forked from cmkaul/SCAMPy
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathEDMF_Updrafts.pyx
555 lines (474 loc) · 25.7 KB
/
EDMF_Updrafts.pyx
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
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: initializedcheck=True
#cython: cdivision=False
import numpy as np
include "parameters.pxi"
from thermodynamic_functions cimport *
from microphysics_functions cimport *
import cython
cimport Grid
cimport ReferenceState
cimport EDMF_Rain
from Variables cimport GridMeanVariables
from NetCDFIO cimport NetCDFIO_Stats
from EDMF_Environment cimport EnvironmentVariables
from libc.math cimport fmax, fmin
cdef class UpdraftVariable:
def __init__(self, nu, nz, loc, kind, name, units):
self.values = np.zeros((nu,nz),dtype=np.double, order='c')
self.old = np.zeros((nu,nz),dtype=np.double, order='c') # needed for prognostic updrafts
self.new = np.zeros((nu,nz),dtype=np.double, order='c') # needed for prognostic updrafts
self.tendencies = np.zeros((nu,nz),dtype=np.double, order='c')
self.flux = np.zeros((nu,nz),dtype=np.double, order='c')
self.bulkvalues = np.zeros((nz,), dtype=np.double, order = 'c')
if loc != 'half' and loc != 'full':
print('Invalid location setting for variable! Must be half or full')
self.loc = loc
if kind != 'scalar' and kind != 'velocity':
print ('Invalid kind setting for variable! Must be scalar or velocity')
self.kind = kind
self.name = name
self.units = units
cpdef set_bcs(self,Grid.Grid Gr):
cdef:
Py_ssize_t i,k
Py_ssize_t start_low = Gr.gw - 1
Py_ssize_t start_high = Gr.nzg - Gr.gw - 1
n_updrafts = np.shape(self.values)[0]
if self.name == 'w':
for i in xrange(n_updrafts):
self.values[i,start_high] = 0.0
self.values[i,start_low] = 0.0
for k in xrange(1,Gr.gw):
self.values[i,start_high+ k] = -self.values[i,start_high - k ]
self.values[i,start_low- k] = -self.values[i,start_low + k ]
else:
for k in xrange(Gr.gw):
for i in xrange(n_updrafts):
self.values[i,start_high + k +1] = self.values[i,start_high - k]
self.values[i,start_low - k] = self.values[i,start_low + 1 + k]
return
cdef class UpdraftVariables:
def __init__(self, nu, namelist, paramlist, Grid.Grid Gr):
self.Gr = Gr
self.n_updrafts = nu
cdef:
Py_ssize_t nzg = Gr.nzg
Py_ssize_t i, k
self.W = UpdraftVariable(nu, nzg, 'full', 'velocity', 'w','m/s' )
self.Area = UpdraftVariable(nu, nzg, 'half', 'scalar', 'area_fraction','[-]' )
self.QT = UpdraftVariable(nu, nzg, 'half', 'scalar', 'qt','kg/kg' )
self.QL = UpdraftVariable(nu, nzg, 'half', 'scalar', 'ql','kg/kg' )
self.RH = UpdraftVariable(nu, nzg, 'half', 'scalar', 'RH','%' )
if namelist['thermodynamics']['thermal_variable'] == 'entropy':
self.H = UpdraftVariable(nu, nzg, 'half', 'scalar', 's','J/kg/K' )
elif namelist['thermodynamics']['thermal_variable'] == 'thetal':
self.H = UpdraftVariable(nu, nzg, 'half', 'scalar', 'thetal','K' )
self.THL = UpdraftVariable(nu, nzg, 'half', 'scalar', 'thetal', 'K')
self.T = UpdraftVariable(nu, nzg, 'half', 'scalar', 'temperature','K' )
self.B = UpdraftVariable(nu, nzg, 'half', 'scalar', 'buoyancy','m^2/s^3' )
if namelist['turbulence']['scheme'] == 'EDMF_PrognosticTKE':
try:
use_steady_updrafts = namelist['turbulence']['EDMF_PrognosticTKE']['use_steady_updrafts']
except:
use_steady_updrafts = False
if use_steady_updrafts:
self.prognostic = False
else:
self.prognostic = True
self.updraft_fraction = paramlist['turbulence']['EDMF_PrognosticTKE']['surface_area']
else:
self.prognostic = False
self.updraft_fraction = paramlist['turbulence']['EDMF_BulkSteady']['surface_area']
# cloud and rain diagnostics for output
self.cloud_fraction = np.zeros((nzg,), dtype=np.double, order='c')
self.cloud_base = np.zeros((nu,), dtype=np.double, order='c')
self.cloud_top = np.zeros((nu,), dtype=np.double, order='c')
self.cloud_cover = np.zeros((nu,), dtype=np.double, order='c')
self.updraft_top = np.zeros((nu,), dtype=np.double, order='c')
self.lwp = 0.
return
cpdef initialize(self, GridMeanVariables GMV):
cdef:
Py_ssize_t i,k
Py_ssize_t gw = self.Gr.gw
double dz = self.Gr.dz
with nogil:
for i in xrange(self.n_updrafts):
for k in xrange(self.Gr.nzg):
self.W.values[i,k] = 0.0
# Simple treatment for now, revise when multiple updraft closures
# become more well defined
if self.prognostic:
self.Area.values[i,k] = 0.0 #self.updraft_fraction/self.n_updrafts
else:
self.Area.values[i,k] = self.updraft_fraction/self.n_updrafts
self.QT.values[i,k] = GMV.QT.values[k]
self.QL.values[i,k] = GMV.QL.values[k]
self.H.values[i,k] = GMV.H.values[k]
self.T.values[i,k] = GMV.T.values[k]
self.B.values[i,k] = 0.0
self.Area.values[i,gw] = self.updraft_fraction/self.n_updrafts
self.QT.set_bcs(self.Gr)
self.H.set_bcs(self.Gr)
return
cpdef initialize_DryBubble(self, GridMeanVariables GMV, ReferenceState.ReferenceState Ref):
cdef:
Py_ssize_t i,k
Py_ssize_t gw = self.Gr.gw
double dz = self.Gr.dz
# criterion 2: b>1e-4
z_in = np.array([
75., 125., 175., 225., 275., 325., 375., 425., 475.,
525., 575., 625., 675., 725., 775., 825., 875., 925.,
975., 1025., 1075., 1125., 1175., 1225., 1275., 1325., 1375.,
1425., 1475., 1525., 1575., 1625., 1675., 1725., 1775., 1825.,
1875., 1925., 1975., 2025., 2075., 2125., 2175., 2225., 2275.,
2325., 2375., 2425., 2475., 2525., 2575., 2625., 2675., 2725.,
2775., 2825., 2875., 2925., 2975., 3025., 3075., 3125., 3175.,
3225., 3275., 3325., 3375., 3425., 3475., 3525., 3575., 3625.,
3675., 3725., 3775., 3825., 3875., 3925.
])
thetal_in = np.array([
299.9882, 299.996 , 300.0063, 300.0205, 300.04 , 300.0594,
300.0848, 300.1131, 300.1438, 300.1766, 300.2198, 300.2567,
300.2946, 300.3452, 300.3849, 300.4245, 300.4791, 300.5182,
300.574 , 300.6305, 300.6668, 300.7222, 300.7771, 300.8074,
300.8591, 300.9092, 300.9574, 300.9758, 301.0182, 301.0579,
301.0944, 301.1276, 301.1572, 301.1515, 301.1729, 301.1902,
301.2033, 301.2122, 301.2167, 301.2169, 301.2127, 301.2041,
301.1913, 301.1743, 301.1533, 301.1593, 301.1299, 301.097 ,
301.0606, 301.0212, 300.9788, 300.9607, 300.9125, 300.8625,
300.8108, 300.7806, 300.7256, 300.6701, 300.6338, 300.5772,
300.5212, 300.482 , 300.4272, 300.3875, 300.3354, 300.2968,
300.2587, 300.2216, 300.1782, 300.1452, 300.1143, 300.0859,
300.0603, 300.0408, 300.0211, 300.0067, 299.9963, 299.9884
])
Area_in = np.array([
0.04 , 0.055, 0.07 , 0.08 , 0.085, 0.095, 0.1 , 0.105, 0.11 ,
0.115, 0.115, 0.12 , 0.125, 0.125, 0.13 , 0.135, 0.135, 0.14 ,
0.14 , 0.14 , 0.145, 0.145, 0.145, 0.15 , 0.15 , 0.15 , 0.15 ,
0.155, 0.155, 0.155, 0.155, 0.155, 0.155, 0.16 , 0.16 , 0.16 ,
0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 ,
0.155, 0.155, 0.155, 0.155, 0.155, 0.155, 0.15 , 0.15 , 0.15 ,
0.15 , 0.145, 0.145, 0.145, 0.14 , 0.14 , 0.14 , 0.135, 0.135,
0.13 , 0.13 , 0.125, 0.12 , 0.115, 0.115, 0.11 , 0.105, 0.1 ,
0.095, 0.085, 0.08 , 0.07 , 0.055, 0.04
])
W_in = np.array([
0.017 , 0.0266, 0.0344, 0.0417, 0.0495, 0.0546, 0.061 , 0.0668,
0.0721, 0.0768, 0.0849, 0.0887, 0.092 , 0.0996, 0.1019, 0.1037,
0.1106, 0.1114, 0.1179, 0.1243, 0.1238, 0.1297, 0.1355, 0.1335,
0.1387, 0.1437, 0.1485, 0.1448, 0.1489, 0.1527, 0.1564, 0.1597,
0.1628, 0.1565, 0.1588, 0.1609, 0.1626, 0.1641, 0.1652, 0.166 ,
0.1665, 0.1667, 0.1666, 0.1662, 0.1655, 0.1736, 0.1722, 0.1706,
0.1686, 0.1664, 0.1639, 0.1698, 0.1667, 0.1634, 0.1599, 0.1641,
0.1601, 0.1559, 0.1589, 0.1543, 0.1496, 0.1514, 0.1464, 0.1475,
0.1422, 0.1425, 0.1424, 0.1419, 0.1361, 0.135 , 0.1335, 0.1316,
0.1294, 0.1302, 0.1271, 0.1264, 0.1269, 0.1256
])
T_in = np.array([
299.2557, 298.775 , 298.2969, 297.8227, 297.3536, 296.8843,
296.421 , 295.9603, 295.502 , 295.0456, 294.5994, 294.1468,
293.6951, 293.2556, 292.8054, 292.3549, 291.9188, 291.4677,
291.0325, 290.5978, 290.1434, 289.7073, 289.2706, 288.81 ,
288.3698, 287.928 , 287.4842, 287.0118, 286.5622, 286.1099,
285.6544, 285.1957, 284.7335, 284.2379, 283.7677, 283.2937,
282.8157, 282.3337, 281.8476, 281.3574, 280.8631, 280.3649,
279.8626, 279.3565, 278.8467, 278.362 , 277.8447, 277.3241,
276.8006, 276.2742, 275.7454, 275.2388, 274.705 , 274.1694,
273.6327, 273.1155, 272.576 , 272.0363, 271.514 , 270.9736,
270.4339, 269.9094, 269.3711, 268.8465, 268.311 , 267.7877,
267.2649, 266.7432, 266.2159, 265.698 , 265.1821, 264.6685,
264.1574, 263.6518, 263.1461, 262.6451, 262.1476, 261.6524
])
Area_in = np.interp(self.Gr.z_half,z_in,Area_in)
thetal_in = np.interp(self.Gr.z_half,z_in,thetal_in)
T_in = np.interp(self.Gr.z_half,z_in,T_in)
for i in xrange(self.n_updrafts):
for k in xrange(self.Gr.nzg):
if z_in.min()<=self.Gr.z_half[k]<=z_in.max():
self.W.values[i,k] = 0.0
self.Area.values[i,k] = Area_in[k] #self.updraft_fraction/self.n_updrafts
self.H.values[i,k] = thetal_in[k]
self.QT.values[i,k] = 0.0
self.QL.values[i,k] = 0.0
self.T.values[i,k] = T_in[k]
# for now temperature is provided as diagnostics from LES
# sa = eos(
# t_to_thetali_c,
# eos_first_guess_thetal,
# Ref.p0_half[k],
# self.QT.values[i,k],
# self.H.values[i,k]
# )
# self.T.values[i,k] = sa.T
else:
self.Area.values[i,k] = 0.0 #self.updraft_fraction/self.n_updrafts
self.H.values[i,k] = GMV.THL.values[k]
self.T.values[i,k] = GMV.T.values[k]
self.QT.set_bcs(self.Gr)
self.H.set_bcs(self.Gr)
self.W.set_bcs(self.Gr)
self.T.set_bcs(self.Gr)
self.set_means(GMV)
return
cpdef initialize_io(self, NetCDFIO_Stats Stats):
Stats.add_profile('updraft_area')
Stats.add_profile('updraft_w')
Stats.add_profile('updraft_qt')
Stats.add_profile('updraft_ql')
Stats.add_profile('updraft_RH')
if self.H.name == 'thetal':
Stats.add_profile('updraft_thetal')
else:
# Stats.add_profile('updraft_thetal')
Stats.add_profile('updraft_s')
Stats.add_profile('updraft_temperature')
Stats.add_profile('updraft_buoyancy')
Stats.add_profile('updraft_cloud_fraction')
Stats.add_ts('updraft_cloud_cover')
Stats.add_ts('updraft_cloud_base')
Stats.add_ts('updraft_cloud_top')
Stats.add_ts('updraft_lwp')
return
cpdef set_means(self, GridMeanVariables GMV):
cdef:
Py_ssize_t i, k
self.Area.bulkvalues = np.sum(self.Area.values,axis=0)
self.W.bulkvalues[:] = 0.0
self.QT.bulkvalues[:] = 0.0
self.QL.bulkvalues[:] = 0.0
self.H.bulkvalues[:] = 0.0
self.T.bulkvalues[:] = 0.0
self.B.bulkvalues[:] = 0.0
self.RH.bulkvalues[:] = 0.0
with nogil:
for k in xrange(self.Gr.gw, self.Gr.nzg-self.Gr.gw):
if self.Area.bulkvalues[k] > 1.0e-20:
for i in xrange(self.n_updrafts):
self.QT.bulkvalues[k] += self.Area.values[i,k] * self.QT.values[i,k]/self.Area.bulkvalues[k]
self.QL.bulkvalues[k] += self.Area.values[i,k] * self.QL.values[i,k]/self.Area.bulkvalues[k]
self.H.bulkvalues[k] += self.Area.values[i,k] * self.H.values[i,k]/self.Area.bulkvalues[k]
self.T.bulkvalues[k] += self.Area.values[i,k] * self.T.values[i,k]/self.Area.bulkvalues[k]
self.RH.bulkvalues[k] += self.Area.values[i,k] * self.RH.values[i,k]/self.Area.bulkvalues[k]
self.B.bulkvalues[k] += self.Area.values[i,k] * self.B.values[i,k]/self.Area.bulkvalues[k]
self.W.bulkvalues[k] += ((self.Area.values[i,k] + self.Area.values[i,k+1]) * self.W.values[i,k]
/(self.Area.bulkvalues[k] + self.Area.bulkvalues[k+1]))
else:
self.QT.bulkvalues[k] = GMV.QT.values[k]
self.QL.bulkvalues[k] = 0.0
self.H.bulkvalues[k] = GMV.H.values[k]
self.RH.bulkvalues[k] = GMV.RH.values[k]
self.T.bulkvalues[k] = GMV.T.values[k]
self.B.bulkvalues[k] = 0.0
self.W.bulkvalues[k] = 0.0
if self.QL.bulkvalues[k] > 1e-8 and self.Area.bulkvalues[k] > 1e-3:
self.cloud_fraction[k] = 1.0
else:
self.cloud_fraction[k] = 0.
return
# quick utility to set "new" arrays with values in the "values" arrays
cpdef set_new_with_values(self):
with nogil:
for i in xrange(self.n_updrafts):
for k in xrange(self.Gr.nzg):
self.W.new[i,k] = self.W.values[i,k]
self.Area.new[i,k] = self.Area.values[i,k]
self.QT.new[i,k] = self.QT.values[i,k]
self.QL.new[i,k] = self.QL.values[i,k]
self.H.new[i,k] = self.H.values[i,k]
self.THL.new[i,k] = self.THL.values[i,k]
self.T.new[i,k] = self.T.values[i,k]
self.B.new[i,k] = self.B.values[i,k]
return
# quick utility to set "new" arrays with values in the "values" arrays
cpdef set_old_with_values(self):
with nogil:
for i in xrange(self.n_updrafts):
for k in xrange(self.Gr.nzg):
self.W.old[i,k] = self.W.values[i,k]
self.Area.old[i,k] = self.Area.values[i,k]
self.QT.old[i,k] = self.QT.values[i,k]
self.QL.old[i,k] = self.QL.values[i,k]
self.H.old[i,k] = self.H.values[i,k]
self.THL.old[i,k] = self.THL.values[i,k]
self.T.old[i,k] = self.T.values[i,k]
self.B.old[i,k] = self.B.values[i,k]
return
# quick utility to set "tmp" arrays with values in the "new" arrays
cpdef set_values_with_new(self):
with nogil:
for i in xrange(self.n_updrafts):
for k in xrange(self.Gr.nzg):
self.W.values[i,k] = self.W.new[i,k]
self.Area.values[i,k] = self.Area.new[i,k]
self.QT.values[i,k] = self.QT.new[i,k]
self.QL.values[i,k] = self.QL.new[i,k]
self.H.values[i,k] = self.H.new[i,k]
self.THL.values[i,k] = self.THL.new[i,k]
self.T.values[i,k] = self.T.new[i,k]
self.B.values[i,k] = self.B.new[i,k]
return
cpdef io(self, NetCDFIO_Stats Stats, ReferenceState.ReferenceState Ref):
Stats.write_profile('updraft_area', self.Area.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_w', self.W.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_qt', self.QT.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_ql', self.QL.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_RH', self.RH.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
if self.H.name == 'thetal':
Stats.write_profile('updraft_thetal', self.H.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
else:
Stats.write_profile('updraft_s', self.H.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
#Stats.write_profile('updraft_thetal', self.THL.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_temperature', self.T.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
Stats.write_profile('updraft_buoyancy', self.B.bulkvalues[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
self.upd_cloud_diagnostics(Ref)
Stats.write_profile('updraft_cloud_fraction', self.cloud_fraction[self.Gr.gw:self.Gr.nzg-self.Gr.gw])
# Note definition of cloud cover : each updraft is associated with a cloud cover equal to the maximum
# area fraction of the updraft where ql > 0. Each updraft is assumed to have maximum overlap with respect to
# itself (i.e. no consideration of tilting due to shear) while the updraft classes are assumed to have no overlap
# at all. Thus total updraft cover is the sum of each updraft's cover
Stats.write_ts('updraft_cloud_cover', np.sum(self.cloud_cover))
Stats.write_ts('updraft_cloud_base', np.amin(self.cloud_base))
Stats.write_ts('updraft_cloud_top', np.amax(self.cloud_top))
Stats.write_ts('updraft_lwp', self.lwp)
return
cpdef upd_cloud_diagnostics(self, ReferenceState.ReferenceState Ref):
cdef Py_ssize_t i, k
self.lwp = 0.
for i in xrange(self.n_updrafts):
#TODO check the setting of ghost point z_half
self.cloud_base[i] = self.Gr.z_half[self.Gr.nzg-self.Gr.gw-1]
self.cloud_top[i] = 0.0
self.updraft_top[i] = 0.0
self.cloud_cover[i] = 0.0
for k in xrange(self.Gr.gw,self.Gr.nzg-self.Gr.gw):
if self.Area.values[i,k] > 1e-3:
self.updraft_top[i] = fmax(self.updraft_top[i], self.Gr.z_half[k])
self.lwp += Ref.rho0_half[k] * self.QL.values[i,k] * self.Area.values[i,k] * self.Gr.dz
if self.QL.values[i,k] > 1e-8:
self.cloud_base[i] = fmin(self.cloud_base[i], self.Gr.z_half[k])
self.cloud_top[i] = fmax(self.cloud_top[i], self.Gr.z_half[k])
self.cloud_cover[i] = fmax(self.cloud_cover[i], self.Area.values[i,k])
return
cdef class UpdraftThermodynamics:
def __init__(self, n_updraft, Grid.Grid Gr,
ReferenceState.ReferenceState Ref, UpdraftVariables UpdVar,
RainVariables Rain):
self.Gr = Gr
self.Ref = Ref
self.n_updraft = n_updraft
if UpdVar.H.name == 's':
self.t_to_prog_fp = t_to_entropy_c
self.prog_to_t_fp = eos_first_guess_entropy
elif UpdVar.H.name == 'thetal':
self.t_to_prog_fp = t_to_thetali_c
self.prog_to_t_fp = eos_first_guess_thetal
# rain source from each updraft from all sub-timesteps
self.prec_source_h = np.zeros((n_updraft, Gr.nzg), dtype=np.double, order='c')
self.prec_source_qt = np.zeros((n_updraft, Gr.nzg), dtype=np.double, order='c')
# rain source from all updrafts from all sub-timesteps
self.prec_source_h_tot = np.zeros((Gr.nzg,), dtype=np.double, order='c')
self.prec_source_qt_tot = np.zeros((Gr.nzg,), dtype=np.double, order='c')
return
cpdef clear_precip_sources(self):
"""
clear precipitation source terms for QT and H from each updraft
"""
self.prec_source_qt[:,:] = 0.
self.prec_source_h[:,:] = 0.
return
cpdef update_total_precip_sources(self):
"""
sum precipitation source terms for QT and H from all sub-timesteps
"""
self.prec_source_h_tot = np.sum(self.prec_source_h, axis=0)
self.prec_source_qt_tot = np.sum(self.prec_source_qt, axis=0)
return
cpdef buoyancy(self, UpdraftVariables UpdVar, EnvironmentVariables EnvVar,
GridMeanVariables GMV, bint extrap):
cdef:
Py_ssize_t k, i
double rho, qv, qt, t, h
Py_ssize_t gw = self.Gr.gw
UpdVar.Area.bulkvalues = np.sum(UpdVar.Area.values,axis=0)
if not extrap:
with nogil:
for i in xrange(self.n_updraft):
for k in xrange(self.Gr.nzg):
if UpdVar.Area.values[i,k] > 0.0:
qv = UpdVar.QT.values[i,k] - UpdVar.QL.values[i,k]
rho = rho_c(self.Ref.p0_half[k], UpdVar.T.values[i,k], UpdVar.QT.values[i,k], qv)
UpdVar.B.values[i,k] = buoyancy_c(self.Ref.rho0_half[k], rho)
else:
UpdVar.B.values[i,k] = EnvVar.B.values[k]
UpdVar.RH.values[i,k] = relative_humidity_c(self.Ref.p0_half[k], UpdVar.QT.values[i,k],
UpdVar.QL.values[i,k], 0.0, UpdVar.T.values[i,k])
else:
with nogil:
for i in xrange(self.n_updraft):
for k in xrange(self.Gr.gw, self.Gr.nzg-self.Gr.gw):
if UpdVar.Area.values[i,k] > 0.0:
qt = UpdVar.QT.values[i,k]
qv = UpdVar.QT.values[i,k] - UpdVar.QL.values[i,k]
h = UpdVar.H.values[i,k]
t = UpdVar.T.values[i,k]
rho = rho_c(self.Ref.p0_half[k], t, qt, qv)
UpdVar.B.values[i,k] = buoyancy_c(self.Ref.rho0_half[k], rho)
UpdVar.RH.values[i,k] = relative_humidity_c(self.Ref.p0_half[k], qt, qt-qv, 0.0, t)
elif UpdVar.Area.values[i,k-1] > 0.0 and k>self.Gr.gw:
sa = eos(self.t_to_prog_fp, self.prog_to_t_fp, self.Ref.p0_half[k],
qt, h)
qt -= sa.ql
qv = qt
t = sa.T
rho = rho_c(self.Ref.p0_half[k], t, qt, qv)
UpdVar.B.values[i,k] = buoyancy_c(self.Ref.rho0_half[k], rho)
UpdVar.RH.values[i,k] = relative_humidity_c(self.Ref.p0_half[k], qt, qt-qv, 0.0, t)
else:
UpdVar.B.values[i,k] = EnvVar.B.values[k]
UpdVar.RH.values[i,k] = EnvVar.RH.values[k]
with nogil:
for k in xrange(self.Gr.gw, self.Gr.nzg-self.Gr.gw):
GMV.B.values[k] = (1.0 - UpdVar.Area.bulkvalues[k]) * EnvVar.B.values[k]
for i in xrange(self.n_updraft):
GMV.B.values[k] += UpdVar.Area.values[i,k] * UpdVar.B.values[i,k]
for i in xrange(self.n_updraft):
UpdVar.B.values[i,k] -= GMV.B.values[k]
EnvVar.B.values[k] -= GMV.B.values[k]
return
cpdef microphysics(self, UpdraftVariables UpdVar, RainVariables Rain, double dt):
"""
compute precipitation source terms
"""
cdef:
Py_ssize_t k, i
rain_struct rst
mph_struct mph
eos_struct sa
with nogil:
for i in xrange(self.n_updraft):
for k in xrange(self.Gr.nzg):
# autoconversion and accretion
mph = microphysics_rain_src(
Rain.rain_model,
UpdVar.QT.new[i,k],
UpdVar.QL.new[i,k],
Rain.Upd_QR.values[k],
UpdVar.Area.new[i,k],
UpdVar.T.new[i,k],
self.Ref.p0_half[k],
self.Ref.rho0_half[k],
dt
)
# update Updraft.new
UpdVar.QT.new[i,k] = mph.qt
UpdVar.QL.new[i,k] = mph.ql
UpdVar.H.new[i,k] = mph.thl
# update rain sources of state variables
self.prec_source_qt[i,k] -= mph.qr_src * UpdVar.Area.new[i,k]
self.prec_source_h[i,k] += mph.thl_rain_src * UpdVar.Area.new[i,k]
return