # Don't advance frame.
    res22.cnLineLabelsOn = False
    res22.cnFillOn = True
    res22.cnLinesOn = False
    res22.cnLineLabelsOn = False
    res22.cnFillMode = "RasterFill"
    res22.trGridType = "TriangularMesh"
    res22.cnLevelSelectionMode = "ExplicitLevels"
    res22.cnLevels = np.arange(-12, 12, 2)
    res22.lbLabelBarOn = True  # Turn on labelbar.
    res22.lbOrientation = "Vertical"  # Verticle labelbar
    res22.lbLabelFontHeightF = 0.04  # Make fonts smaller.

    res22.sfXArray = lon22
    res22.sfYArray = lat22

    # ---- for the elevation -------
    sres = Ngl.Resources()
    sres.nglDraw = False  # Don't draw individual plots
    sres.nglFrame = False  # Don't advance frame.
    sres.cnFillOn = False
    sres.cnLinesOn = True
    sres.cnLineLabelsOn = False
    sres.cnLevelSelectionMode = "ExplicitLevels"
    sres.cnLevels = [0, 1000, 2000, 3000]
    sres.sfXArray = lon
    sres.sfYArray = lat

    # ---- for the elevation of RACMO -------
    sres1 = Ngl.Resources()
    sres1.nglDraw = False  # Don't draw individual plots
    sres1.nglFrame = False  # Don't advance frame.
    sres1.cnFillOn = False
    sres1.cnLinesOn = True
    sres1.cnLineLabelsOn = False
    sres1.cnLevelSelectionMode = "ExplicitLevels"
    sres1.cnLevels = [0, 1000, 2000, 3000]
    sres1.sfXArray = lon4
    sres1.sfYArray = lat4

    # ---- Overlay plots, each one has its own ID
    # overlay ice on base map, and then overlay elevation on ice
    usrf_plot1 = Ngl.contour(wks, usrf, sres)
    usrf_plot2 = Ngl.contour(wks, racmo_elev, sres1)
    usrf_plot3 = Ngl.contour(wks, usrf, sres)

    t2mw_plot = Ngl.contour(wks, w_cesm, res1)
    t2ms_l_plot = Ngl.contour(wks, w_racmo_l, res2)
    diff_plot = Ngl.contour(wks, diff_mask, res22)
    # diff_plot = Ngl.contour(wks,w_racmo,res2)  #Remapped RACMO

    # Creat multiple figures and draw, which now contains the elevation and temperature
    # "[1,3]" indicates 1 row, 3 columns.
    map_title = ["CESM T(~S1~o C )", "RACMO T(~S1~o C )", "CESM-RACMO T(~S1~o C )"]

    nmap = 3
    plot = []
    for i in range(nmap):
        mres.tiMainString = map_title[i]
        plot.append(Ngl.map(wks, mres))

    # Overlay everything on the map plot.
    Ngl.overlay(plot[0], t2mw_plot)
    Ngl.overlay(plot[0], usrf_plot1)
    Ngl.overlay(plot[1], t2ms_l_plot)
    Ngl.overlay(plot[1], usrf_plot2)
    Ngl.overlay(plot[2], diff_plot)
    Ngl.overlay(plot[2], usrf_plot3)

    Ngl.panel(wks, plot, [1, nmap])

    img_link = os.path.join(os.path.basename(out_path),
                            os.path.basename(wks_img + '.' + wks_type))
    img_elem = el.image('CESM_RACMO23_t2m_ann',
                        ' '.join(describe.split()),
                        img_link)
    if config:
        img_elem['Height'] = config['image_height']
    img_list.append(img_elem)

    return img_list


if __name__ == '__main__':
    make_plot()