Line data Source code
1 : !---------------------------------------------------------------------------
2 : ! $Id$
3 : !===============================================================================
4 : module stats_zt_module
5 :
6 : implicit none
7 :
8 : private ! Default Scope
9 :
10 : public :: stats_init_zt
11 :
12 : ! Constant parameters
13 : integer, parameter, public :: nvarmax_zt = 800 ! Maximum variables allowed
14 :
15 : contains
16 :
17 : !=============================================================================
18 0 : subroutine stats_init_zt( vars_zt, & ! intent(in)
19 : l_error, & ! intent(inout)
20 : stats_metadata, stats_zt ) ! intent(inout)
21 :
22 : ! Description:
23 : ! Initializes array indices for stats_zt
24 :
25 : ! Note:
26 : ! All code that is within subroutine stats_init_zt, including variable
27 : ! allocation code, is not called if l_stats is false. This subroutine is
28 : ! called only when l_stats is true.
29 :
30 : !-----------------------------------------------------------------------
31 :
32 : use constants_clubb, only: &
33 : fstderr ! Constant(s)
34 :
35 :
36 : use stats_type_utilities, only: &
37 : stat_assign ! Procedure
38 :
39 : use parameters_model, only: &
40 : hydromet_dim, & ! Variable(s)
41 : sclr_dim, &
42 : edsclr_dim
43 :
44 : use array_index, only: &
45 : hydromet_list, & ! Variable(s)
46 : l_mix_rat_hm
47 :
48 : use stats_type, only: &
49 : stats ! Type
50 :
51 : use stats_variables, only: &
52 : stats_metadata_type
53 :
54 : implicit none
55 :
56 : ! External
57 : intrinsic :: trim
58 :
59 : ! Local Constants
60 :
61 : !--------------------- Input Variable ---------------------
62 : character(len= * ), dimension(nvarmax_zt), intent(in) :: &
63 : vars_zt
64 :
65 : !--------------------- InOut Variables ---------------------
66 : type (stats_metadata_type), intent(inout) :: &
67 : stats_metadata
68 :
69 : type (stats), target, intent(inout) :: &
70 : stats_zt
71 :
72 : logical, intent(inout) :: l_error
73 :
74 : !--------------------- Local Varables ---------------------
75 : integer :: tot_zt_loops
76 :
77 : integer :: i, j, k
78 :
79 : integer :: hm_idx, hmx_idx, hmy_idx
80 :
81 : character(len=10) :: hm_type, hmx_type, hmy_type
82 :
83 : character(len=50) :: sclr_idx
84 :
85 : !--------------------- Begin Code ---------------------
86 :
87 : ! The default initialization for array indices for stats_zt is zero (see module
88 : ! stats_variables)
89 :
90 : ! If any of the index arrays are allocated, then we have called this before
91 : ! to set up stats_metadata, so all we want to do is set stats_zt via stats_assign
92 0 : if ( .not. allocated(stats_metadata%ihm_1) ) then
93 :
94 : ! Allocate and initialize hydrometeor statistical variables.
95 0 : allocate( stats_metadata%ihm_1(1:hydromet_dim) )
96 0 : allocate( stats_metadata%ihm_2(1:hydromet_dim) )
97 0 : allocate( stats_metadata%imu_hm_1(1:hydromet_dim) )
98 0 : allocate( stats_metadata%imu_hm_2(1:hydromet_dim) )
99 0 : allocate( stats_metadata%imu_hm_1_n(1:hydromet_dim) )
100 0 : allocate( stats_metadata%imu_hm_2_n(1:hydromet_dim) )
101 0 : allocate( stats_metadata%isigma_hm_1(1:hydromet_dim) )
102 0 : allocate( stats_metadata%isigma_hm_2(1:hydromet_dim) )
103 0 : allocate( stats_metadata%isigma_hm_1_n(1:hydromet_dim) )
104 0 : allocate( stats_metadata%isigma_hm_2_n(1:hydromet_dim) )
105 :
106 0 : allocate( stats_metadata%icorr_w_hm_1(1:hydromet_dim) )
107 0 : allocate( stats_metadata%icorr_w_hm_2(1:hydromet_dim) )
108 0 : allocate( stats_metadata%icorr_chi_hm_1(1:hydromet_dim) )
109 0 : allocate( stats_metadata%icorr_chi_hm_2(1:hydromet_dim) )
110 0 : allocate( stats_metadata%icorr_eta_hm_1(1:hydromet_dim) )
111 0 : allocate( stats_metadata%icorr_eta_hm_2(1:hydromet_dim) )
112 0 : allocate( stats_metadata%icorr_Ncn_hm_1(1:hydromet_dim) )
113 0 : allocate( stats_metadata%icorr_Ncn_hm_2(1:hydromet_dim) )
114 0 : allocate( stats_metadata%icorr_hmx_hmy_1(1:hydromet_dim,1:hydromet_dim) )
115 0 : allocate( stats_metadata%icorr_hmx_hmy_2(1:hydromet_dim,1:hydromet_dim) )
116 :
117 0 : allocate( stats_metadata%icorr_w_hm_1_n(1:hydromet_dim) )
118 0 : allocate( stats_metadata%icorr_w_hm_2_n(1:hydromet_dim) )
119 0 : allocate( stats_metadata%icorr_chi_hm_1_n(1:hydromet_dim) )
120 0 : allocate( stats_metadata%icorr_chi_hm_2_n(1:hydromet_dim) )
121 0 : allocate( stats_metadata%icorr_eta_hm_1_n(1:hydromet_dim) )
122 0 : allocate( stats_metadata%icorr_eta_hm_2_n(1:hydromet_dim) )
123 0 : allocate( stats_metadata%icorr_Ncn_hm_1_n(1:hydromet_dim) )
124 0 : allocate( stats_metadata%icorr_Ncn_hm_2_n(1:hydromet_dim) )
125 0 : allocate( stats_metadata%icorr_hmx_hmy_1_n(1:hydromet_dim,1:hydromet_dim) )
126 0 : allocate( stats_metadata%icorr_hmx_hmy_2_n(1:hydromet_dim,1:hydromet_dim) )
127 :
128 0 : allocate( stats_metadata%ihmp2_zt(1:hydromet_dim) )
129 :
130 0 : allocate( stats_metadata%iwp2hmp(1:hydromet_dim) )
131 :
132 0 : stats_metadata%ihm_1(:) = 0
133 0 : stats_metadata%ihm_2(:) = 0
134 0 : stats_metadata%imu_hm_1(:) = 0
135 0 : stats_metadata%imu_hm_2(:) = 0
136 0 : stats_metadata%imu_hm_1_n(:) = 0
137 0 : stats_metadata%imu_hm_2_n(:) = 0
138 0 : stats_metadata%isigma_hm_1(:) = 0
139 0 : stats_metadata%isigma_hm_2(:) = 0
140 0 : stats_metadata%isigma_hm_1_n(:) = 0
141 0 : stats_metadata%isigma_hm_2_n(:) = 0
142 :
143 0 : stats_metadata%icorr_w_hm_1(:) = 0
144 0 : stats_metadata%icorr_w_hm_2(:) = 0
145 0 : stats_metadata%icorr_chi_hm_1(:) = 0
146 0 : stats_metadata%icorr_chi_hm_2(:) = 0
147 0 : stats_metadata%icorr_eta_hm_1(:) = 0
148 0 : stats_metadata%icorr_eta_hm_2(:) = 0
149 0 : stats_metadata%icorr_Ncn_hm_1(:) = 0
150 0 : stats_metadata%icorr_Ncn_hm_2(:) = 0
151 0 : stats_metadata%icorr_hmx_hmy_1(:,:) = 0
152 0 : stats_metadata%icorr_hmx_hmy_2(:,:) = 0
153 :
154 0 : stats_metadata%icorr_w_hm_1_n(:) = 0
155 0 : stats_metadata%icorr_w_hm_2_n(:) = 0
156 0 : stats_metadata%icorr_chi_hm_1_n(:) = 0
157 0 : stats_metadata%icorr_chi_hm_2_n(:) = 0
158 0 : stats_metadata%icorr_eta_hm_1_n(:) = 0
159 0 : stats_metadata%icorr_eta_hm_2_n(:) = 0
160 0 : stats_metadata%icorr_Ncn_hm_1_n(:) = 0
161 0 : stats_metadata%icorr_Ncn_hm_2_n(:) = 0
162 0 : stats_metadata%icorr_hmx_hmy_1_n(:,:) = 0
163 0 : stats_metadata%icorr_hmx_hmy_2_n(:,:) = 0
164 :
165 0 : stats_metadata%ihmp2_zt(:) = 0
166 :
167 0 : stats_metadata%iwp2hmp(:) = 0
168 :
169 : ! Allocate and then zero out passive scalar arrays
170 0 : allocate( stats_metadata%isclrm(1:sclr_dim) )
171 0 : allocate( stats_metadata%isclrm_f(1:sclr_dim) )
172 :
173 0 : stats_metadata%isclrm(:) = 0
174 0 : stats_metadata%isclrm_f(:) = 0
175 :
176 0 : allocate( stats_metadata%iedsclrm(1:edsclr_dim) )
177 0 : allocate( stats_metadata%iedsclrm_f(1:edsclr_dim) )
178 :
179 0 : stats_metadata%iedsclrm(:) = 0
180 0 : stats_metadata%iedsclrm_f(:) = 0
181 :
182 : end if
183 :
184 : ! Assign pointers for statistics variables stats_zt using stat_assign
185 :
186 0 : tot_zt_loops = stats_zt%num_output_fields
187 :
188 0 : if ( any( vars_zt == "hm_i" ) ) then
189 : ! Correct for number of variables found under "hm_i".
190 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
191 : ! for each hydrometeor.
192 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
193 : ! Add 1 for "hm_i" to the loop size.
194 0 : tot_zt_loops = tot_zt_loops + 1
195 : endif
196 0 : if ( any( vars_zt == "mu_hm_i" ) ) then
197 : ! Correct for number of variables found under "mu_hm_i".
198 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
199 : ! for each hydrometeor.
200 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
201 : ! Add 1 for "mu_hm_i" to the loop size.
202 0 : tot_zt_loops = tot_zt_loops + 1
203 : endif
204 0 : if ( any( vars_zt == "mu_Ncn_i" ) ) then
205 : ! Correct for number of variables found under "mu_Ncn_i".
206 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
207 0 : tot_zt_loops = tot_zt_loops - 2
208 : ! Add 1 for "mu_Ncn_i" to the loop size.
209 0 : tot_zt_loops = tot_zt_loops + 1
210 : endif
211 0 : if ( any( vars_zt == "mu_hm_i_n" ) ) then
212 : ! Correct for number of variables found under "mu_hm_i_n".
213 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
214 : ! for each hydrometeor.
215 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
216 : ! Add 1 for "mu_hm_i_n" to the loop size.
217 0 : tot_zt_loops = tot_zt_loops + 1
218 : endif
219 0 : if ( any( vars_zt == "mu_Ncn_i_n" ) ) then
220 : ! Correct for number of variables found under "mu_Ncn_i_n".
221 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
222 0 : tot_zt_loops = tot_zt_loops - 2
223 : ! Add 1 for "mu_Ncn_i_n" to the loop size.
224 0 : tot_zt_loops = tot_zt_loops + 1
225 : endif
226 0 : if ( any( vars_zt == "sigma_hm_i" ) ) then
227 : ! Correct for number of variables found under "sigma_hm_i".
228 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
229 : ! for each hydrometeor.
230 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
231 : ! Add 1 for "sigma_hm_i" to the loop size.
232 0 : tot_zt_loops = tot_zt_loops + 1
233 : endif
234 0 : if ( any( vars_zt == "sigma_Ncn_i" ) ) then
235 : ! Correct for number of variables found under "sigma_Ncn_i".
236 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
237 0 : tot_zt_loops = tot_zt_loops - 2
238 : ! Add 1 for "sigma_Ncn_i" to the loop size.
239 0 : tot_zt_loops = tot_zt_loops + 1
240 : endif
241 0 : if ( any( vars_zt == "sigma_hm_i_n" ) ) then
242 : ! Correct for number of variables found under "sigma_hm_i_n".
243 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
244 : ! for each hydrometeor.
245 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
246 : ! Add 1 for "sigma_hm_i_n" to the loop size.
247 0 : tot_zt_loops = tot_zt_loops + 1
248 : endif
249 0 : if ( any( vars_zt == "sigma_Ncn_i_n" ) ) then
250 : ! Correct for number of variables found under "sigma_Ncn_i_n".
251 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
252 0 : tot_zt_loops = tot_zt_loops - 2
253 : ! Add 1 for "sigma_Ncn_i_n" to the loop size.
254 0 : tot_zt_loops = tot_zt_loops + 1
255 : endif
256 :
257 0 : if ( any( vars_zt == "corr_w_hm_i" ) ) then
258 : ! Correct for number of variables found under "corr_whm_i".
259 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
260 : ! for each hydrometeor.
261 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
262 : ! Add 1 for "corr_whm_i" to the loop size.
263 0 : tot_zt_loops = tot_zt_loops + 1
264 : endif
265 0 : if ( any( vars_zt == "corr_w_Ncn_i" ) ) then
266 : ! Correct for number of variables found under "corr_wNcn_i".
267 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
268 0 : tot_zt_loops = tot_zt_loops - 2
269 : ! Add 1 for "corr_wNcn_i" to the loop size.
270 0 : tot_zt_loops = tot_zt_loops + 1
271 : endif
272 0 : if ( any( vars_zt == "corr_chi_hm_i" ) ) then
273 : ! Correct for number of variables found under "corr_chi_hm_i".
274 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
275 : ! for each hydrometeor.
276 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
277 : ! Add 1 for "corr_chi_hm_i" to the loop size.
278 0 : tot_zt_loops = tot_zt_loops + 1
279 : endif
280 0 : if ( any( vars_zt == "corr_chi_Ncn_i" ) ) then
281 : ! Correct for number of variables found under "corr_chi_Ncn_i".
282 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
283 0 : tot_zt_loops = tot_zt_loops - 2
284 : ! Add 1 for "corr_chi_Ncn_i" to the loop size.
285 0 : tot_zt_loops = tot_zt_loops + 1
286 : endif
287 0 : if ( any( vars_zt == "corr_eta_hm_i" ) ) then
288 : ! Correct for number of variables found under "corr_eta_hm_i".
289 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
290 : ! for each hydrometeor.
291 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
292 : ! Add 1 for "corr_eta_hm_i" to the loop size.
293 0 : tot_zt_loops = tot_zt_loops + 1
294 : endif
295 0 : if ( any( vars_zt == "corr_eta_Ncn_i" ) ) then
296 : ! Correct for number of variables found under "corr_eta_Ncn_i".
297 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
298 0 : tot_zt_loops = tot_zt_loops - 2
299 : ! Add 1 for "corr_eta_Ncn_i" to the loop size.
300 0 : tot_zt_loops = tot_zt_loops + 1
301 : endif
302 0 : if ( any( vars_zt == "corr_Ncn_hm_i" ) ) then
303 : ! Correct for number of variables found under "corr_Ncnhm_i".
304 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
305 : ! for each hydrometeor.
306 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
307 : ! Add 1 for "corr_Ncnhm_i" to the loop size.
308 0 : tot_zt_loops = tot_zt_loops + 1
309 : endif
310 0 : if ( any( vars_zt == "corr_hmx_hmy_i" ) ) then
311 : ! Correct for number of variables found under "corr_hmxhmy_i".
312 : ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
313 : ! number of correlations of two hydrometeors, which is found by:
314 : ! (1/2) * hydromet_dim * ( hydromet_dim - 1 ); from the loop size.
315 0 : tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
316 : ! Add 1 for "corr_hmxhmy_i" to the loop size.
317 0 : tot_zt_loops = tot_zt_loops + 1
318 : endif
319 :
320 0 : if ( any( vars_zt == "corr_w_hm_i_n" ) ) then
321 : ! Correct for number of variables found under "corr_whm_i_n".
322 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
323 : ! for each hydrometeor.
324 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
325 : ! Add 1 for "corr_whm_i_n" to the loop size.
326 0 : tot_zt_loops = tot_zt_loops + 1
327 : endif
328 0 : if ( any( vars_zt == "corr_w_Ncn_i_n" ) ) then
329 : ! Correct for number of variables found under "corr_wNcn_i_n".
330 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
331 0 : tot_zt_loops = tot_zt_loops - 2
332 : ! Add 1 for "corr_wNcn_i_n" to the loop size.
333 0 : tot_zt_loops = tot_zt_loops + 1
334 : endif
335 0 : if ( any( vars_zt == "corr_chi_hm_i_n" ) ) then
336 : ! Correct for number of variables found under "corr_chi_hm_i_n".
337 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
338 : ! for each hydrometeor.
339 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
340 : ! Add 1 for "corr_chi_hm_i_n" to the loop size.
341 0 : tot_zt_loops = tot_zt_loops + 1
342 : endif
343 0 : if ( any( vars_zt == "corr_chi_Ncn_i_n" ) ) then
344 : ! Correct for number of variables found under "corr_chi_Ncn_i_n".
345 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
346 0 : tot_zt_loops = tot_zt_loops - 2
347 : ! Add 1 for "corr_chi_Ncn_i_n" to the loop size.
348 0 : tot_zt_loops = tot_zt_loops + 1
349 : endif
350 0 : if ( any( vars_zt == "corr_eta_hm_i_n" ) ) then
351 : ! Correct for number of variables found under "corr_eta_hm_i_n".
352 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
353 : ! for each hydrometeor.
354 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
355 : ! Add 1 for "corr_eta_hm_i_n" to the loop size.
356 0 : tot_zt_loops = tot_zt_loops + 1
357 : endif
358 0 : if ( any( vars_zt == "corr_eta_Ncn_i_n" ) ) then
359 : ! Correct for number of variables found under "corr_eta_Ncn_i_n".
360 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
361 0 : tot_zt_loops = tot_zt_loops - 2
362 : ! Add 1 for "corr_eta_Ncn_i_n" to the loop size.
363 0 : tot_zt_loops = tot_zt_loops + 1
364 : endif
365 0 : if ( any( vars_zt == "corr_Ncn_hm_i_n" ) ) then
366 : ! Correct for number of variables found under "corr_Ncnhm_i_n".
367 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
368 : ! for each hydrometeor.
369 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
370 : ! Add 1 for "corr_Ncnhm_i_n" to the loop size.
371 0 : tot_zt_loops = tot_zt_loops + 1
372 : endif
373 0 : if ( any( vars_zt == "corr_hmx_hmy_i_n" ) ) then
374 : ! Correct for number of variables found under "corr_hmxhmy_i_n".
375 : ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
376 : ! number of normal space correlations of two hydrometeors, which is found
377 : ! by: (1/2) * hydromet_dim * ( hydromet_dim - 1 );
378 : ! from the loop size.
379 0 : tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
380 : ! Add 1 for "corr_hmxhmy_i_n" to the loop size.
381 0 : tot_zt_loops = tot_zt_loops + 1
382 : endif
383 :
384 0 : if ( any( vars_zt == "hmp2_zt" ) ) then
385 : ! Correct for number of variables found under "hmp2_zt".
386 : ! Subtract 1 from the loop size for each hydrometeor.
387 0 : tot_zt_loops = tot_zt_loops - hydromet_dim
388 : ! Add 1 for "hmp2_zt" to the loop size.
389 0 : tot_zt_loops = tot_zt_loops + 1
390 : endif
391 :
392 0 : if ( any( vars_zt == "wp2hmp" ) ) then
393 : ! Correct for number of variables found under "wp2hmp".
394 : ! Subtract 1 from the loop size for each hydrometeor.
395 0 : tot_zt_loops = tot_zt_loops - hydromet_dim
396 : ! Add 1 for "wp2hmp" to the loop size.
397 0 : tot_zt_loops = tot_zt_loops + 1
398 : endif
399 :
400 0 : if ( any( vars_zt == "sclrm" ) ) then
401 : ! Correct for number of variables found under "sclrm".
402 : ! Subtract 1 from the loop size for each scalar.
403 0 : tot_zt_loops = tot_zt_loops - sclr_dim
404 :
405 : ! Add 1 for "sclrm" to the loop size.
406 0 : tot_zt_loops = tot_zt_loops + 1
407 : endif
408 :
409 0 : if ( any( vars_zt == "sclrm_f" ) ) then
410 : ! Correct for number of variables found under "sclrm_f".
411 : ! Subtract 1 from the loop size for each scalar.
412 0 : tot_zt_loops = tot_zt_loops - sclr_dim
413 : ! Add 1 for "sclrm_f" to the loop size.
414 0 : tot_zt_loops = tot_zt_loops + 1
415 : endif
416 :
417 0 : if ( any( vars_zt == "edsclrm" ) ) then
418 : ! Correct for number of variables found under "edsclrm".
419 : ! Subtract 1 from the loop size for each scalar.
420 0 : tot_zt_loops = tot_zt_loops - edsclr_dim
421 : ! Add 1 for "edsclrm" to the loop size.
422 0 : tot_zt_loops = tot_zt_loops + 1
423 : endif
424 :
425 0 : if ( any( vars_zt == "edsclrm_f" ) ) then
426 : ! Correct for number of variables found under "edsclrm_f".
427 : ! Subtract 1 from the loop size for each scalar.
428 0 : tot_zt_loops = tot_zt_loops - edsclr_dim
429 : ! Add 1 for "edsclrm_f" to the loop size.
430 0 : tot_zt_loops = tot_zt_loops + 1
431 : endif
432 :
433 0 : k = 1
434 :
435 0 : do i = 1, tot_zt_loops
436 :
437 0 : select case ( trim( vars_zt(i) ) )
438 : case ('thlm')
439 0 : stats_metadata%ithlm = k
440 : call stat_assign( var_index=stats_metadata%ithlm, var_name="thlm", &
441 : var_description="thlm, Liquid water potential temperature (theta_l)", var_units="K", &
442 0 : l_silhs=.false., grid_kind=stats_zt )
443 0 : k = k + 1
444 :
445 : case ('T_in_K')
446 0 : stats_metadata%iT_in_K = k
447 : call stat_assign( var_index=stats_metadata%iT_in_K, var_name="T_in_K", &
448 : var_description="T_in_K, Absolute temperature", var_units="K", l_silhs=.false., &
449 0 : grid_kind=stats_zt )
450 0 : k = k + 1
451 :
452 : case ('thvm')
453 0 : stats_metadata%ithvm = k
454 : call stat_assign( var_index=stats_metadata%ithvm, var_name="thvm", &
455 : var_description="thvm, Virtual potential temperature", &
456 : var_units="K", l_silhs=.false., &
457 0 : grid_kind=stats_zt )
458 0 : k = k + 1
459 :
460 : case ('rtm')
461 0 : stats_metadata%irtm = k
462 :
463 : call stat_assign( var_index=stats_metadata%irtm, var_name="rtm", &
464 : var_description="rtm, Total (vapor+liquid) water mixing ratio", &
465 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
466 :
467 0 : k = k + 1
468 :
469 : case ('rcm')
470 0 : stats_metadata%ircm = k
471 : call stat_assign( var_index=stats_metadata%ircm, var_name="rcm", &
472 : var_description="rcm, Cloud water mixing ratio", var_units="kg/kg", &
473 0 : l_silhs=.false., grid_kind=stats_zt )
474 0 : k = k + 1
475 :
476 : case ('rfrzm')
477 0 : stats_metadata%irfrzm = k
478 : call stat_assign( var_index=stats_metadata%irfrzm, var_name="rfrzm", &
479 : var_description="rfrzm, Total ice phase water mixing ratio", var_units="kg/kg", &
480 0 : l_silhs=.false., grid_kind=stats_zt )
481 0 : k = k + 1
482 :
483 : case ('rvm')
484 0 : stats_metadata%irvm = k
485 : call stat_assign( var_index=stats_metadata%irvm, var_name="rvm", &
486 : var_description="rvm, Vapor water mixing ratio", var_units="kg/kg", &
487 0 : l_silhs=.false., grid_kind=stats_zt )
488 0 : k = k + 1
489 : case ('rel_humidity')
490 0 : stats_metadata%irel_humidity = k
491 : call stat_assign( var_index=stats_metadata%irel_humidity, var_name="rel_humidity", &
492 : var_description="rel_humidity, Relative humidity w.r.t. liquid (between 0 and 1)", &
493 0 : var_units="[-]", l_silhs=.false., grid_kind=stats_zt )
494 0 : k = k + 1
495 : case ('um')
496 0 : stats_metadata%ium = k
497 : call stat_assign( var_index=stats_metadata%ium, var_name="um", &
498 : var_description="u_bar, Grid-mean eastward (u) wind", &
499 : var_units="m/s", l_silhs=.false., &
500 0 : grid_kind=stats_zt )
501 0 : k = k + 1
502 : case ('vm')
503 0 : stats_metadata%ivm = k
504 : call stat_assign( var_index=stats_metadata%ivm, var_name="vm", &
505 : var_description="v_bar, Grid-mean northward (v) wind", &
506 : var_units="m/s", l_silhs=.false., &
507 0 : grid_kind=stats_zt )
508 0 : k = k + 1
509 : case ('wm_zt')
510 0 : stats_metadata%iwm_zt = k
511 : call stat_assign( var_index=stats_metadata%iwm_zt, var_name="wm", &
512 : var_description="w_bar, Grid-mean upward (w) wind", &
513 : var_units="m/s", l_silhs=.false., &
514 0 : grid_kind=stats_zt )
515 0 : k = k + 1
516 : case ('um_ref')
517 0 : stats_metadata%ium_ref = k
518 : call stat_assign( var_index=stats_metadata%ium_ref, var_name="um_ref", &
519 : var_description="um_ref, Reference u wind", var_units="m/s", l_silhs=.false., &
520 0 : grid_kind=stats_zt )
521 0 : k = k + 1
522 : case ('vm_ref')
523 0 : stats_metadata%ivm_ref = k
524 : call stat_assign( var_index=stats_metadata%ivm_ref, var_name="vm_ref", &
525 : var_description="vm_ref, Reference v wind", var_units="m/s", l_silhs=.false., &
526 0 : grid_kind=stats_zt )
527 0 : k = k + 1
528 : case ('ug')
529 0 : stats_metadata%iug = k
530 : call stat_assign( var_index=stats_metadata%iug, var_name="ug", &
531 : var_description="ug, u geostrophic wind", var_units="m/s", l_silhs=.false., &
532 0 : grid_kind=stats_zt )
533 0 : k = k + 1
534 : case ('vg')
535 0 : stats_metadata%ivg = k
536 : call stat_assign( var_index=stats_metadata%ivg, var_name="vg", &
537 : var_description="vg, v geostrophic wind", var_units="m/s", l_silhs=.false., &
538 0 : grid_kind=stats_zt )
539 0 : k = k + 1
540 : case ('cloud_frac')
541 0 : stats_metadata%icloud_frac = k
542 : call stat_assign( var_index=stats_metadata%icloud_frac, var_name="cloud_frac", &
543 : var_description="cloud_frac, Cloud fraction (between 0 and 1)", var_units="-", &
544 0 : l_silhs=.false., grid_kind=stats_zt )
545 0 : k = k + 1
546 :
547 : case ('ice_supersat_frac')
548 0 : stats_metadata%iice_supersat_frac = k
549 : call stat_assign( var_index=stats_metadata%iice_supersat_frac, var_name="ice_supersat_frac", &
550 : var_description="ice_supersat_frac, Ice cloud fraction (between 0 and 1)", &
551 : var_units="count", &
552 0 : l_silhs=.false., grid_kind=stats_zt )
553 0 : k = k + 1
554 :
555 : case ('rcm_in_layer')
556 0 : stats_metadata%ircm_in_layer = k
557 : call stat_assign( var_index=stats_metadata%ircm_in_layer, var_name="rcm_in_layer", &
558 : var_description="rcm_in_layer, rcm in cloud layer", &
559 : var_units="kg/kg", l_silhs=.false., &
560 0 : grid_kind=stats_zt )
561 0 : k = k + 1
562 :
563 : case ('rcm_in_cloud')
564 0 : stats_metadata%ircm_in_cloud = k
565 : call stat_assign( var_index=stats_metadata%ircm_in_cloud, var_name="rcm_in_cloud", &
566 : var_description="rcm_in_cloud, In-cloud value of rcm (for microphysics)", &
567 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
568 0 : k = k + 1
569 :
570 : case ('cloud_cover')
571 0 : stats_metadata%icloud_cover = k
572 : call stat_assign( var_index=stats_metadata%icloud_cover, var_name="cloud_cover", &
573 : var_description="cloud_cover, Cloud cover (between 0 and 1)", var_units="count", &
574 0 : l_silhs=.false., grid_kind=stats_zt )
575 0 : k = k + 1
576 : case ('p_in_Pa')
577 0 : stats_metadata%ip_in_Pa = k
578 : call stat_assign( var_index=stats_metadata%ip_in_Pa, var_name="p_in_Pa", &
579 : var_description="p_in_Pa, Pressure", &
580 0 : var_units="Pa", l_silhs=.false., grid_kind=stats_zt )
581 0 : k = k + 1
582 : case ('exner')
583 0 : stats_metadata%iexner = k
584 : call stat_assign( var_index=stats_metadata%iexner, var_name="exner", &
585 : var_description="exner, Exner function = (p/p0)**(rd/cp)", var_units="count", &
586 0 : l_silhs=.false., grid_kind=stats_zt )
587 0 : k = k + 1
588 : case ('rho_ds_zt')
589 0 : stats_metadata%irho_ds_zt = k
590 : call stat_assign( var_index=stats_metadata%irho_ds_zt, var_name="rho_ds_zt", &
591 : var_description="rho_ds_zt, Dry static base-state density", var_units="kg m^{-3}", &
592 0 : l_silhs=.false., grid_kind=stats_zt )
593 0 : k = k + 1
594 : case ('thv_ds_zt')
595 0 : stats_metadata%ithv_ds_zt = k
596 : call stat_assign( var_index=stats_metadata%ithv_ds_zt, var_name="thv_ds_zt", &
597 : var_description="thv_ds_zt, Dry base-state theta_v", &
598 : var_units="K", l_silhs=.false., &
599 0 : grid_kind=stats_zt )
600 0 : k = k + 1
601 : case ('Lscale')
602 0 : stats_metadata%iLscale = k
603 : call stat_assign( var_index=stats_metadata%iLscale, var_name="Lscale", &
604 : var_description="L, Turbulent mixing length", &
605 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
606 0 : k = k + 1
607 : case ('thlm_forcing')
608 0 : stats_metadata%ithlm_forcing = k
609 : call stat_assign( var_index=stats_metadata%ithlm_forcing, var_name="thlm_forcing", &
610 : var_description="thlm_forcing, thlm budget: thetal forcing " &
611 : // "(includes thlm_mc and radht)",&
612 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
613 0 : k = k + 1
614 : case ('thlm_mc')
615 0 : stats_metadata%ithlm_mc = k
616 : call stat_assign( var_index=stats_metadata%ithlm_mc, var_name="thlm_mc", &
617 : var_description="thlm_mc, Change in thlm due to microphysics (not in budget)", &
618 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
619 0 : k = k + 1
620 : case ('rtm_forcing')
621 0 : stats_metadata%irtm_forcing = k
622 : call stat_assign( var_index=stats_metadata%irtm_forcing, var_name="rtm_forcing", &
623 : var_description="rtm_forcing, rtm budget: rt forcing (includes rtm_mc)", &
624 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
625 0 : k = k + 1
626 :
627 : case ('rtm_mc')
628 0 : stats_metadata%irtm_mc = k
629 : call stat_assign( var_index=stats_metadata%irtm_mc, var_name="rtm_mc", &
630 : var_description="rtm_mc, Change in rt due to microphysics (not in budget)", &
631 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
632 0 : k = k + 1
633 :
634 : case ('rvm_mc')
635 0 : stats_metadata%irvm_mc = k
636 : call stat_assign( var_index=stats_metadata%irvm_mc, var_name="rvm_mc", &
637 : var_description="rvm_mc, Time tendency of vapor mixing ratio due to microphysics", &
638 0 : var_units="kg/(kg s)", l_silhs=.false., grid_kind=stats_zt )
639 0 : k = k + 1
640 :
641 : case ('rcm_mc')
642 0 : stats_metadata%ircm_mc = k
643 : call stat_assign( var_index=stats_metadata%ircm_mc, var_name="rcm_mc", &
644 : var_description="rcm_mc, Time tendency of liquid water mixing ratio " &
645 : // "due microphysics",&
646 0 : var_units="kg/kg/s", l_silhs=.false., grid_kind=stats_zt )
647 0 : k = k + 1
648 :
649 : case ('rcm_sd_mg_morr')
650 0 : stats_metadata%ircm_sd_mg_morr = k
651 : call stat_assign( var_index=stats_metadata%ircm_sd_mg_morr, var_name="rcm_sd_mg_morr", &
652 : var_description="rcm_sd_mg_morr, rcm sedimentation when using morrision or MG " &
653 : // "microphysics (not in budget, included in rcm_mc)", &
654 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
655 0 : k = k + 1
656 :
657 : case ('thlm_mfl_min')
658 0 : stats_metadata%ithlm_mfl_min = k
659 : call stat_assign( var_index=stats_metadata%ithlm_mfl_min, var_name="thlm_mfl_min", &
660 : var_description="thlm_mfl_min, Minimum allowable thlm", var_units="K", &
661 : l_silhs=.false., &
662 0 : grid_kind=stats_zt )
663 0 : k = k + 1
664 :
665 : case ('thlm_mfl_max')
666 0 : stats_metadata%ithlm_mfl_max = k
667 : call stat_assign( var_index=stats_metadata%ithlm_mfl_max, var_name="thlm_mfl_max", &
668 : var_description="thlm_mfl_max, Maximum allowable thlm", var_units="K", &
669 : l_silhs=.false., &
670 0 : grid_kind=stats_zt )
671 0 : k = k + 1
672 :
673 : case ('thlm_enter_mfl')
674 0 : stats_metadata%ithlm_enter_mfl = k
675 : call stat_assign( var_index=stats_metadata%ithlm_enter_mfl, var_name="thlm_enter_mfl", &
676 : var_description="thlm_enter_mfl, Thlm before flux-limiter", var_units="K", &
677 : l_silhs=.false., &
678 0 : grid_kind=stats_zt )
679 0 : k = k + 1
680 :
681 : case ('thlm_exit_mfl')
682 0 : stats_metadata%ithlm_exit_mfl = k
683 : call stat_assign( var_index=stats_metadata%ithlm_exit_mfl, var_name="thlm_exit_mfl", &
684 : var_description="thlm_exit_mfl, Thlm exiting flux-limiter", var_units="K", &
685 : l_silhs=.false., &
686 0 : grid_kind=stats_zt )
687 0 : k = k + 1
688 :
689 : case ('thlm_old')
690 0 : stats_metadata%ithlm_old = k
691 : call stat_assign( var_index=stats_metadata%ithlm_old, var_name="thlm_old", &
692 : var_description="thlm_old, Thlm at previous timestep", var_units="K", &
693 : l_silhs=.false., &
694 0 : grid_kind=stats_zt )
695 0 : k = k + 1
696 :
697 : case ('thlm_without_ta')
698 0 : stats_metadata%ithlm_without_ta = k
699 : call stat_assign( var_index=stats_metadata%ithlm_without_ta, var_name="thlm_without_ta", &
700 : var_description="thlm_without_ta, Thlm without turbulent advection contribution", &
701 : var_units="K", &
702 0 : l_silhs=.false., grid_kind=stats_zt )
703 0 : k = k + 1
704 :
705 : case ('rtm_mfl_min')
706 0 : stats_metadata%irtm_mfl_min = k
707 : call stat_assign( var_index=stats_metadata%irtm_mfl_min, var_name="rtm_mfl_min", &
708 : var_description="rtm_mfl_min, Minimum allowable rtm", var_units="kg/kg", &
709 0 : l_silhs=.false., grid_kind=stats_zt )
710 0 : k = k + 1
711 :
712 : case ('rtm_mfl_max')
713 0 : stats_metadata%irtm_mfl_max = k
714 : call stat_assign( var_index=stats_metadata%irtm_mfl_max, var_name="rtm_mfl_max", &
715 : var_description="rtm_mfl_max, Maximum allowable rtm", var_units="kg/kg", &
716 0 : l_silhs=.false., grid_kind=stats_zt )
717 0 : k = k + 1
718 :
719 : case ('rtm_enter_mfl')
720 0 : stats_metadata%irtm_enter_mfl = k
721 : call stat_assign( var_index=stats_metadata%irtm_enter_mfl, var_name="rtm_enter_mfl", &
722 : var_description="rtm_enter_mfl, Rtm before flux-limiter", var_units="kg/kg", &
723 0 : l_silhs=.false., grid_kind=stats_zt )
724 0 : k = k + 1
725 :
726 : case ('rtm_exit_mfl')
727 0 : stats_metadata%irtm_exit_mfl = k
728 : call stat_assign( var_index=stats_metadata%irtm_exit_mfl, var_name="rtm_exit_mfl", &
729 : var_description="rtm_exit_mfl, Rtm exiting flux-limiter", var_units="kg/kg", &
730 0 : l_silhs=.false., grid_kind=stats_zt )
731 0 : k = k + 1
732 :
733 : case ('rtm_old')
734 0 : stats_metadata%irtm_old = k
735 : call stat_assign( var_index=stats_metadata%irtm_old, var_name="rtm_old", &
736 : var_description="rtm_old, Rtm at previous timestep", var_units="kg/kg", &
737 0 : l_silhs=.false., grid_kind=stats_zt )
738 0 : k = k + 1
739 :
740 : case ('rtm_without_ta')
741 0 : stats_metadata%irtm_without_ta = k
742 : call stat_assign( var_index=stats_metadata%irtm_without_ta, var_name="rtm_without_ta", &
743 : var_description="rtm_without_ta, Rtm without turbulent advection contribution", &
744 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
745 0 : k = k + 1
746 :
747 : case ('wp3')
748 0 : stats_metadata%iwp3 = k
749 : call stat_assign( var_index=stats_metadata%iwp3, var_name="wp3", &
750 : var_description="w'^3, Third-order moment of vertical air velocity", &
751 : var_units="m^3/s^3", &
752 0 : l_silhs=.false., grid_kind=stats_zt )
753 0 : k = k + 1
754 :
755 : case ('wpup2')
756 0 : stats_metadata%iwpup2 = k
757 : call stat_assign( var_index=stats_metadata%iwpup2, var_name="wpup2", &
758 : var_description="w'u'^2, Third-order moment from PDF", &
759 : var_units="m^3/s^3", &
760 0 : l_silhs=.false., grid_kind=stats_zt )
761 0 : k = k + 1
762 :
763 : case ('wpvp2')
764 0 : stats_metadata%iwpvp2 = k
765 : call stat_assign( var_index=stats_metadata%iwpvp2, var_name="wpvp2", &
766 : var_description="w'v'^2, Third-order moment from PDF", &
767 : var_units="m^3/s^3", &
768 0 : l_silhs=.false., grid_kind=stats_zt )
769 0 : k = k + 1
770 :
771 : case ('thlp3')
772 0 : stats_metadata%ithlp3 = k
773 : call stat_assign( var_index=stats_metadata%ithlp3, var_name="thlp3", &
774 : var_description="thl'^3, Third-order moment of theta_l", var_units="K^3", &
775 0 : l_silhs=.false., grid_kind=stats_zt )
776 0 : k = k + 1
777 :
778 : case ('rtp3')
779 0 : stats_metadata%irtp3 = k
780 : call stat_assign( var_index=stats_metadata%irtp3, var_name="rtp3", &
781 : var_description="rt'^3, Third-order moment of total water, rt", var_units="(kg/kg)^3", &
782 0 : l_silhs=.false., grid_kind=stats_zt )
783 0 : k = k + 1
784 :
785 : case ('wpthlp2')
786 0 : stats_metadata%iwpthlp2 = k
787 : call stat_assign( var_index=stats_metadata%iwpthlp2, var_name="wpthlp2", &
788 : var_description="w'theta_l'^2, Vertical turbulent flux of theta_l'^2", &
789 : var_units="(m K^2)/s", l_silhs=.false., &
790 0 : grid_kind=stats_zt )
791 0 : k = k + 1
792 :
793 : case ('wp2thlp')
794 0 : stats_metadata%iwp2thlp = k
795 : call stat_assign( var_index=stats_metadata%iwp2thlp, var_name="wp2thlp", &
796 : var_description="w'^2theta_l', Vertical turbulent flux of w'theta_l'", &
797 : var_units="(m^2 K)/s^2", l_silhs=.false., &
798 0 : grid_kind=stats_zt )
799 0 : k = k + 1
800 :
801 : case ('wprtp2')
802 0 : stats_metadata%iwprtp2 = k
803 : call stat_assign( var_index=stats_metadata%iwprtp2, var_name="wprtp2", &
804 : var_description="w'rt'^2, Vertical turbulent flux of rt'^2", &
805 : var_units="(m kg)/(s kg)", &
806 0 : l_silhs=.false., grid_kind=stats_zt )
807 0 : k = k + 1
808 :
809 : case ('wp2rtp')
810 0 : stats_metadata%iwp2rtp = k
811 : call stat_assign( var_index=stats_metadata%iwp2rtp, var_name="wp2rtp", &
812 : var_description="w'^2rt', Vertical turbulent flux of w'rt'", &
813 : var_units="(m^2 kg)/(s^2 kg)", &
814 0 : l_silhs=.false., grid_kind=stats_zt )
815 0 : k = k + 1
816 :
817 : case ('Lscale_up')
818 0 : stats_metadata%iLscale_up = k
819 : call stat_assign( var_index=stats_metadata%iLscale_up, var_name="Lscale_up", &
820 : var_description="Lscale_up, Upward mixing length", var_units="m", l_silhs=.false., &
821 0 : grid_kind=stats_zt )
822 0 : k = k + 1
823 :
824 : case ('Lscale_down')
825 0 : stats_metadata%iLscale_down = k
826 : call stat_assign( var_index=stats_metadata%iLscale_down, var_name="Lscale_down", &
827 : var_description="Lscale_down, Downward mixing length", var_units="m", &
828 : l_silhs=.false.,&
829 0 : grid_kind=stats_zt )
830 0 : k = k + 1
831 :
832 : case ('Lscale_pert_1')
833 0 : stats_metadata%iLscale_pert_1 = k
834 : call stat_assign( var_index=stats_metadata%iLscale_pert_1, var_name="Lscale_pert_1", &
835 : var_description="Lscale_pert_1, Mixing length using a perturbed value of rtm/thlm", &
836 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
837 0 : k = k + 1
838 :
839 : case ('Lscale_pert_2')
840 0 : stats_metadata%iLscale_pert_2 = k
841 : call stat_assign( var_index=stats_metadata%iLscale_pert_2, var_name="Lscale_pert_2", &
842 : var_description="Lscale_pert_2, Mixing length using a perturbed value of rtm/thlm", &
843 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
844 0 : k = k + 1
845 :
846 : case ('tau_zt')
847 0 : stats_metadata%itau_zt = k
848 : call stat_assign( var_index=stats_metadata%itau_zt, var_name="tau_zt", &
849 : var_description="tau_zt, Dissipation time", var_units="s", l_silhs=.false., &
850 0 : grid_kind=stats_zt )
851 0 : k = k + 1
852 :
853 : case ('invrs_tau_zt')
854 0 : stats_metadata%iinvrs_tau_zt = k
855 : call stat_assign( var_index=stats_metadata%iinvrs_tau_zt, var_name="invrs_tau_zt", &
856 : var_description="invrs_tau_zt, Inverse of dissipation time", var_units="s^-1", &
857 0 : l_silhs=.false., grid_kind=stats_zt )
858 0 : k = k + 1
859 :
860 : case ('Kh_zt')
861 0 : stats_metadata%iKh_zt = k
862 : call stat_assign( var_index=stats_metadata%iKh_zt, var_name="Kh_zt", &
863 : var_description="Kh_zt, Eddy diffusivity", var_units="m^2/s", l_silhs=.false., &
864 0 : grid_kind=stats_zt )
865 0 : k = k + 1
866 :
867 : case ('wp2thvp')
868 0 : stats_metadata%iwp2thvp = k
869 : call stat_assign( var_index=stats_metadata%iwp2thvp, var_name="wp2thvp", &
870 : var_description="w'^2theta_v', Vertical turbulent flux of w'theta_v'", &
871 : var_units="K m^2/s^2", l_silhs=.false., &
872 0 : grid_kind=stats_zt )
873 0 : k = k + 1
874 :
875 : case ('wp2rcp')
876 0 : stats_metadata%iwp2rcp = k
877 : call stat_assign( var_index=stats_metadata%iwp2rcp, var_name="wp2rcp", &
878 : var_description="w'^2rc'", var_units="(m^2 kg)/(s^2 kg)", &
879 0 : l_silhs=.false., grid_kind=stats_zt )
880 0 : k = k + 1
881 :
882 : case ('w_up_in_cloud')
883 0 : stats_metadata%iw_up_in_cloud = k
884 : call stat_assign( var_index=stats_metadata%iw_up_in_cloud, var_name="w_up_in_cloud", &
885 : var_description="Mean W in saturated updrafts", &
886 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
887 0 : k = k + 1
888 :
889 : case ('w_down_in_cloud')
890 0 : stats_metadata%iw_down_in_cloud = k
891 : call stat_assign( var_index=stats_metadata%iw_down_in_cloud, var_name="w_down_in_cloud", &
892 : var_description="Mean W in saturated downdrafts", &
893 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
894 0 : k = k + 1
895 :
896 : case ('cld_updr_frac')
897 0 : stats_metadata%icld_updr_frac = k
898 : call stat_assign( var_index=stats_metadata%icld_updr_frac, var_name="cld_updr_frac", &
899 : var_description="Cloudy Updraft Fraction", &
900 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
901 0 : k = k + 1
902 :
903 : case ('cld_downdr_frac')
904 0 : stats_metadata%icld_downdr_frac = k
905 : call stat_assign( var_index=stats_metadata%icld_downdr_frac, var_name="cld_downdr_frac", &
906 : var_description="Cloudy Downdraft Fraction", &
907 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
908 0 : k = k + 1
909 :
910 : case ('wprtpthlp')
911 0 : stats_metadata%iwprtpthlp = k
912 : call stat_assign( var_index=stats_metadata%iwprtpthlp, var_name="wprtpthlp", &
913 : var_description="w'rt'theta_l', Vertical turbulent flux of rt'theta_l'", &
914 : var_units="(m kg K)/(s kg)", &
915 0 : l_silhs=.false., grid_kind=stats_zt )
916 0 : k = k + 1
917 :
918 : case ('rc_coef')
919 0 : stats_metadata%irc_coef = k
920 : call stat_assign( var_index=stats_metadata%irc_coef, var_name="rc_coef", &
921 : var_description="rc_coef, Coefficient of X'r_c'", &
922 0 : var_units="K/(kg/kg)", l_silhs=.false., grid_kind=stats_zt )
923 0 : k = k + 1
924 :
925 : case ('sigma_sqd_w_zt')
926 0 : stats_metadata%isigma_sqd_w_zt = k
927 : call stat_assign( var_index=stats_metadata%isigma_sqd_w_zt, var_name="sigma_sqd_w_zt", &
928 : var_description="sigma_sqd_w_zt, Nondimensionalized w variance of " &
929 : // "Gaussian component",&
930 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
931 0 : k = k + 1
932 :
933 : case ('rho')
934 0 : stats_metadata%irho = k
935 : call stat_assign( var_index=stats_metadata%irho, var_name="rho", var_description="rho, Air density", &
936 0 : var_units="kg m^{-3}", l_silhs=.false., grid_kind=stats_zt )
937 0 : k = k + 1
938 :
939 : case ('Ncm') ! Brian
940 0 : stats_metadata%iNcm = k
941 : call stat_assign( var_index=stats_metadata%iNcm, var_name="Ncm", &
942 : var_description="Ncm, Cloud droplet number concentration", var_units="num/kg", &
943 0 : l_silhs=.false., grid_kind=stats_zt )
944 0 : k = k + 1
945 :
946 : case ('Nc_in_cloud')
947 0 : stats_metadata%iNc_in_cloud = k
948 :
949 : call stat_assign( var_index=stats_metadata%iNc_in_cloud, var_name="Nc_in_cloud", &
950 : var_description="Nc_in_cloud, In cloud droplet concentration", var_units="num/kg", &
951 0 : l_silhs=.false., grid_kind=stats_zt )
952 :
953 0 : k = k + 1
954 :
955 : case ('Nc_activated')
956 0 : stats_metadata%iNc_activated = k
957 :
958 : call stat_assign( var_index=stats_metadata%iNc_activated, var_name="Nc_activated", &
959 : var_description="Nc_activated, Droplets activated by GFDL activation", &
960 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
961 :
962 0 : k = k + 1
963 :
964 : case ('Nccnm')
965 0 : stats_metadata%iNccnm = k
966 : call stat_assign( var_index=stats_metadata%iNccnm, var_name="Nccnm", &
967 : var_description="Nccnm, Cloud condensation nuclei concentration", &
968 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
969 0 : k = k + 1
970 :
971 : case ('Nim') ! Brian
972 0 : stats_metadata%iNim = k
973 : call stat_assign( var_index=stats_metadata%iNim, var_name="Nim", &
974 : var_description="Nim, Ice crystal number concentration", var_units="num/kg", &
975 0 : l_silhs=.false., grid_kind=stats_zt )
976 0 : k = k + 1
977 :
978 : case ('snowslope') ! Adam Smith, 22 April 2008
979 0 : stats_metadata%isnowslope = k
980 : call stat_assign( var_index=stats_metadata%isnowslope, var_name="snowslope", &
981 : var_description="snowslope, COAMPS microphysics snow slope parameter", &
982 : var_units="1/m", &
983 0 : l_silhs=.false., grid_kind=stats_zt )
984 0 : k = k + 1
985 :
986 : case ('Nsm') ! Adam Smith, 22 April 2008
987 0 : stats_metadata%iNsm = k
988 : call stat_assign( var_index=stats_metadata%iNsm, var_name="Nsm", &
989 : var_description="Nsm, Snow particle number concentration", var_units="num/kg", &
990 0 : l_silhs=.false., grid_kind=stats_zt )
991 0 : k = k + 1
992 :
993 : case ('Ngm')
994 0 : stats_metadata%iNgm = k
995 : call stat_assign( var_index=stats_metadata%iNgm, var_name="Ngm", &
996 : var_description="Ngm, Graupel number concentration", var_units="num/kg", &
997 0 : l_silhs=.false., grid_kind=stats_zt )
998 0 : k = k + 1
999 :
1000 : case ('sed_rcm') ! Brian
1001 0 : stats_metadata%ised_rcm = k
1002 : call stat_assign( var_index=stats_metadata%ised_rcm, var_name="sed_rcm", &
1003 : var_description="sed_rcm, d(rcm)/dt due to cloud sedimentation", &
1004 0 : var_units="kg / [m^2 s]", l_silhs=.false., grid_kind=stats_zt )
1005 0 : k = k + 1
1006 :
1007 : case ('rsat') ! Brian
1008 0 : stats_metadata%irsat = k
1009 : call stat_assign( var_index=stats_metadata%irsat, var_name="rsat", &
1010 : var_description="rsat, Saturation mixing ratio over liquid", var_units="kg/kg", &
1011 0 : l_silhs=.false., grid_kind=stats_zt )
1012 0 : k = k + 1
1013 :
1014 : case ('rsati')
1015 0 : stats_metadata%irsati = k
1016 : call stat_assign( var_index=stats_metadata%irsati, var_name="rsati", &
1017 : var_description="rsati, Saturation mixing ratio over ice", var_units="kg/kg", &
1018 0 : l_silhs=.false., grid_kind=stats_zt )
1019 0 : k = k + 1
1020 :
1021 : case ('rrm') ! Brian
1022 0 : stats_metadata%irrm = k
1023 : call stat_assign( var_index=stats_metadata%irrm, var_name="rrm", &
1024 : var_description="rrm, Rain water mixing ratio", var_units="kg/kg", &
1025 0 : l_silhs=.false., grid_kind=stats_zt )
1026 0 : k = k + 1
1027 :
1028 : case ('rsm')
1029 0 : stats_metadata%irsm = k
1030 : call stat_assign( var_index=stats_metadata%irsm, var_name="rsm", &
1031 : var_description="rsm, Snow water mixing ratio", var_units="kg/kg", &
1032 0 : l_silhs=.false., grid_kind=stats_zt )
1033 0 : k = k + 1
1034 :
1035 : case ('rim')
1036 0 : stats_metadata%irim = k
1037 : call stat_assign( var_index=stats_metadata%irim, var_name="rim", &
1038 : var_description="rim, Pristine ice water mixing ratio", var_units="kg/kg", &
1039 0 : l_silhs=.false., grid_kind=stats_zt )
1040 0 : k = k + 1
1041 :
1042 : case ('rgm')
1043 0 : stats_metadata%irgm = k
1044 : call stat_assign( var_index=stats_metadata%irgm, var_name="rgm", &
1045 : var_description="rgm, Graupel water mixing ratio", var_units="kg/kg", &
1046 0 : l_silhs=.false., grid_kind=stats_zt )
1047 0 : k = k + 1
1048 :
1049 : case ('Nrm') ! Brian
1050 0 : stats_metadata%iNrm = k
1051 : call stat_assign( var_index=stats_metadata%iNrm, var_name="Nrm", &
1052 : var_description="Nrm, Rain drop number concentration", var_units="num/kg", &
1053 0 : l_silhs=.false., grid_kind=stats_zt )
1054 0 : k = k + 1
1055 :
1056 : case ('m_vol_rad_rain') ! Brian
1057 0 : stats_metadata%im_vol_rad_rain = k
1058 : call stat_assign( var_index=stats_metadata%im_vol_rad_rain, var_name="mvrr", &
1059 : var_description="mvrr, Rain drop mean volume radius", &
1060 : var_units="m", l_silhs=.false.,&
1061 0 : grid_kind=stats_zt )
1062 0 : k = k + 1
1063 :
1064 : case ('m_vol_rad_cloud')
1065 0 : stats_metadata%im_vol_rad_cloud = k
1066 : call stat_assign( var_index=stats_metadata%im_vol_rad_cloud, var_name="m_vol_rad_cloud", &
1067 : var_description="m_vol_rad_cloud, Cloud drop mean volume radius", var_units="m", &
1068 : l_silhs=.false., &
1069 0 : grid_kind=stats_zt )
1070 0 : k = k + 1
1071 :
1072 : case ('eff_rad_cloud')
1073 0 : stats_metadata%ieff_rad_cloud = k
1074 : call stat_assign( var_index=stats_metadata%ieff_rad_cloud, var_name="eff_rad_cloud", &
1075 : var_description="eff_rad_cloud, Cloud drop effective volume radius", &
1076 : var_units="microns", &
1077 0 : l_silhs=.false., grid_kind=stats_zt )
1078 0 : k = k + 1
1079 :
1080 : case ('eff_rad_ice')
1081 0 : stats_metadata%ieff_rad_ice = k
1082 :
1083 : call stat_assign( var_index=stats_metadata%ieff_rad_ice, var_name="eff_rad_ice", &
1084 : var_description="eff_rad_ice, Ice effective volume radius", var_units="microns", &
1085 0 : l_silhs=.false., grid_kind=stats_zt )
1086 0 : k = k + 1
1087 :
1088 : case ('eff_rad_snow')
1089 0 : stats_metadata%ieff_rad_snow = k
1090 : call stat_assign( var_index=stats_metadata%ieff_rad_snow, var_name="eff_rad_snow", &
1091 : var_description="eff_rad_snow, Snow effective volume radius", &
1092 : var_units="microns", &
1093 0 : l_silhs=.false., grid_kind=stats_zt )
1094 0 : k = k + 1
1095 :
1096 : case ('eff_rad_rain')
1097 0 : stats_metadata%ieff_rad_rain = k
1098 : call stat_assign( var_index=stats_metadata%ieff_rad_rain, var_name="eff_rad_rain", &
1099 : var_description="eff_rad_rain, Rain drop effective volume radius", &
1100 : var_units="microns", &
1101 0 : l_silhs=.false., grid_kind=stats_zt )
1102 0 : k = k + 1
1103 :
1104 : case ('eff_rad_graupel')
1105 0 : stats_metadata%ieff_rad_graupel = k
1106 : call stat_assign( var_index=stats_metadata%ieff_rad_graupel, var_name="eff_rad_graupel", &
1107 : var_description="eff_rad_graupel, Graupel effective volume radius", &
1108 : var_units="microns", &
1109 0 : l_silhs=.false., grid_kind=stats_zt )
1110 0 : k = k + 1
1111 :
1112 : case ('precip_rate_zt') ! Brian
1113 0 : stats_metadata%iprecip_rate_zt = k
1114 :
1115 : call stat_assign( var_index=stats_metadata%iprecip_rate_zt, var_name="precip_rate_zt", &
1116 : var_description="precip_rate_zt, Rain rate", var_units="mm/day", l_silhs=.false., &
1117 0 : grid_kind=stats_zt )
1118 0 : k = k + 1
1119 :
1120 : case ('radht')
1121 0 : stats_metadata%iradht = k
1122 :
1123 : call stat_assign( var_index=stats_metadata%iradht, var_name="radht", &
1124 : var_description="radht, Total (sw+lw) radiative heating rate", var_units="K/s", &
1125 0 : l_silhs=.false., grid_kind=stats_zt )
1126 0 : k = k + 1
1127 :
1128 : case ('radht_LW')
1129 0 : stats_metadata%iradht_LW = k
1130 :
1131 : call stat_assign( var_index=stats_metadata%iradht_LW, var_name="radht_LW", &
1132 : var_description="radht_LW, Long-wave radiative heating rate", var_units="K/s", &
1133 0 : l_silhs=.false., grid_kind=stats_zt )
1134 :
1135 0 : k = k + 1
1136 :
1137 : case ('radht_SW')
1138 0 : stats_metadata%iradht_SW = k
1139 : call stat_assign( var_index=stats_metadata%iradht_SW, var_name="radht_SW", &
1140 : var_description="radht_SW, Short-wave radiative heating rate", var_units="K/s", &
1141 0 : l_silhs=.false., grid_kind=stats_zt )
1142 0 : k = k + 1
1143 :
1144 : case ('diam')
1145 0 : stats_metadata%idiam = k
1146 :
1147 : call stat_assign( var_index=stats_metadata%idiam, var_name="diam", &
1148 : var_description="diam, Ice crystal diameter", var_units="m", l_silhs=.false., &
1149 0 : grid_kind=stats_zt )
1150 0 : k = k + 1
1151 :
1152 : case ('mass_ice_cryst')
1153 0 : stats_metadata%imass_ice_cryst = k
1154 : call stat_assign( var_index=stats_metadata%imass_ice_cryst, var_name="mass_ice_cryst", &
1155 : var_description="mass_ice_cryst, Mass of a single ice crystal", var_units="kg", &
1156 0 : l_silhs=.false., grid_kind=stats_zt )
1157 0 : k = k + 1
1158 :
1159 : case ('rcm_icedfs')
1160 0 : stats_metadata%ircm_icedfs = k
1161 : call stat_assign( var_index=stats_metadata%ircm_icedfs, var_name="rcm_icedfs", &
1162 : var_description="rcm_icedfs, Change in liquid due to ice", var_units="kg/kg/s", &
1163 0 : l_silhs=.false., grid_kind=stats_zt )
1164 0 : k = k + 1
1165 :
1166 : case ('u_T_cm')
1167 0 : stats_metadata%iu_T_cm = k
1168 : call stat_assign( var_index=stats_metadata%iu_T_cm, var_name="u_T_cm", &
1169 : var_description="u_T_cm, Ice crystal fallspeed", var_units="cm s^{-1}", &
1170 0 : l_silhs=.false., grid_kind=stats_zt )
1171 0 : k = k + 1
1172 :
1173 : case ('rtm_bt')
1174 0 : stats_metadata%irtm_bt = k
1175 :
1176 : call stat_assign( var_index=stats_metadata%irtm_bt, var_name="rtm_bt", &
1177 : var_description="rtm_bt, rtm budget: rtm time tendency", &
1178 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1179 0 : k = k + 1
1180 :
1181 : case ('rtm_ma')
1182 0 : stats_metadata%irtm_ma = k
1183 :
1184 : call stat_assign( var_index=stats_metadata%irtm_ma, var_name="rtm_ma", &
1185 : var_description="rtm_ma, rtm budget: rtm vertical mean advection", &
1186 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1187 0 : k = k + 1
1188 :
1189 : case ('rtm_ta')
1190 0 : stats_metadata%irtm_ta = k
1191 :
1192 : call stat_assign( var_index=stats_metadata%irtm_ta, var_name="rtm_ta", &
1193 : var_description="rtm_ta, rtm budget: rtm turbulent advection", &
1194 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1195 0 : k = k + 1
1196 :
1197 : case ('rtm_mfl')
1198 0 : stats_metadata%irtm_mfl = k
1199 :
1200 : call stat_assign( var_index=stats_metadata%irtm_mfl, var_name="rtm_mfl", &
1201 : var_description="rtm_mfl, rtm budget: rtm correction due to monotonic flux limiter", &
1202 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
1203 0 : k = k + 1
1204 :
1205 : case ('rtm_tacl')
1206 0 : stats_metadata%irtm_tacl = k
1207 :
1208 : call stat_assign( var_index=stats_metadata%irtm_tacl, var_name="rtm_tacl", &
1209 : var_description="rtm_tacl, rtm budget: rtm correction due to ta term" &
1210 : // " (wprtp) clipping", &
1211 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
1212 :
1213 0 : k = k + 1
1214 :
1215 : case ('rtm_cl')
1216 0 : stats_metadata%irtm_cl = k
1217 :
1218 : call stat_assign( var_index=stats_metadata%irtm_cl, var_name="rtm_cl", &
1219 : var_description="rtm_cl, rtm budget: rtm clipping", &
1220 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1221 :
1222 0 : k = k + 1
1223 : case ('rtm_sdmp')
1224 0 : stats_metadata%irtm_sdmp = k
1225 :
1226 : call stat_assign( var_index=stats_metadata%irtm_sdmp, var_name="rtm_sdmp", &
1227 : var_description="rtm_sdmp, rtm budget: rtm correction due to sponge damping", &
1228 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1229 0 : k = k + 1
1230 :
1231 :
1232 : case ('rtm_pd')
1233 0 : stats_metadata%irtm_pd = k
1234 :
1235 : call stat_assign( var_index=stats_metadata%irtm_pd, var_name="rtm_pd", &
1236 : var_description="rtm_pd, rtm budget: rtm positive definite adjustment", &
1237 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1238 :
1239 0 : k = k + 1
1240 :
1241 : case ('thlm_bt')
1242 0 : stats_metadata%ithlm_bt = k
1243 :
1244 : call stat_assign( var_index=stats_metadata%ithlm_bt, var_name="thlm_bt", &
1245 : var_description="thlm_bt, thlm budget: thlm time tendency", var_units="K s^{-1}", &
1246 0 : l_silhs=.false., grid_kind=stats_zt )
1247 0 : k = k + 1
1248 :
1249 : case ('thlm_ma')
1250 0 : stats_metadata%ithlm_ma = k
1251 :
1252 : call stat_assign( var_index=stats_metadata%ithlm_ma, var_name="thlm_ma", &
1253 : var_description="thlm_ma, thlm budget: thlm vertical mean advection", &
1254 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1255 0 : k = k + 1
1256 :
1257 : case ('thlm_sdmp')
1258 0 : stats_metadata%ithlm_sdmp = k
1259 :
1260 : call stat_assign( var_index=stats_metadata%ithlm_sdmp, var_name="thlm_sdmp", &
1261 : var_description="thlm_sdmp, thlm budget: thlm correction due to sponge damping", &
1262 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1263 0 : k = k + 1
1264 :
1265 :
1266 : case ('thlm_ta')
1267 0 : stats_metadata%ithlm_ta = k
1268 :
1269 : call stat_assign( var_index=stats_metadata%ithlm_ta, var_name="thlm_ta", &
1270 : var_description="thlm_ta, thlm budget: thlm turbulent advection", &
1271 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1272 0 : k = k + 1
1273 :
1274 : case ('thlm_mfl')
1275 0 : stats_metadata%ithlm_mfl = k
1276 :
1277 : call stat_assign( var_index=stats_metadata%ithlm_mfl, var_name="thlm_mfl", &
1278 : var_description="thlm_mfl, thlm budget: thlm correction due to monotonic " &
1279 : // "flux limiter", &
1280 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1281 0 : k = k + 1
1282 :
1283 : case ('thlm_tacl')
1284 0 : stats_metadata%ithlm_tacl = k
1285 :
1286 : call stat_assign( var_index=stats_metadata%ithlm_tacl, var_name="thlm_tacl", &
1287 : var_description="thlm_tacl, thlm budget: thlm correction due to ta term " &
1288 : // "(wpthlp) clipping", &
1289 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1290 0 : k = k + 1
1291 :
1292 : case ('thlm_cl')
1293 0 : stats_metadata%ithlm_cl = k
1294 :
1295 : call stat_assign( var_index=stats_metadata%ithlm_cl, var_name="thlm_cl", &
1296 : var_description="thlm_cl, thlm budget: thlm_cl", var_units="K s^{-1}", &
1297 0 : l_silhs=.false., grid_kind=stats_zt )
1298 0 : k = k + 1
1299 :
1300 : case ('wp3_bt')
1301 0 : stats_metadata%iwp3_bt = k
1302 :
1303 : call stat_assign( var_index=stats_metadata%iwp3_bt, var_name="wp3_bt", &
1304 : var_description="wp3_bt, wp3 budget: wp3 time tendency", &
1305 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1306 0 : k = k + 1
1307 :
1308 : case ('wp3_ma')
1309 0 : stats_metadata%iwp3_ma = k
1310 :
1311 : call stat_assign( var_index=stats_metadata%iwp3_ma, var_name="wp3_ma", &
1312 : var_description="wp3_ma, wp3 budget: wp3 vertical mean advection", &
1313 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1314 0 : k = k + 1
1315 :
1316 : case ('wp3_ta')
1317 0 : stats_metadata%iwp3_ta = k
1318 :
1319 : call stat_assign( var_index=stats_metadata%iwp3_ta, var_name="wp3_ta", &
1320 : var_description="wp3_ta, wp3 budget: wp3 turbulent advection", &
1321 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1322 :
1323 0 : k = k + 1
1324 :
1325 : case ('wp3_tp')
1326 0 : stats_metadata%iwp3_tp = k
1327 : call stat_assign( var_index=stats_metadata%iwp3_tp, var_name="wp3_tp", &
1328 : var_description="wp3_tp, wp3 budget: wp3 turbulent transport", &
1329 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1330 0 : k = k + 1
1331 :
1332 : case ('wp3_ac')
1333 0 : stats_metadata%iwp3_ac = k
1334 : call stat_assign( var_index=stats_metadata%iwp3_ac, var_name="wp3_ac", &
1335 : var_description="wp3_ac, wp3 budget: wp3 accumulation term", &
1336 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1337 0 : k = k + 1
1338 :
1339 : case ('wp3_bp1')
1340 0 : stats_metadata%iwp3_bp1 = k
1341 : call stat_assign( var_index=stats_metadata%iwp3_bp1, var_name="wp3_bp1", &
1342 : var_description="wp3_bp1, wp3 budget: wp3 buoyancy production", &
1343 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1344 0 : k = k + 1
1345 :
1346 : case ('wp3_pr_tp')
1347 0 : stats_metadata%iwp3_pr_tp = k
1348 : call stat_assign( var_index=stats_metadata%iwp3_pr_tp, var_name="wp3_pr_tp", &
1349 : var_description= &
1350 : "wp3_pr_tp, wp3 budget: wp3 pressure damping of turbulent production", &
1351 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1352 0 : k = k + 1
1353 :
1354 : case ('wp3_pr_turb')
1355 0 : stats_metadata%iwp3_pr_turb = k
1356 : call stat_assign( var_index=stats_metadata%iwp3_pr_turb, var_name="wp3_pr_turb", &
1357 : var_description="wp3_pr_turb, wp3 budget: wp3 pressure-turbulence correlation term", &
1358 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1359 0 : k = k + 1
1360 :
1361 : case ('wp3_pr_dfsn')
1362 0 : stats_metadata%iwp3_pr_dfsn = k
1363 : call stat_assign( var_index=stats_metadata%iwp3_pr_dfsn, var_name="wp3_pr_dfsn", &
1364 : var_description="wp3_pr_dfsn, wp3 budget: wp3 pressure diffusion term", &
1365 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1366 0 : k = k + 1
1367 :
1368 : case ('wp3_pr1')
1369 0 : stats_metadata%iwp3_pr1 = k
1370 : call stat_assign( var_index=stats_metadata%iwp3_pr1, var_name="wp3_pr1", &
1371 : var_description="wp3_pr1, wp3 budget: wp3 pressure term 1", &
1372 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1373 0 : k = k + 1
1374 :
1375 : case ('wp3_pr2')
1376 0 : stats_metadata%iwp3_pr2 = k
1377 : call stat_assign( var_index=stats_metadata%iwp3_pr2, var_name="wp3_pr2", &
1378 : var_description="wp3_pr2, wp3 budget: wp3 pressure term 2", &
1379 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1380 :
1381 0 : k = k + 1
1382 :
1383 : case ('wp3_pr3')
1384 0 : stats_metadata%iwp3_pr3 = k
1385 : call stat_assign( var_index=stats_metadata%iwp3_pr3, var_name="wp3_pr3", &
1386 : var_description="wp3_pr3, wp3 budget: wp3 pressure term 3", &
1387 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1388 :
1389 0 : k = k + 1
1390 :
1391 : case ('wp3_dp1')
1392 0 : stats_metadata%iwp3_dp1 = k
1393 : call stat_assign( var_index=stats_metadata%iwp3_dp1, var_name="wp3_dp1", &
1394 : var_description="wp3_dp1, wp3 budget: wp3 dissipation term 1", &
1395 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1396 0 : k = k + 1
1397 :
1398 : case ('wp3_sdmp')
1399 0 : stats_metadata%iwp3_sdmp = k
1400 : call stat_assign( var_index=stats_metadata%iwp3_sdmp, var_name="wp3_sdmp", &
1401 : var_description="wp3_sdmp, wp3 budget: wp3 sponge damping term", &
1402 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1403 0 : k = k + 1
1404 :
1405 : case ('wp3_cl')
1406 0 : stats_metadata%iwp3_cl = k
1407 : call stat_assign( var_index=stats_metadata%iwp3_cl, var_name="wp3_cl", &
1408 : var_description="wp3_cl, wp3 budget: wp3 clipping term", &
1409 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1410 0 : k = k + 1
1411 :
1412 : case ('wp3_splat')
1413 0 : stats_metadata%iwp3_splat = k
1414 : call stat_assign( var_index=stats_metadata%iwp3_splat, var_name="wp3_splat", &
1415 : var_description="wp3_splat, wp3 budget: wp3 splatting term", &
1416 0 : var_units="m^3 s^-4", l_silhs=.false., grid_kind=stats_zt )
1417 0 : k = k + 1
1418 :
1419 : case ('rtp3_bt')
1420 0 : stats_metadata%irtp3_bt = k
1421 :
1422 : call stat_assign( var_index=stats_metadata%irtp3_bt, var_name="rtp3_bt", &
1423 : var_description="rtp3_bt, rtp3 budget: rtp3 time tendency" &
1424 : // "[kg^{3} kg^{-3} s^{-1}]", &
1425 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1426 0 : grid_kind=stats_zt )
1427 0 : k = k + 1
1428 :
1429 : case ('rtp3_tp')
1430 0 : stats_metadata%irtp3_tp = k
1431 :
1432 : call stat_assign( var_index=stats_metadata%irtp3_tp, var_name="rtp3_tp", &
1433 : var_description="rtp3_tp, rtp3 budget: rtp3 turbulent production" &
1434 : // "[kg^{3} kg^{-3} s^{-1}]", &
1435 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1436 0 : grid_kind=stats_zt )
1437 0 : k = k + 1
1438 :
1439 : case ('rtp3_ac')
1440 0 : stats_metadata%irtp3_ac = k
1441 :
1442 : call stat_assign( var_index=stats_metadata%irtp3_ac, var_name="rtp3_ac", &
1443 : var_description="rtp3_ac, rtp3 budget: rtp3 accumulation" &
1444 : // "[kg^{3} kg^{-3} s^{-1}]", &
1445 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1446 0 : grid_kind=stats_zt )
1447 0 : k = k + 1
1448 :
1449 : case ('rtp3_dp')
1450 0 : stats_metadata%irtp3_dp = k
1451 :
1452 : call stat_assign( var_index=stats_metadata%irtp3_dp, var_name="rtp3_dp", &
1453 : var_description="rtp3_dp, rtp3 budget: rtp3 dissipation" &
1454 : // "[kg^{3} kg^{-3} s^{-1}]", &
1455 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1456 0 : grid_kind=stats_zt )
1457 0 : k = k + 1
1458 :
1459 : case ('thlp3_bt')
1460 0 : stats_metadata%ithlp3_bt = k
1461 :
1462 : call stat_assign( var_index=stats_metadata%ithlp3_bt, var_name="thlp3_bt", &
1463 : var_description="thlp3_bt, thlp3 budget: thlp3 time tendency" &
1464 : // "[K^{3} s^{-1}]", &
1465 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1466 0 : k = k + 1
1467 :
1468 : case ('thlp3_tp')
1469 0 : stats_metadata%ithlp3_tp = k
1470 :
1471 : call stat_assign( var_index=stats_metadata%ithlp3_tp, var_name="thlp3_tp", &
1472 : var_description="thlp3_tp, thlp3 budget: thlp3 turbulent production" &
1473 : // "[K^{3} s^{-1}]", &
1474 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1475 0 : k = k + 1
1476 :
1477 : case ('thlp3_ac')
1478 0 : stats_metadata%ithlp3_ac = k
1479 :
1480 : call stat_assign( var_index=stats_metadata%ithlp3_ac, var_name="thlp3_ac", &
1481 : var_description="thlp3_ac, thlp3 budget: thlp3 accumulation" &
1482 : // "[K^{3} s^{-1}]", &
1483 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1484 0 : k = k + 1
1485 :
1486 : case ('thlp3_dp')
1487 0 : stats_metadata%ithlp3_dp = k
1488 :
1489 : call stat_assign( var_index=stats_metadata%ithlp3_dp, var_name="thlp3_dp", &
1490 : var_description="thlp3_dp, thlp3 budget: thlp3 dissipation", &
1491 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1492 0 : k = k + 1
1493 :
1494 : case ('rrm_bt')
1495 0 : stats_metadata%irrm_bt = k
1496 : call stat_assign( var_index=stats_metadata%irrm_bt, var_name="rrm_bt", &
1497 : var_description="rrm_bt, rrm budget: rrm time tendency", &
1498 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1499 0 : k = k + 1
1500 :
1501 : case ('rrm_ma')
1502 0 : stats_metadata%irrm_ma = k
1503 :
1504 : call stat_assign( var_index=stats_metadata%irrm_ma, var_name="rrm_ma", &
1505 : var_description="rrm_ma, rrm budget: rrm vertical mean advection", &
1506 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1507 0 : k = k + 1
1508 :
1509 : case ('rrm_sd')
1510 0 : stats_metadata%irrm_sd = k
1511 :
1512 : call stat_assign( var_index=stats_metadata%irrm_sd, var_name="rrm_sd", &
1513 : var_description="rrm_sd, rrm budget: rrm sedimentation", &
1514 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1515 0 : k = k + 1
1516 :
1517 : case ('rrm_ts')
1518 0 : stats_metadata%irrm_ts = k
1519 :
1520 : call stat_assign( var_index=stats_metadata%irrm_ts, var_name="rrm_ts", &
1521 : var_description="rrm_ts, rrm budget: rrm turbulent sedimentation", &
1522 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1523 0 : k = k + 1
1524 :
1525 : case ('rrm_sd_morr')
1526 0 : stats_metadata%irrm_sd_morr = k
1527 :
1528 : call stat_assign( var_index=stats_metadata%irrm_sd_morr, var_name="rrm_sd_morr", &
1529 : var_description="rrm_sd_morr, rrm sedimentation when using morrision microphysics &
1530 : &(not in budget, included in rrm_mc)", &
1531 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
1532 0 : k = k + 1
1533 :
1534 : case ('rrm_ta')
1535 0 : stats_metadata%irrm_ta = k
1536 :
1537 : call stat_assign( var_index=stats_metadata%irrm_ta, var_name="rrm_ta", &
1538 : var_description="rrm_ta, rrm budget: rrm turbulent advection", &
1539 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1540 0 : k = k + 1
1541 :
1542 : case ('rrm_evap')
1543 0 : stats_metadata%irrm_evap = k
1544 :
1545 : call stat_assign( var_index=stats_metadata%irrm_evap, var_name="rrm_evap", &
1546 : var_description="rrm_evap, rrm evaporation rate", &
1547 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1548 0 : k = k + 1
1549 :
1550 : case ('rrm_auto')
1551 0 : stats_metadata%irrm_auto = k
1552 :
1553 : call stat_assign( var_index=stats_metadata%irrm_auto, var_name="rrm_auto", &
1554 : var_description="rrm_auto, rrm autoconversion rate", &
1555 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1556 0 : k = k + 1
1557 :
1558 : case ('rrm_accr')
1559 0 : stats_metadata%irrm_accr = k
1560 : call stat_assign( var_index=stats_metadata%irrm_accr, var_name="rrm_accr", &
1561 : var_description="rrm_accr, rrm accretion rate", &
1562 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1563 0 : k = k + 1
1564 :
1565 : case ('rrm_evap_adj')
1566 0 : stats_metadata%irrm_evap_adj = k
1567 :
1568 : call stat_assign( var_index=stats_metadata%irrm_evap_adj, var_name="rrm_evap_adj", &
1569 : var_description="rrm_evap_adj, rrm evaporation adjustment due to over-evaporation", &
1570 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1571 0 : k = k + 1
1572 :
1573 : case ('rrm_src_adj')
1574 0 : stats_metadata%irrm_src_adj = k
1575 :
1576 : call stat_assign( var_index=stats_metadata%irrm_src_adj, var_name="rrm_src_adj", &
1577 : var_description="rrm_src_adj, rrm source term adjustment due to over-depletion", &
1578 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1579 0 : k = k + 1
1580 :
1581 : case ('rrm_mc_nonadj')
1582 0 : stats_metadata%irrm_mc_nonadj = k
1583 :
1584 : call stat_assign( var_index=stats_metadata%irrm_mc_nonadj, var_name="rrm_mc_nonadj", &
1585 : var_description="rrm_mc_nonadj, Value of rrm_mc tendency before adjustment", &
1586 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1587 0 : k = k + 1
1588 :
1589 : case ('rrm_hf')
1590 0 : stats_metadata%irrm_hf = k
1591 : call stat_assign( var_index=stats_metadata%irrm_hf, var_name="rrm_hf", &
1592 : var_description="rrm_hf, rrm budget: rrm hole-filling term", &
1593 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1594 0 : k = k + 1
1595 :
1596 : case ('rrm_wvhf')
1597 0 : stats_metadata%irrm_wvhf = k
1598 : call stat_assign( var_index=stats_metadata%irrm_wvhf, var_name="rrm_wvhf", &
1599 : var_description="rrm_wvhf, rrm budget: rrm water vapor hole-filling term", &
1600 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1601 0 : k = k + 1
1602 :
1603 : case ('rrm_cl')
1604 0 : stats_metadata%irrm_cl = k
1605 : call stat_assign( var_index=stats_metadata%irrm_cl, var_name="rrm_cl", &
1606 : var_description="rrm_cl, rrm budget: rrm clipping term", &
1607 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1608 0 : k = k + 1
1609 :
1610 : case ('rrm_mc')
1611 0 : stats_metadata%irrm_mc = k
1612 :
1613 : call stat_assign( var_index=stats_metadata%irrm_mc, var_name="rrm_mc", &
1614 : var_description="rrm_mc, rrm budget: Change in rrm due to microphysics", &
1615 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1616 0 : k = k + 1
1617 :
1618 : case ('Nrm_bt')
1619 0 : stats_metadata%iNrm_bt = k
1620 : call stat_assign( var_index=stats_metadata%iNrm_bt, var_name="Nrm_bt", &
1621 : var_description="Nrm_bt, Nrm budget: Nrm time tendency", &
1622 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1623 :
1624 0 : k = k + 1
1625 :
1626 : case ('Nrm_ma')
1627 0 : stats_metadata%iNrm_ma = k
1628 :
1629 : call stat_assign( var_index=stats_metadata%iNrm_ma, var_name="Nrm_ma", &
1630 : var_description="Nrm_ma, Nrm budget: Nrm vertical mean advection", &
1631 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1632 0 : k = k + 1
1633 :
1634 : case ('Nrm_sd')
1635 0 : stats_metadata%iNrm_sd = k
1636 :
1637 : call stat_assign( var_index=stats_metadata%iNrm_sd, var_name="Nrm_sd", &
1638 : var_description="Nrm_sd, Nrm budget: Nrm sedimentation", &
1639 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1640 :
1641 0 : k = k + 1
1642 :
1643 : case ('Nrm_ts')
1644 0 : stats_metadata%iNrm_ts = k
1645 :
1646 : call stat_assign( var_index=stats_metadata%iNrm_ts, var_name="Nrm_ts", &
1647 : var_description="Nrm_ts, Nrm budget: Nrm turbulent sedimentation", &
1648 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1649 0 : k = k + 1
1650 :
1651 : case ('Nrm_ta')
1652 0 : stats_metadata%iNrm_ta = k
1653 : call stat_assign( var_index=stats_metadata%iNrm_ta, var_name="Nrm_ta", &
1654 : var_description="Nrm_ta, Nrm budget: Nrm turbulent advection", &
1655 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1656 :
1657 0 : k = k + 1
1658 :
1659 : case ('Nrm_evap')
1660 0 : stats_metadata%iNrm_evap = k
1661 :
1662 : call stat_assign( var_index=stats_metadata%iNrm_evap, var_name="Nrm_evap", &
1663 : var_description="Nrm_evap, Nrm evaporation rate", var_units="(num/kg)/s", &
1664 0 : l_silhs=.false., grid_kind=stats_zt )
1665 0 : k = k + 1
1666 :
1667 : case ('Nrm_auto')
1668 0 : stats_metadata%iNrm_auto = k
1669 :
1670 : call stat_assign( var_index=stats_metadata%iNrm_auto, var_name="Nrm_auto", &
1671 : var_description="Nrm_auto, Nrm autoconversion rate", var_units="(num/kg)/s", &
1672 0 : l_silhs=.false., grid_kind=stats_zt )
1673 :
1674 0 : k = k + 1
1675 :
1676 : case ('Nrm_evap_adj')
1677 0 : stats_metadata%iNrm_evap_adj = k
1678 :
1679 : call stat_assign( var_index=stats_metadata%iNrm_evap_adj, var_name="Nrm_evap_adj", &
1680 : var_description="Nrm_evap_adj, Nrm evaporation adjustment due to over-evaporation", &
1681 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1682 0 : k = k + 1
1683 :
1684 : case ('Nrm_src_adj')
1685 0 : stats_metadata%iNrm_src_adj = k
1686 :
1687 : call stat_assign( var_index=stats_metadata%iNrm_src_adj, var_name="Nrm_src_adj", &
1688 : var_description="Nrm_src_adj, Nrm source term adjustment due to over-depletion", &
1689 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1690 0 : k = k + 1
1691 :
1692 : case ('Nrm_cl')
1693 0 : stats_metadata%iNrm_cl = k
1694 : call stat_assign( var_index=stats_metadata%iNrm_cl, var_name="Nrm_cl", &
1695 : var_description="Nrm_cl, Nrm budget: Nrm clipping term", &
1696 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1697 0 : k = k + 1
1698 :
1699 : case ('Nrm_mc')
1700 0 : stats_metadata%iNrm_mc = k
1701 : call stat_assign( var_index=stats_metadata%iNrm_mc, var_name="Nrm_mc", &
1702 : var_description="Nrm_mc, Nrm budget: Change in Nrm due to microphysics " &
1703 : // "(Not in budget)", &
1704 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1705 0 : k = k + 1
1706 :
1707 : case ('rsm_bt')
1708 0 : stats_metadata%irsm_bt = k
1709 : call stat_assign( var_index=stats_metadata%irsm_bt, var_name="rsm_bt", &
1710 : var_description="rsm_bt, rsm budget: rsm time tendency", &
1711 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1712 :
1713 0 : k = k + 1
1714 :
1715 : case ('rsm_ma')
1716 0 : stats_metadata%irsm_ma = k
1717 :
1718 : call stat_assign( var_index=stats_metadata%irsm_ma, var_name="rsm_ma", &
1719 : var_description="rsm_ma, rsm budget: rsm vertical mean advection", &
1720 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1721 0 : k = k + 1
1722 :
1723 : case ('rsm_sd')
1724 0 : stats_metadata%irsm_sd = k
1725 : call stat_assign( var_index=stats_metadata%irsm_sd, var_name="rsm_sd", &
1726 : var_description="rsm_sd, rsm budget: rsm sedimentation", &
1727 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1728 0 : k = k + 1
1729 :
1730 : case ('rsm_sd_morr')
1731 0 : stats_metadata%irsm_sd_morr = k
1732 : call stat_assign( var_index=stats_metadata%irsm_sd_morr, var_name="rsm_sd_morr", &
1733 : var_description="rsm_sd_morr, rsm sedimentation when using morrison microphysics &
1734 : &(Not in budget, included in rsm_mc)", &
1735 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1736 0 : k = k + 1
1737 :
1738 : case ('rsm_ta')
1739 0 : stats_metadata%irsm_ta = k
1740 :
1741 : call stat_assign( var_index=stats_metadata%irsm_ta, var_name="rsm_ta", &
1742 : var_description="rsm_ta, rsm budget: rsm turbulent advection", &
1743 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1744 0 : k = k + 1
1745 :
1746 : case ('rsm_mc')
1747 0 : stats_metadata%irsm_mc = k
1748 :
1749 : call stat_assign( var_index=stats_metadata%irsm_mc, var_name="rsm_mc", &
1750 : var_description="rsm_mc, rsm budget: Change in rsm due to microphysics", &
1751 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1752 0 : k = k + 1
1753 :
1754 : case ('rsm_hf')
1755 0 : stats_metadata%irsm_hf = k
1756 :
1757 : call stat_assign( var_index=stats_metadata%irsm_hf, var_name="rsm_hf", &
1758 : var_description="rsm_hf, rsm budget: rsm hole-filling term", &
1759 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1760 0 : k = k + 1
1761 :
1762 : case ('rsm_wvhf')
1763 0 : stats_metadata%irsm_wvhf = k
1764 :
1765 : call stat_assign( var_index=stats_metadata%irsm_wvhf, var_name="rsm_wvhf", &
1766 : var_description="rsm_wvhf, rsm budget: rsm water vapor hole-filling term", &
1767 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1768 0 : k = k + 1
1769 :
1770 : case ('rsm_cl')
1771 0 : stats_metadata%irsm_cl = k
1772 :
1773 : call stat_assign( var_index=stats_metadata%irsm_cl, var_name="rsm_cl", &
1774 : var_description="rsm_cl, rsm budget: rsm clipping term", &
1775 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1776 0 : k = k + 1
1777 :
1778 : case ('Nsm_bt')
1779 0 : stats_metadata%iNsm_bt = k
1780 : call stat_assign( var_index=stats_metadata%iNsm_bt, var_name="Nsm_bt", &
1781 : var_description="Nsm_bt, Nsm budget", var_units="(num/kg)/s", &
1782 0 : l_silhs=.false., grid_kind=stats_zt )
1783 :
1784 0 : k = k + 1
1785 :
1786 : case ('Nsm_ma')
1787 0 : stats_metadata%iNsm_ma = k
1788 :
1789 : call stat_assign( var_index=stats_metadata%iNsm_ma, var_name="Nsm_ma", &
1790 : var_description="Nsm_ma, Nsm budget: Nsm mean advection", &
1791 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1792 0 : k = k + 1
1793 :
1794 : case ('Nsm_sd')
1795 0 : stats_metadata%iNsm_sd = k
1796 :
1797 : call stat_assign( var_index=stats_metadata%iNsm_sd, var_name="Nsm_sd", &
1798 : var_description="Nsm_sd, Nsm budget: Nsm sedimentation", &
1799 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1800 :
1801 0 : k = k + 1
1802 :
1803 : case ('Nsm_ta')
1804 0 : stats_metadata%iNsm_ta = k
1805 : call stat_assign( var_index=stats_metadata%iNsm_ta, var_name="Nsm_ta", &
1806 : var_description="Nsm_ta, Nsm budget: Nsm turbulent advection", &
1807 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1808 :
1809 0 : k = k + 1
1810 :
1811 : case ('Nsm_mc')
1812 0 : stats_metadata%iNsm_mc = k
1813 : call stat_assign( var_index=stats_metadata%iNsm_mc, var_name="Nsm_mc", &
1814 : var_description="Nsm_mc, Nsm budget: Nsm microphysics", &
1815 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1816 :
1817 0 : k = k + 1
1818 :
1819 : case ('Nsm_cl')
1820 0 : stats_metadata%iNsm_cl = k
1821 :
1822 : call stat_assign( var_index=stats_metadata%iNsm_cl, var_name="Nsm_cl", &
1823 : var_description="Nsm_cl, Nsm budget: Nsm clipping term", &
1824 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1825 0 : k = k + 1
1826 :
1827 : case ('rim_bt')
1828 0 : stats_metadata%irim_bt = k
1829 :
1830 : call stat_assign( var_index=stats_metadata%irim_bt, var_name="rim_bt", &
1831 : var_description="rim_bt, rim budget: rim time tendency", &
1832 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1833 :
1834 0 : k = k + 1
1835 :
1836 : case ('rim_ma')
1837 0 : stats_metadata%irim_ma = k
1838 :
1839 : call stat_assign( var_index=stats_metadata%irim_ma, var_name="rim_ma", &
1840 : var_description="rim_ma, rim budget: rim vertical mean advection", &
1841 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1842 0 : k = k + 1
1843 :
1844 : case ('rim_sd')
1845 0 : stats_metadata%irim_sd = k
1846 :
1847 : call stat_assign( var_index=stats_metadata%irim_sd, var_name="rim_sd", &
1848 : var_description="rim_sd, rim budget: rim sedimentation", &
1849 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1850 0 : k = k + 1
1851 :
1852 : case ('rim_sd_mg_morr')
1853 0 : stats_metadata%irim_sd_mg_morr = k
1854 :
1855 : call stat_assign( var_index=stats_metadata%irim_sd_mg_morr, var_name="rim_sd_mg_morr", &
1856 : var_description="rim_sd_mg_morr, rim sedimentation when using morrison or MG " &
1857 : // "microphysics" &
1858 : // "(not in budget, included in rim_mc)", &
1859 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1860 0 : k = k + 1
1861 :
1862 : case ('rim_ta')
1863 0 : stats_metadata%irim_ta = k
1864 :
1865 : call stat_assign( var_index=stats_metadata%irim_ta, var_name="rim_ta", &
1866 : var_description="rim_ta, rim budget: rim turbulent advection", &
1867 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1868 0 : k = k + 1
1869 :
1870 : case ('rim_mc')
1871 0 : stats_metadata%irim_mc = k
1872 :
1873 : call stat_assign( var_index=stats_metadata%irim_mc, var_name="rim_mc", &
1874 : var_description="rim_mc, rim budget: Change in rim due to microphysics", &
1875 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1876 0 : k = k + 1
1877 :
1878 : case ('rim_hf')
1879 0 : stats_metadata%irim_hf = k
1880 :
1881 : call stat_assign( var_index=stats_metadata%irim_hf, var_name="rim_hf", &
1882 : var_description="rim_hf, rim budget: rim hole-filling term", &
1883 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1884 0 : k = k + 1
1885 :
1886 : case ('rim_wvhf')
1887 0 : stats_metadata%irim_wvhf = k
1888 :
1889 : call stat_assign( var_index=stats_metadata%irim_wvhf, var_name="rim_wvhf", &
1890 : var_description="rim_wvhf, rim budget: rim water vapor hole-filling term", &
1891 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1892 0 : k = k + 1
1893 :
1894 : case ('rim_cl')
1895 0 : stats_metadata%irim_cl = k
1896 :
1897 : call stat_assign( var_index=stats_metadata%irim_cl, var_name="rim_cl", &
1898 : var_description="rim_cl, rim budget: rim clipping term", &
1899 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1900 0 : k = k + 1
1901 :
1902 : case ('rgm_bt')
1903 0 : stats_metadata%irgm_bt = k
1904 :
1905 : call stat_assign( var_index=stats_metadata%irgm_bt, var_name="rgm_bt", &
1906 : var_description="rgm_bt, rgm budget: rgm time tendency", &
1907 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1908 0 : k = k + 1
1909 :
1910 : case ('rgm_ma')
1911 0 : stats_metadata%irgm_ma = k
1912 :
1913 : call stat_assign( var_index=stats_metadata%irgm_ma, var_name="rgm_ma", &
1914 : var_description="rgm_ma, rgm budget: rgm vertical mean advection", &
1915 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1916 0 : k = k + 1
1917 :
1918 : case ('rgm_sd')
1919 0 : stats_metadata%irgm_sd = k
1920 :
1921 : call stat_assign( var_index=stats_metadata%irgm_sd, var_name="rgm_sd", &
1922 : var_description="rgm_sd, rgm budget: rgm sedimentation", &
1923 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1924 0 : k = k + 1
1925 :
1926 : case ('rgm_sd_morr')
1927 0 : stats_metadata%irgm_sd_morr = k
1928 :
1929 : call stat_assign( var_index=stats_metadata%irgm_sd_morr, var_name="rgm_sd_morr", &
1930 : var_description="rgm_sd_morr, rgm sedimentation when using morrison microphysics &
1931 : &(not in budget, included in rgm_mc)", &
1932 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1933 0 : k = k + 1
1934 :
1935 : case ('rgm_ta')
1936 0 : stats_metadata%irgm_ta = k
1937 :
1938 : call stat_assign( var_index=stats_metadata%irgm_ta, var_name="rgm_ta", &
1939 : var_description="rgm_ta, rgm budget: rgm turbulent advection", &
1940 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1941 0 : k = k + 1
1942 :
1943 : case ('rgm_mc')
1944 0 : stats_metadata%irgm_mc = k
1945 :
1946 : call stat_assign( var_index=stats_metadata%irgm_mc, var_name="rgm_mc", &
1947 : var_description="rgm_mc, rgm budget: Change in rgm due to microphysics", &
1948 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1949 0 : k = k + 1
1950 :
1951 : case ('rgm_hf')
1952 0 : stats_metadata%irgm_hf = k
1953 :
1954 : call stat_assign( var_index=stats_metadata%irgm_hf, var_name="rgm_hf", &
1955 : var_description="rgm_hf, rgm budget: rgm hole-filling term", &
1956 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1957 0 : k = k + 1
1958 :
1959 : case ('rgm_wvhf')
1960 0 : stats_metadata%irgm_wvhf = k
1961 :
1962 : call stat_assign( var_index=stats_metadata%irgm_wvhf, var_name="rgm_wvhf", &
1963 : var_description="rgm_wvhf, rgm budget: rgm water vapor hole-filling term", &
1964 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1965 0 : k = k + 1
1966 :
1967 : case ('rgm_cl')
1968 0 : stats_metadata%irgm_cl = k
1969 :
1970 : call stat_assign( var_index=stats_metadata%irgm_cl, var_name="rgm_cl", &
1971 : var_description="rgm_cl, rgm budget: rgm clipping term", &
1972 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1973 0 : k = k + 1
1974 :
1975 : case ('Ngm_bt')
1976 0 : stats_metadata%iNgm_bt = k
1977 : call stat_assign( var_index=stats_metadata%iNgm_bt, var_name="Ngm_bt", &
1978 : var_description="Ngm_bt, Ngm budget:", var_units="(num/kg)/s", &
1979 0 : l_silhs=.false., grid_kind=stats_zt )
1980 :
1981 0 : k = k + 1
1982 :
1983 : case ('Ngm_ma')
1984 0 : stats_metadata%iNgm_ma = k
1985 :
1986 : call stat_assign( var_index=stats_metadata%iNgm_ma, var_name="Ngm_ma", &
1987 : var_description="Ngm_ma, Ngm budget: Ngm mean advection", &
1988 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1989 0 : k = k + 1
1990 :
1991 : case ('Ngm_sd')
1992 0 : stats_metadata%iNgm_sd = k
1993 :
1994 : call stat_assign( var_index=stats_metadata%iNgm_sd, var_name="Ngm_sd", &
1995 : var_description="Ngm_sd, Ngm budget: Ngm sedimentation", &
1996 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1997 :
1998 0 : k = k + 1
1999 :
2000 : case ('Ngm_ta')
2001 0 : stats_metadata%iNgm_ta = k
2002 : call stat_assign( var_index=stats_metadata%iNgm_ta, var_name="Ngm_ta", &
2003 : var_description="Ngm_ta, Ngm budget: Ngm turbulent advection", &
2004 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2005 :
2006 0 : k = k + 1
2007 :
2008 : case ('Ngm_mc')
2009 0 : stats_metadata%iNgm_mc = k
2010 :
2011 : call stat_assign( var_index=stats_metadata%iNgm_mc, var_name="Ngm_mc", &
2012 : var_description="Ngm_mc, Ngm budget: Ngm microphysics term", &
2013 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2014 0 : k = k + 1
2015 :
2016 : case ('Ngm_cl')
2017 0 : stats_metadata%iNgm_cl = k
2018 :
2019 : call stat_assign( var_index=stats_metadata%iNgm_cl, var_name="Ngm_cl", &
2020 : var_description="Ngm_cl, Ngm budget: Ngm clipping term", &
2021 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2022 0 : k = k + 1
2023 :
2024 : case ('Nim_bt')
2025 0 : stats_metadata%iNim_bt = k
2026 : call stat_assign( var_index=stats_metadata%iNim_bt, var_name="Nim_bt", &
2027 : var_description="Nim_bt, Nim budget", var_units="(num/kg)/s", l_silhs=.false., &
2028 0 : grid_kind=stats_zt )
2029 :
2030 0 : k = k + 1
2031 :
2032 : case ('Nim_ma')
2033 0 : stats_metadata%iNim_ma = k
2034 :
2035 : call stat_assign( var_index=stats_metadata%iNim_ma, var_name="Nim_ma", &
2036 : var_description="Nim_ma, Nim budget: Nim mean advection", &
2037 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2038 0 : k = k + 1
2039 :
2040 : case ('Nim_sd')
2041 0 : stats_metadata%iNim_sd = k
2042 :
2043 : call stat_assign( var_index=stats_metadata%iNim_sd, var_name="Nim_sd", &
2044 : var_description="Nim_sd, Nim budget: Nim sedimentation", &
2045 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2046 :
2047 0 : k = k + 1
2048 :
2049 : case ('Nim_ta')
2050 0 : stats_metadata%iNim_ta = k
2051 : call stat_assign( var_index=stats_metadata%iNim_ta, var_name="Nim_ta", &
2052 : var_description="Nim_ta, Nim budget: Nim turbulent advection", &
2053 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2054 :
2055 0 : k = k + 1
2056 :
2057 : case ('Nim_mc')
2058 0 : stats_metadata%iNim_mc = k
2059 :
2060 : call stat_assign( var_index=stats_metadata%iNim_mc, var_name="Nim_mc", &
2061 : var_description="Nim_mc, Nim budget: Nim microphysics term", &
2062 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2063 0 : k = k + 1
2064 :
2065 : case ('Nim_cl')
2066 0 : stats_metadata%iNim_cl = k
2067 :
2068 : call stat_assign( var_index=stats_metadata%iNim_cl, var_name="Nim_cl", &
2069 : var_description="Nim_cl, Nim budget: Nim clipping term", &
2070 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2071 0 : k = k + 1
2072 :
2073 : case ('Ncm_bt')
2074 0 : stats_metadata%iNcm_bt = k
2075 : call stat_assign( var_index=stats_metadata%iNcm_bt, var_name="Ncm_bt", &
2076 : var_description="Ncm_bt, Ncm budget: Cloud droplet number concentration budget", &
2077 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2078 :
2079 0 : k = k + 1
2080 :
2081 : case ('Ncm_ma')
2082 0 : stats_metadata%iNcm_ma = k
2083 :
2084 : call stat_assign( var_index=stats_metadata%iNcm_ma, var_name="Ncm_ma", &
2085 : var_description="Ncm_ma, Ncm budget: Ncm vertical mean advection", &
2086 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2087 0 : k = k + 1
2088 :
2089 : case ('Ncm_act')
2090 0 : stats_metadata%iNcm_act = k
2091 :
2092 : call stat_assign( var_index=stats_metadata%iNcm_act, var_name="Ncm_act", &
2093 : var_description="Ncm_act, Ncm budget: Change in Ncm due to activation", &
2094 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2095 :
2096 0 : k = k + 1
2097 :
2098 : case ('Ncm_ta')
2099 0 : stats_metadata%iNcm_ta = k
2100 : call stat_assign( var_index=stats_metadata%iNcm_ta, var_name="Ncm_ta", &
2101 : var_description="Ncm_ta, Ncm budget: Ncm turbulent advection", &
2102 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2103 :
2104 0 : k = k + 1
2105 :
2106 : case ('Ncm_mc')
2107 0 : stats_metadata%iNcm_mc = k
2108 :
2109 : call stat_assign( var_index=stats_metadata%iNcm_mc, var_name="Ncm_mc", &
2110 : var_description="Ncm_mc, Ncm budget: Change in Ncm due to microphysics", &
2111 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2112 0 : k = k + 1
2113 :
2114 : case ('Ncm_cl')
2115 0 : stats_metadata%iNcm_cl = k
2116 :
2117 : call stat_assign( var_index=stats_metadata%iNcm_cl, var_name="Ncm_cl", &
2118 : var_description="Ncm_cl, Ncm budget: Ncm clipping term", &
2119 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2120 0 : k = k + 1
2121 :
2122 : case ('PSMLT')
2123 0 : stats_metadata%iPSMLT = k
2124 :
2125 : call stat_assign( var_index=stats_metadata%iPSMLT, var_name="PSMLT", &
2126 : var_description="PSMLT, Freezing of rain to form snow, +rsm, -rrm", &
2127 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2128 0 : k = k + 1
2129 :
2130 : case ('EVPMS')
2131 0 : stats_metadata%iEVPMS = k
2132 :
2133 : call stat_assign( var_index=stats_metadata%iEVPMS, var_name="EVPMS", &
2134 : var_description="EVPMS, Evaporation of melted snow, +rsm, -rvm", &
2135 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2136 0 : k = k + 1
2137 :
2138 : case ('PRACS')
2139 0 : stats_metadata%iPRACS = k
2140 :
2141 : call stat_assign( var_index=stats_metadata%iPRACS, var_name="PRACS", &
2142 : var_description="PRACS, Collection of rain by snow, +rsm, -rrm", &
2143 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2144 0 : k = k + 1
2145 :
2146 : case ('EVPMG')
2147 0 : stats_metadata%iEVPMG = k
2148 :
2149 : call stat_assign( var_index=stats_metadata%iEVPMG, var_name="EVPMG", &
2150 : var_description="EVPMG, Evaporation of melted graupel, +rgm, -rvm", &
2151 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2152 0 : k = k + 1
2153 :
2154 : case ('PRACG')
2155 0 : stats_metadata%iPRACG = k
2156 :
2157 : call stat_assign( var_index=stats_metadata%iPRACG, var_name="PRACG", &
2158 : var_description="PRACG, Negative of collection of rain by graupel, +rrm, -rgm", &
2159 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2160 0 : k = k + 1
2161 :
2162 : case ('PGMLT')
2163 0 : stats_metadata%iPGMLT = k
2164 :
2165 : call stat_assign( var_index=stats_metadata%iPGMLT, var_name="PGMLT", &
2166 : var_description="PGMLT, Negative of melting of graupel, +rgm, -rrm", &
2167 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2168 0 : k = k + 1
2169 :
2170 : case ('MNUCCC')
2171 0 : stats_metadata%iMNUCCC = k
2172 :
2173 : call stat_assign( var_index=stats_metadata%iMNUCCC, var_name="MNUCCC", &
2174 : var_description="MNUCCC, Contact freezing of cloud droplets, +rim, -rcm", &
2175 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2176 0 : k = k + 1
2177 :
2178 : case ('PSACWS')
2179 0 : stats_metadata%iPSACWS = k
2180 :
2181 : call stat_assign( var_index=stats_metadata%iPSACWS, var_name="PSACWS", &
2182 : var_description="PSACWS, Collection of cloud water by snow, +rsm, -rcm", &
2183 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2184 0 : k = k + 1
2185 :
2186 : case ('PSACWI')
2187 0 : stats_metadata%iPSACWI = k
2188 :
2189 : call stat_assign( var_index=stats_metadata%iPSACWI, var_name="PSACWI", &
2190 : var_description="PSACWI, Collection of cloud water by cloud ice, +rim, -rcm", &
2191 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2192 0 : k = k + 1
2193 :
2194 : case ('QMULTS')
2195 0 : stats_metadata%iQMULTS = k
2196 :
2197 : call stat_assign( var_index=stats_metadata%iQMULTS, var_name="QMULTS", &
2198 : var_description="QMULTS, Splintering from cloud droplets accreted onto snow, " &
2199 : // "+rim, -rcm", &
2200 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2201 0 : k = k + 1
2202 :
2203 : case ('QMULTG')
2204 0 : stats_metadata%iQMULTG = k
2205 :
2206 : call stat_assign( var_index=stats_metadata%iQMULTG, var_name="QMULTG", &
2207 : var_description="QMULTG, Splintering from droplets accreted onto graupel, " &
2208 : // "+rim, -rcm", &
2209 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2210 0 : k = k + 1
2211 :
2212 : case ('PSACWG')
2213 0 : stats_metadata%iPSACWG = k
2214 :
2215 : call stat_assign( var_index=stats_metadata%iPSACWG, var_name="PSACWG", &
2216 : var_description="PSACWG, Collection of cloud water by graupel, +rgm, -rcm", &
2217 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2218 0 : k = k + 1
2219 :
2220 : case ('PGSACW')
2221 0 : stats_metadata%iPGSACW = k
2222 :
2223 : call stat_assign( var_index=stats_metadata%iPGSACW, var_name="PGSACW", &
2224 : var_description="PGSACW, Reclassification of rimed snow as graupel, +rgm, -rcm", &
2225 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2226 0 : k = k + 1
2227 :
2228 : case ('PRD')
2229 0 : stats_metadata%iPRD = k
2230 :
2231 : call stat_assign( var_index=stats_metadata%iPRD, var_name="PRD", &
2232 : var_description="PRD, Depositional growth of cloud ice, +rim, -rvm", &
2233 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2234 0 : k = k + 1
2235 :
2236 : case ('PRCI')
2237 0 : stats_metadata%iPRCI = k
2238 :
2239 : call stat_assign( var_index=stats_metadata%iPRCI, var_name="PRCI", &
2240 : var_description="PRCI, Autoconversion of cloud ice to snow, +rsm, -rim", &
2241 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2242 0 : k = k + 1
2243 :
2244 : case ('PRAI')
2245 0 : stats_metadata%iPRAI = k
2246 :
2247 : call stat_assign( var_index=stats_metadata%iPRAI, var_name="PRAI", &
2248 : var_description="PRAI, Collection of cloud ice by snow, +rsm, -rim", &
2249 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2250 0 : k = k + 1
2251 :
2252 : case ('QMULTR')
2253 0 : stats_metadata%iQMULTR = k
2254 :
2255 : call stat_assign( var_index=stats_metadata%iQMULTR, var_name="QMULTR", &
2256 : var_description="QMULTR, Splintering from rain droplets accreted onto snow, " &
2257 : // "+rim, -rrm", &
2258 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2259 0 : k = k + 1
2260 :
2261 : case ('QMULTRG')
2262 0 : stats_metadata%iQMULTRG = k
2263 :
2264 : call stat_assign( var_index=stats_metadata%iQMULTRG, var_name="QMULTRG", &
2265 : var_description="QMULTRG, Splintering from rain droplets accreted onto graupel, " &
2266 : // "+rim, -rrm", &
2267 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2268 0 : k = k + 1
2269 :
2270 : case ('MNUCCD')
2271 0 : stats_metadata%iMNUCCD = k
2272 :
2273 : call stat_assign( var_index=stats_metadata%iMNUCCD, var_name="MNUCCD", &
2274 : var_description="MNUCCD, Freezing of aerosol, +rim, -rvm", &
2275 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2276 0 : k = k + 1
2277 :
2278 : case ('PRACI')
2279 0 : stats_metadata%iPRACI = k
2280 :
2281 : call stat_assign( var_index=stats_metadata%iPRACI, var_name="PRACI", &
2282 : var_description="PRACI, Collection of cloud ice by rain, +rgm, -rim", &
2283 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2284 0 : k = k + 1
2285 :
2286 : case ('PRACIS')
2287 0 : stats_metadata%iPRACIS = k
2288 :
2289 : call stat_assign( var_index=stats_metadata%iPRACIS, var_name="PRACIS", &
2290 : var_description="PRACIS, Collection of cloud ice by rain, +rsm, -rim", &
2291 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2292 0 : k = k + 1
2293 :
2294 : case ('EPRD')
2295 0 : stats_metadata%iEPRD = k
2296 :
2297 : call stat_assign( var_index=stats_metadata%iEPRD, var_name="EPRD", &
2298 : var_description="EPRD, Negative of sublimation of cloud ice, +rim, -rvm", &
2299 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2300 0 : k = k + 1
2301 :
2302 : case ('MNUCCR')
2303 0 : stats_metadata%iMNUCCR = k
2304 :
2305 : call stat_assign( var_index=stats_metadata%iMNUCCR, var_name="MNUCCR", &
2306 : var_description="MNUCCR, Contact freezing of rain droplets, +rgm, -rrm", &
2307 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2308 0 : k = k + 1
2309 :
2310 : case ('PIACR')
2311 0 : stats_metadata%iPIACR = k
2312 :
2313 : call stat_assign( var_index=stats_metadata%iPIACR, var_name="PIACR", &
2314 : var_description="PIACR, Collection of cloud ice by rain, +rgm, -rrm", &
2315 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2316 0 : k = k + 1
2317 :
2318 : case ('PIACRS')
2319 0 : stats_metadata%iPIACRS = k
2320 :
2321 : call stat_assign( var_index=stats_metadata%iPIACRS, var_name="PIACRS", &
2322 : var_description="PIACRS, Collection of cloud ice by rain, +rsm, -rrm", &
2323 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2324 0 : k = k + 1
2325 :
2326 : case ('PGRACS')
2327 0 : stats_metadata%iPGRACS = k
2328 :
2329 : call stat_assign( var_index=stats_metadata%iPGRACS, var_name="PGRACS", &
2330 : var_description="PGRACS, Collection of rain by snow, +rgm, -rrm", &
2331 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2332 0 : k = k + 1
2333 :
2334 : case ('PRDS')
2335 0 : stats_metadata%iPRDS = k
2336 :
2337 : call stat_assign( var_index=stats_metadata%iPRDS, var_name="PRDS", &
2338 : var_description="PRDS, Depositional growth of snow, +rsm, -rvm", &
2339 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2340 0 : k = k + 1
2341 :
2342 : case ('EPRDS')
2343 0 : stats_metadata%iEPRDS = k
2344 :
2345 : call stat_assign( var_index=stats_metadata%iEPRDS, var_name="EPRDS", &
2346 : var_description="EPRDS, Negative of sublimation of snow, +rsm, -rvm", &
2347 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2348 0 : k = k + 1
2349 :
2350 : case ('PSACR')
2351 0 : stats_metadata%iPSACR = k
2352 :
2353 : call stat_assign( var_index=stats_metadata%iPSACR, var_name="PSACR", &
2354 : var_description="PSACR, Collection of snow by rain, +rgm, -rsm", &
2355 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2356 0 : k = k + 1
2357 :
2358 : case ('PRDG')
2359 0 : stats_metadata%iPRDG = k
2360 :
2361 : call stat_assign( var_index=stats_metadata%iPRDG, var_name="PRDG", &
2362 : var_description="PRDG, Depositional growth of graupel, +rgm, -rvm", &
2363 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2364 0 : k = k + 1
2365 :
2366 : case ('EPRDG')
2367 0 : stats_metadata%iEPRDG = k
2368 :
2369 : call stat_assign( var_index=stats_metadata%iEPRDG, var_name="EPRDG", &
2370 : var_description="EPRDG, Negative of sublimation of graupel, +rgm, -rvm", &
2371 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2372 0 : k = k + 1
2373 :
2374 : case ('NGSTEN')
2375 0 : stats_metadata%iNGSTEN = k
2376 :
2377 : call stat_assign( var_index=stats_metadata%iNGSTEN, var_name="NGSTEN", &
2378 : var_description="NGSTEN, Graupel sedimentation tendency", var_units="(#/kg/s)", &
2379 0 : l_silhs=.true., grid_kind=stats_zt )
2380 0 : k = k + 1
2381 :
2382 : case ('NRSTEN')
2383 0 : stats_metadata%iNRSTEN = k
2384 :
2385 : call stat_assign( var_index=stats_metadata%iNRSTEN, var_name="NRSTEN", &
2386 : var_description="NRSTEN, Rain sedimentation tendency", var_units="(#/kg/s)", &
2387 0 : l_silhs=.true., grid_kind=stats_zt )
2388 0 : k = k + 1
2389 :
2390 : case ('NISTEN')
2391 0 : stats_metadata%iNISTEN = k
2392 :
2393 : call stat_assign( var_index=stats_metadata%iNISTEN, var_name="NISTEN", &
2394 : var_description="NISTEN, Cloud ice sedimentation tendency", var_units="(#/kg/s)", &
2395 0 : l_silhs=.true., grid_kind=stats_zt )
2396 0 : k = k + 1
2397 :
2398 : case ('NSSTEN')
2399 0 : stats_metadata%iNSSTEN = k
2400 :
2401 : call stat_assign( var_index=stats_metadata%iNSSTEN, var_name="NSSTEN", &
2402 : var_description="NSSTEN, Snow sedimentation tendency", var_units="(#/kg/s)", &
2403 0 : l_silhs=.true., grid_kind=stats_zt )
2404 0 : k = k + 1
2405 :
2406 : case ('NCSTEN')
2407 0 : stats_metadata%iNCSTEN = k
2408 :
2409 : call stat_assign( var_index=stats_metadata%iNCSTEN, var_name="NCSTEN", &
2410 : var_description="NCSTEN, Cloud water sedimentation tendency", &
2411 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2412 0 : k = k + 1
2413 :
2414 : case ('NPRC1')
2415 0 : stats_metadata%iNPRC1 = k
2416 :
2417 : call stat_assign( var_index=stats_metadata%iNPRC1, var_name="NPRC1", &
2418 : var_description="NPRC1, Change in Nrm due to autoconversion of droplets, +Nrm", &
2419 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2420 0 : k = k + 1
2421 :
2422 : case ('NRAGG')
2423 0 : stats_metadata%iNRAGG = k
2424 :
2425 : call stat_assign( var_index=stats_metadata%iNRAGG, var_name="NRAGG", &
2426 : var_description="NRAGG, Change in Nrm due to self-collection of raindrops, +Nrm", &
2427 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2428 0 : k = k + 1
2429 :
2430 : case ('NPRACG')
2431 0 : stats_metadata%iNPRACG = k
2432 :
2433 : call stat_assign( var_index=stats_metadata%iNPRACG, var_name="NPRACG", &
2434 : var_description="NPRACG, Collection of rainwater by graupel, -Nrm", &
2435 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2436 0 : k = k + 1
2437 :
2438 : case ('NSUBR')
2439 0 : stats_metadata%iNSUBR = k
2440 :
2441 : call stat_assign( var_index=stats_metadata%iNSUBR, var_name="NSUBR", &
2442 : var_description="NSUBR, Loss of Nrm by evaporation, +Nrm", var_units="(#/kg/s)", &
2443 0 : l_silhs=.true., grid_kind=stats_zt )
2444 0 : k = k + 1
2445 :
2446 : case ('NSMLTR')
2447 0 : stats_metadata%iNSMLTR = k
2448 :
2449 : call stat_assign( var_index=stats_metadata%iNSMLTR, var_name="NSMLTR", &
2450 : var_description="NSMLTR, Melting of snow to form rain, -Nrm", &
2451 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2452 0 : k = k + 1
2453 :
2454 : case ('NGMLTR')
2455 0 : stats_metadata%iNGMLTR = k
2456 :
2457 : call stat_assign( var_index=stats_metadata%iNGMLTR, var_name="NGMLTR", &
2458 : var_description="NGMLTR, Melting of graupel to form rain, -Nrm", &
2459 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2460 0 : k = k + 1
2461 :
2462 : case ('NPRACS')
2463 0 : stats_metadata%iNPRACS = k
2464 :
2465 : call stat_assign( var_index=stats_metadata%iNPRACS, var_name="NPRACS", &
2466 : var_description="NPRACS, Collection of rainwater by snow, -Nrm", &
2467 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2468 0 : k = k + 1
2469 :
2470 : case ('NNUCCR')
2471 0 : stats_metadata%iNNUCCR = k
2472 :
2473 : call stat_assign( var_index=stats_metadata%iNNUCCR, var_name="NNUCCR", &
2474 : var_description="NNUCCR, Contact freezing of rain, +Ngm, -Nrm", &
2475 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2476 0 : k = k + 1
2477 :
2478 : case ('NIACR')
2479 0 : stats_metadata%iNIACR = k
2480 :
2481 : call stat_assign( var_index=stats_metadata%iNIACR, var_name="NIACR", &
2482 : var_description="NIACR, Collection of cloud ice by rain, +Ngm, -Nrm, -Nim", &
2483 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2484 0 : k = k + 1
2485 :
2486 : case ('NIACRS')
2487 0 : stats_metadata%iNIACRS = k
2488 :
2489 : call stat_assign( var_index=stats_metadata%iNIACRS, var_name="NIACRS", &
2490 : var_description="NIACRS, Collection of cloud ice by rain, +Nsm, -Nrm, -Nim", &
2491 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2492 0 : k = k + 1
2493 :
2494 : case ('NGRACS')
2495 0 : stats_metadata%iNGRACS = k
2496 :
2497 : call stat_assign( var_index=stats_metadata%iNGRACS, var_name="NGRACS", &
2498 : var_description="NGRACS, Collection of rain by snow, +Ngm, -Nrm, -Nsm", &
2499 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2500 0 : k = k + 1
2501 :
2502 : case ('NSMLTS')
2503 0 : stats_metadata%iNSMLTS= k
2504 :
2505 : call stat_assign( var_index=stats_metadata%iNSMLTS, var_name="NSMLTS", &
2506 : var_description="NSMLTS, Melting of snow, +Nsm", var_units="(#/kg/s)", &
2507 0 : l_silhs=.true., grid_kind=stats_zt )
2508 0 : k = k + 1
2509 :
2510 : case ('NSAGG')
2511 0 : stats_metadata%iNSAGG= k
2512 :
2513 : call stat_assign( var_index=stats_metadata%iNSAGG, var_name="NSAGG", &
2514 : var_description="NSAGG, Self collection of snow, +Nsm", var_units="(#/kg/s)", &
2515 0 : l_silhs=.true., grid_kind=stats_zt )
2516 :
2517 0 : k = k + 1
2518 :
2519 : case ('NPRCI')
2520 0 : stats_metadata%iNPRCI= k
2521 :
2522 : call stat_assign( var_index=stats_metadata%iNPRCI, var_name="NPRCI", &
2523 : var_description="NPRCI, Autoconversion of cloud ice to snow, -Nim, +Nsm", &
2524 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2525 0 : k = k + 1
2526 :
2527 : case ('NSCNG')
2528 0 : stats_metadata%iNSCNG= k
2529 :
2530 : call stat_assign( var_index=stats_metadata%iNSCNG, var_name="NSCNG", &
2531 : var_description="NSCNG, Conversion of snow to graupel, +Ngm, -Nsm", &
2532 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2533 0 : k = k + 1
2534 :
2535 : case ('NSUBS')
2536 0 : stats_metadata%iNSUBS= k
2537 :
2538 : call stat_assign( var_index=stats_metadata%iNSUBS, var_name="NSUBS", &
2539 : var_description="NSUBS, Loss of snow due to sublimation, +Nsm", &
2540 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2541 0 : k = k + 1
2542 :
2543 : case ('PRC')
2544 0 : stats_metadata%iPRC= k
2545 :
2546 : call stat_assign( var_index=stats_metadata%iPRC, var_name="PRC", &
2547 : var_description="PRC, Autoconversion +rrm -rcm", var_units="(kg/kg/s)", &
2548 0 : l_silhs=.true., grid_kind=stats_zt )
2549 0 : k = k + 1
2550 :
2551 : case ('PRA')
2552 0 : stats_metadata%iPRA= k
2553 :
2554 : call stat_assign( var_index=stats_metadata%iPRA, var_name="PRA", &
2555 : var_description="PRA, Accretion +rrm -rcm", var_units="(kg/kg/s)", &
2556 0 : l_silhs=.true., grid_kind=stats_zt )
2557 0 : k = k + 1
2558 :
2559 : case ('PRE')
2560 0 : stats_metadata%iPRE= k
2561 :
2562 : call stat_assign( var_index=stats_metadata%iPRE, var_name="PRE", &
2563 : var_description="PRE, Evaporation of rain -rrm", var_units="(kg/kg/s)", &
2564 0 : l_silhs=.true., grid_kind=stats_zt )
2565 0 : k = k + 1
2566 :
2567 : case ('PCC')
2568 0 : stats_metadata%iPCC= k
2569 :
2570 : call stat_assign( var_index=stats_metadata%iPCC, var_name="PCC", &
2571 : var_description="PCC, Satuation adjustment -rvm +rcm", var_units="(kg/kg/s)", &
2572 0 : l_silhs=.true., grid_kind=stats_zt )
2573 0 : k = k + 1
2574 :
2575 : case ('NNUCCC')
2576 0 : stats_metadata%iNNUCCC= k
2577 :
2578 : call stat_assign( var_index=stats_metadata%iNNUCCC, var_name="NNUCCC", &
2579 : var_description="NNUCCC, Contact freezing of drops, -Ncm + Nim", &
2580 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2581 0 : k = k + 1
2582 :
2583 : case ('NPSACWS')
2584 0 : stats_metadata%iNPSACWS= k
2585 :
2586 : call stat_assign( var_index=stats_metadata%iNPSACWS, var_name="NPSACWS", &
2587 : var_description="NPSACWS, Droplet accretion by snow, -Ncm", var_units="(#/kg/s)", &
2588 0 : l_silhs=.true., grid_kind=stats_zt )
2589 0 : k = k + 1
2590 :
2591 : case ('NPRA')
2592 0 : stats_metadata%iNPRA= k
2593 :
2594 : call stat_assign( var_index=stats_metadata%iNPRA, var_name="NPRA", &
2595 : var_description="NPRA, Droplet accretion by rain, -Ncm", var_units="(#/kg/s)", &
2596 0 : l_silhs=.true., grid_kind=stats_zt )
2597 0 : k = k + 1
2598 :
2599 : case ('NPRC')
2600 0 : stats_metadata%iNPRC= k
2601 :
2602 : call stat_assign( var_index=stats_metadata%iNPRC, var_name="NPRC", &
2603 : var_description="NPRC, Autoconversion of cloud drops, -Ncm", &
2604 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2605 0 : k = k + 1
2606 :
2607 : case ('NPSACWI')
2608 0 : stats_metadata%iNPSACWI= k
2609 :
2610 : call stat_assign( var_index=stats_metadata%iNPSACWI, var_name="NPSACWI", &
2611 : var_description="NPSACWI, Droplet accretion by cloud ice, -Ncm", &
2612 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2613 0 : k = k + 1
2614 :
2615 : case ('NPSACWG')
2616 0 : stats_metadata%iNPSACWG= k
2617 :
2618 : call stat_assign( var_index=stats_metadata%iNPSACWG, var_name="NPSACWG", &
2619 : var_description="NPSACWG, Collection of cloud droplets by graupel, -Ncm", &
2620 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2621 0 : k = k + 1
2622 :
2623 : case ('NPRAI')
2624 0 : stats_metadata%iNPRAI= k
2625 :
2626 : call stat_assign( var_index=stats_metadata%iNPRAI, var_name="NPRAI", &
2627 : var_description="NPRAI, Accretion of cloud ice by snow, -Nim", &
2628 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2629 0 : k = k + 1
2630 :
2631 : case ('NMULTS')
2632 0 : stats_metadata%iNMULTS= k
2633 :
2634 : call stat_assign( var_index=stats_metadata%iNMULTS, var_name="NMULTS", &
2635 : var_description="NMULTS, Ice multiplication due to riming of cloud droplets " &
2636 : // "by snow, +Nim", &
2637 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2638 0 : k = k + 1
2639 :
2640 : case ('NMULTG')
2641 0 : stats_metadata%iNMULTG= k
2642 :
2643 : call stat_assign( var_index=stats_metadata%iNMULTG, var_name="NMULTG", &
2644 : var_description="NMULTG, Ice multiplication due to accretion of droplets " &
2645 : // "by graupel, +Nim", &
2646 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2647 0 : k = k + 1
2648 :
2649 : case ('NMULTR')
2650 0 : stats_metadata%iNMULTR= k
2651 :
2652 : call stat_assign( var_index=stats_metadata%iNMULTR, var_name="NMULTR", &
2653 : var_description="Ice multiplication due to riming of rain by snow, +Nim", &
2654 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2655 0 : k = k + 1
2656 :
2657 : case ('NMULTRG')
2658 0 : stats_metadata%iNMULTRG= k
2659 :
2660 : call stat_assign( var_index=stats_metadata%iNMULTRG, var_name="NMULTRG", &
2661 : var_description="NMULTR, Ice multiplication due to accretion of rain by " &
2662 : // "graupel, +Nim", &
2663 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2664 0 : k = k + 1
2665 :
2666 : case ('NNUCCD')
2667 0 : stats_metadata%iNNUCCD= k
2668 :
2669 : call stat_assign( var_index=stats_metadata%iNNUCCD, var_name="NNUCCD", &
2670 : var_description="NNUCCD, Primary ice nucleation, freezing of aerosol, +Nim", &
2671 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2672 0 : k = k + 1
2673 :
2674 : case ('NSUBI')
2675 0 : stats_metadata%iNSUBI= k
2676 :
2677 : call stat_assign( var_index=stats_metadata%iNSUBI, var_name="NSUBI", &
2678 : var_description="NSUBI, Loss of ice due to sublimation, -Nim", &
2679 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2680 0 : k = k + 1
2681 :
2682 : case ('NGMLTG')
2683 0 : stats_metadata%iNGMLTG= k
2684 :
2685 : call stat_assign( var_index=stats_metadata%iNGMLTG, var_name="NGMLTG", &
2686 : var_description="NGMLTG, Loss of graupel due to melting, -Ngm", &
2687 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2688 0 : k = k + 1
2689 :
2690 : case ('NSUBG')
2691 0 : stats_metadata%iNSUBG= k
2692 :
2693 : call stat_assign( var_index=stats_metadata%iNSUBG, var_name="NSUBG", &
2694 : var_description="NSUBG, Loss of graupel due to sublimation, -Ngm", &
2695 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2696 0 : k = k + 1
2697 :
2698 : case ('NACT')
2699 0 : stats_metadata%iNACT= k
2700 :
2701 : call stat_assign( var_index=stats_metadata%iNACT, var_name="NACT", &
2702 : var_description="NACT, Cloud drop formation by aerosol activation, +Ncm", &
2703 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2704 0 : k = k + 1
2705 :
2706 : case ('SIZEFIX_NR')
2707 0 : stats_metadata%iSIZEFIX_NR= k
2708 :
2709 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NR, var_name="SIZEFIX_NR", &
2710 : var_description="SIZEFIX_NR, Adjust rain # conc. for large/small drops, +Nrm", &
2711 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2712 0 : k = k + 1
2713 :
2714 : case ('SIZEFIX_NC')
2715 0 : stats_metadata%iSIZEFIX_NC= k
2716 :
2717 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NC, var_name="SIZEFIX_NC", &
2718 : var_description="SIZEFIX_NC, Adjust cloud # conc. for large/small drops, +Ncm", &
2719 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2720 0 : k = k + 1
2721 :
2722 : case ('SIZEFIX_NI')
2723 0 : stats_metadata%iSIZEFIX_NI= k
2724 :
2725 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NI, var_name="SIZEFIX_NI", &
2726 : var_description="SIZEFIX_NI, Adjust ice # conc. for large/small drops, +Nim", &
2727 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2728 0 : k = k + 1
2729 :
2730 : case ('SIZEFIX_NS')
2731 0 : stats_metadata%iSIZEFIX_NS= k
2732 :
2733 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NS, var_name="SIZEFIX_NS", &
2734 : var_description="SIZEFIX_NS, Adjust snow # conc. for large/small drops, +Nsm", &
2735 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2736 0 : k = k + 1
2737 :
2738 : case ('SIZEFIX_NG')
2739 0 : stats_metadata%iSIZEFIX_NG= k
2740 :
2741 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NG, var_name="SIZEFIX_NG", &
2742 : var_description="SIZEFIX_NG, Adjust graupel # conc. for large/small drops,+Ngm",&
2743 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2744 0 : k = k + 1
2745 :
2746 : case ('NEGFIX_NR')
2747 0 : stats_metadata%iNEGFIX_NR= k
2748 :
2749 : call stat_assign( var_index=stats_metadata%iNEGFIX_NR, var_name="NEGFIX_NR", &
2750 : var_description="NEGFIX_NR, Removal of negative rain drop number conc., -Nrm", &
2751 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2752 0 : k = k + 1
2753 :
2754 : case ('NEGFIX_NC')
2755 0 : stats_metadata%iNEGFIX_NC= k
2756 :
2757 : call stat_assign( var_index=stats_metadata%iNEGFIX_NC, var_name="NEGFIX_NC", &
2758 : var_description="NEGFIX_NC, Removal of negative cloud drop number conc., -Ncm", &
2759 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2760 0 : k = k + 1
2761 :
2762 : case ('NEGFIX_NI')
2763 0 : stats_metadata%iNEGFIX_NI= k
2764 :
2765 : call stat_assign( var_index=stats_metadata%iNEGFIX_NI, var_name="NEGFIX_NI", &
2766 : var_description="NEGFIX_NI, Removal of negative ice number conc., -Nim", &
2767 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2768 0 : k = k + 1
2769 :
2770 : case ('NEGFIX_NS')
2771 0 : stats_metadata%iNEGFIX_NS= k
2772 :
2773 : call stat_assign( var_index=stats_metadata%iNEGFIX_NS, var_name="NEGFIX_NS", &
2774 : var_description="NEGFIX_NS, Removal of negative snow number conc, -Nsm", &
2775 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2776 0 : k = k + 1
2777 :
2778 : case ('NEGFIX_NG')
2779 0 : stats_metadata%iNEGFIX_NG= k
2780 :
2781 : call stat_assign( var_index=stats_metadata%iNEGFIX_NG, var_name="NEGFIX_NG", &
2782 : var_description="NEGFIX_NG, Removal of negative graupel number conc., -Ngm", &
2783 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2784 0 : k = k + 1
2785 :
2786 : case ('NIM_MORR_CL')
2787 0 : stats_metadata%iNIM_MORR_CL= k
2788 :
2789 : call stat_assign( var_index=stats_metadata%iNIM_MORR_CL, var_name="NIM_MORR_CL", &
2790 : var_description="NIM_MORR_CL, Clipping of large ice concentrations, -Nim", &
2791 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2792 0 : k = k + 1
2793 :
2794 : case ('QC_INST')
2795 0 : stats_metadata%iQC_INST= k
2796 :
2797 : call stat_assign( var_index=stats_metadata%iQC_INST, var_name="QC_INST", &
2798 : var_description="QC_INST, Change in mixing ratio due to instantaneous " &
2799 : // "processes +rcm", &
2800 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2801 0 : k = k + 1
2802 :
2803 : case ('QR_INST')
2804 0 : stats_metadata%iQR_INST= k
2805 :
2806 : call stat_assign( var_index=stats_metadata%iQR_INST, var_name="QR_INST", &
2807 : var_description="QR_INST, Change in mixing ratio from instantaneous processes, +rrm",&
2808 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2809 0 : k = k + 1
2810 :
2811 : case ('QI_INST')
2812 0 : stats_metadata%iQI_INST= k
2813 :
2814 : call stat_assign( var_index=stats_metadata%iQI_INST, var_name="QI_INST", &
2815 : var_description="QI_INST, Change in mixing ratio from instantaneous processes +rim",&
2816 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2817 0 : k = k + 1
2818 :
2819 : case ('QS_INST')
2820 0 : stats_metadata%iQS_INST= k
2821 :
2822 : call stat_assign( var_index=stats_metadata%iQS_INST, var_name="QS_INST", &
2823 : var_description="QS_INST, Change in mixing ratio from instantaneous processes +rsm",&
2824 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2825 0 : k = k + 1
2826 :
2827 : case ('QG_INST')
2828 0 : stats_metadata%iQG_INST= k
2829 :
2830 : call stat_assign( var_index=stats_metadata%iQG_INST, var_name="QG_INST", &
2831 : var_description="QG_INST, Change in mixing ratio from instantaneous processes +rgm",&
2832 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2833 0 : k = k + 1
2834 :
2835 : case ('NC_INST')
2836 0 : stats_metadata%iNC_INST= k
2837 :
2838 : call stat_assign( var_index=stats_metadata%iNC_INST, var_name="NC_INST", &
2839 : var_description="NC_INST, Change in # conc. from instantaneous processes +Ncm", &
2840 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2841 0 : k = k + 1
2842 :
2843 : case ('NR_INST')
2844 0 : stats_metadata%iNR_INST= k
2845 :
2846 : call stat_assign( var_index=stats_metadata%iNR_INST, var_name="NR_INST", &
2847 : var_description="NR_INST, Change in # conc. from instantaneous processes +Nrm", &
2848 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2849 0 : k = k + 1
2850 :
2851 : case ('NI_INST')
2852 0 : stats_metadata%iNI_INST= k
2853 :
2854 : call stat_assign( var_index=stats_metadata%iNI_INST, var_name="NI_INST", &
2855 : var_description="NI_INST, Change in # conc. from instantaneous processes +Nim", &
2856 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2857 0 : k = k + 1
2858 :
2859 : case ('NS_INST')
2860 0 : stats_metadata%iNS_INST= k
2861 :
2862 : call stat_assign( var_index=stats_metadata%iNS_INST, var_name="NS_INST", &
2863 : var_description="NS_INST, Change in # conc. from instantaneous processes +Nsm", &
2864 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2865 0 : k = k + 1
2866 :
2867 : case ('NG_INST')
2868 0 : stats_metadata%iNG_INST= k
2869 :
2870 : call stat_assign( var_index=stats_metadata%iNG_INST, var_name="NG_INST", &
2871 : var_description="NG_INST, Change in # conc. from instantaneous processes +Ngm", &
2872 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2873 0 : k = k + 1
2874 :
2875 :
2876 : case ('T_in_K_mc')
2877 0 : stats_metadata%iT_in_K_mc= k
2878 :
2879 : call stat_assign( var_index=stats_metadata%iT_in_K_mc, var_name="T_in_K_mc", &
2880 : var_description="T_in_K_mc, Temperature tendency from Morrison microphysics", &
2881 0 : var_units="(K/s)", l_silhs=.true., grid_kind=stats_zt )
2882 0 : k = k + 1
2883 :
2884 : case ('w_KK_evap_covar_zt')
2885 0 : stats_metadata%iw_KK_evap_covar_zt = k
2886 :
2887 : call stat_assign( var_index=stats_metadata%iw_KK_evap_covar_zt, var_name="w_KK_evap_covar_zt", &
2888 : var_description="w_KK_evap_covar_zt, Covariance of w and KK evaporation rate", &
2889 0 : var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
2890 0 : k = k + 1
2891 :
2892 : case ('rt_KK_evap_covar_zt')
2893 0 : stats_metadata%irt_KK_evap_covar_zt = k
2894 :
2895 : call stat_assign( var_index=stats_metadata%irt_KK_evap_covar_zt, var_name="rt_KK_evap_covar_zt", &
2896 : var_description="rt_KK_evap_covar_zt, Covariance of r_t and KK evaporation rate", &
2897 0 : var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
2898 0 : k = k + 1
2899 :
2900 : case ('thl_KK_evap_covar_zt')
2901 0 : stats_metadata%ithl_KK_evap_covar_zt = k
2902 :
2903 : call stat_assign( var_index=stats_metadata%ithl_KK_evap_covar_zt, var_name="thl_KK_evap_covar_zt", &
2904 : var_description="thl_KK_evap_covar_zt, Covariance of theta_l and KK " &
2905 : // "evaporation rate", &
2906 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2907 0 : k = k + 1
2908 :
2909 : case ('w_KK_auto_covar_zt')
2910 0 : stats_metadata%iw_KK_auto_covar_zt = k
2911 :
2912 : call stat_assign( var_index=stats_metadata%iw_KK_auto_covar_zt, var_name="w_KK_auto_covar_zt", &
2913 : var_description="w_KK_auto_covar_zt, Covariance of w and KK autoconversion rate", &
2914 0 : var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
2915 0 : k = k + 1
2916 :
2917 : case ('rt_KK_auto_covar_zt')
2918 0 : stats_metadata%irt_KK_auto_covar_zt = k
2919 :
2920 : call stat_assign( var_index=stats_metadata%irt_KK_auto_covar_zt, var_name="rt_KK_auto_covar_zt", &
2921 : var_description="rt_KK_auto_covar_zt, Covariance of r_t and KK autoconversion rate",&
2922 0 : var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
2923 0 : k = k + 1
2924 :
2925 : case ('thl_KK_auto_covar_zt')
2926 0 : stats_metadata%ithl_KK_auto_covar_zt = k
2927 :
2928 : call stat_assign( var_index=stats_metadata%ithl_KK_auto_covar_zt, var_name="thl_KK_auto_covar_zt", &
2929 : var_description="thl_KK_auto_covar_zt, Covariance of theta_l and " &
2930 : // "KK autoconversion rate", &
2931 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2932 0 : k = k + 1
2933 :
2934 : case ('w_KK_accr_covar_zt')
2935 0 : stats_metadata%iw_KK_accr_covar_zt = k
2936 :
2937 : call stat_assign( var_index=stats_metadata%iw_KK_accr_covar_zt, var_name="w_KK_accr_covar_zt", &
2938 : var_description="w_KK_accr_covar_zt, Covariance of w and KK accretion rate", &
2939 : var_units="m*(kg/kg)/s^2", &
2940 0 : l_silhs=.false., grid_kind=stats_zt )
2941 0 : k = k + 1
2942 :
2943 : case ('rt_KK_accr_covar_zt')
2944 0 : stats_metadata%irt_KK_accr_covar_zt = k
2945 :
2946 : call stat_assign( var_index=stats_metadata%irt_KK_accr_covar_zt, var_name="rt_KK_accr_covar_zt", &
2947 : var_description="rt_KK_accr_covar_zt, Covariance of r_t and KK accretion rate", &
2948 : var_units="(kg/kg)^2/s", &
2949 0 : l_silhs=.false., grid_kind=stats_zt )
2950 0 : k = k + 1
2951 :
2952 : case ('thl_KK_accr_covar_zt')
2953 0 : stats_metadata%ithl_KK_accr_covar_zt = k
2954 :
2955 : call stat_assign( var_index=stats_metadata%ithl_KK_accr_covar_zt, var_name="thl_KK_accr_covar_zt", &
2956 : var_description="thl_KK_accr_covar_zt, Covariance of theta_l and KK accretion rate",&
2957 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2958 0 : k = k + 1
2959 :
2960 : case ('rr_KK_mvr_covar_zt')
2961 0 : stats_metadata%irr_KK_mvr_covar_zt = k
2962 :
2963 : call stat_assign( var_index=stats_metadata%irr_KK_mvr_covar_zt, var_name="rr_KK_mvr_covar_zt", &
2964 : var_description="rr_KK_mvr_covar_zt, Covariance of r_r and KK rain drop mean " &
2965 : // "volume radius", &
2966 0 : var_units="(kg/kg)m", l_silhs=.false., grid_kind=stats_zt )
2967 0 : k = k + 1
2968 :
2969 : case ('Nr_KK_mvr_covar_zt')
2970 0 : stats_metadata%iNr_KK_mvr_covar_zt = k
2971 :
2972 : call stat_assign( var_index=stats_metadata%iNr_KK_mvr_covar_zt, var_name="Nr_KK_mvr_covar_zt", &
2973 : var_description="Nr_KK_mvr_covar_zt, Covariance of N_r and KK rain drop mean " &
2974 : // "volume radius", &
2975 0 : var_units="(num/kg)m", l_silhs=.false., grid_kind=stats_zt )
2976 0 : k = k + 1
2977 :
2978 : case ('KK_mvr_variance_zt')
2979 0 : stats_metadata%iKK_mvr_variance_zt = k
2980 :
2981 : call stat_assign( var_index=stats_metadata%iKK_mvr_variance_zt, var_name="KK_mvr_variance_zt", &
2982 : var_description="KK_mvr_variance_zt, Variance of KK rain drop mean volume radius", &
2983 0 : var_units="m^2", l_silhs=.false., grid_kind=stats_zt )
2984 0 : k = k + 1
2985 :
2986 : case ('vm_bt')
2987 0 : stats_metadata%ivm_bt = k
2988 :
2989 : call stat_assign( var_index=stats_metadata%ivm_bt, var_name="vm_bt", &
2990 : var_description="vm_bt, vm budget: vm time tendency", var_units="m s^{-2}", &
2991 0 : l_silhs=.false., grid_kind=stats_zt )
2992 0 : k = k + 1
2993 :
2994 : case ('vm_ma')
2995 0 : stats_metadata%ivm_ma = k
2996 : call stat_assign( var_index=stats_metadata%ivm_ma, var_name="vm_ma", &
2997 : var_description="vm_ma, vm budget: vm vertical mean advection", &
2998 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
2999 0 : k = k + 1
3000 :
3001 : case ('vm_gf')
3002 0 : stats_metadata%ivm_gf = k
3003 :
3004 : call stat_assign( var_index=stats_metadata%ivm_gf, var_name="vm_gf", &
3005 : var_description="vm_gf, vm budget: vm geostrophic forcing", &
3006 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3007 0 : k = k + 1
3008 :
3009 : case ('vm_cf')
3010 0 : stats_metadata%ivm_cf = k
3011 :
3012 : call stat_assign( var_index=stats_metadata%ivm_cf, var_name="vm_cf", &
3013 : var_description="vm_cf, vm budget: vm coriolis forcing", var_units="m s^{-2}", &
3014 0 : l_silhs=.false., grid_kind=stats_zt )
3015 0 : k = k + 1
3016 :
3017 : case ('vm_ta')
3018 0 : stats_metadata%ivm_ta = k
3019 :
3020 : call stat_assign( var_index=stats_metadata%ivm_ta, var_name="vm_ta", &
3021 : var_description="vm_ta, vm budget: vm turbulent transport", &
3022 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3023 0 : k = k + 1
3024 :
3025 : case ('vm_f')
3026 0 : stats_metadata%ivm_f = k
3027 : call stat_assign( var_index=stats_metadata%ivm_f, var_name="vm_f", &
3028 : var_description="vm_f, vm budget: vm forcing", var_units="m s^{-2}", &
3029 0 : l_silhs=.false., grid_kind=stats_zt )
3030 0 : k = k + 1
3031 :
3032 : case ('vm_sdmp')
3033 0 : stats_metadata%ivm_sdmp = k
3034 : call stat_assign( var_index=stats_metadata%ivm_sdmp, var_name="vm_sdmp", &
3035 : var_description="vm_sdmp, vm budget: vm sponge damping", var_units="m s^{-2}", &
3036 0 : l_silhs=.false., grid_kind=stats_zt )
3037 0 : k = k + 1
3038 :
3039 : case ('vm_ndg')
3040 0 : stats_metadata%ivm_ndg = k
3041 : call stat_assign( var_index=stats_metadata%ivm_ndg, var_name="vm_ndg", &
3042 : var_description="vm_ndg, vm budget: vm nudging", var_units="m s^{-2}", &
3043 0 : l_silhs=.false., grid_kind=stats_zt )
3044 0 : k = k + 1
3045 :
3046 : case ('vm_mfl')
3047 0 : stats_metadata%ivm_mfl = k
3048 : call stat_assign( var_index=stats_metadata%ivm_mfl, var_name="vm_mfl", &
3049 : var_description="vm_mfl, vm budget: vm monotonic flux limiter", &
3050 : var_units="m s^{-2}", &
3051 0 : l_silhs=.false., grid_kind=stats_zt )
3052 0 : k = k + 1
3053 :
3054 : case ('um_bt')
3055 0 : stats_metadata%ium_bt = k
3056 :
3057 : call stat_assign( var_index=stats_metadata%ium_bt, var_name="um_bt", &
3058 : var_description="um_bt, um budget: um time tendency", var_units="m s^{-2}", &
3059 0 : l_silhs=.false., grid_kind=stats_zt )
3060 0 : k = k + 1
3061 :
3062 : case ('um_ma')
3063 0 : stats_metadata%ium_ma = k
3064 :
3065 : call stat_assign( var_index=stats_metadata%ium_ma, var_name="um_ma", &
3066 : var_description="um_ma, um budget: um vertical mean advection", &
3067 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3068 0 : k = k + 1
3069 :
3070 : case ('um_gf')
3071 0 : stats_metadata%ium_gf = k
3072 : call stat_assign( var_index=stats_metadata%ium_gf, var_name="um_gf", &
3073 : var_description="um_gf, um budget: um geostrophic forcing", &
3074 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3075 0 : k = k + 1
3076 :
3077 : case ('um_cf')
3078 0 : stats_metadata%ium_cf = k
3079 : call stat_assign( var_index=stats_metadata%ium_cf, var_name="um_cf", &
3080 : var_description="um_cf, um budget: um coriolis forcing", var_units="m s^{-2}", &
3081 0 : l_silhs=.false., grid_kind=stats_zt )
3082 0 : k = k + 1
3083 :
3084 : case ('um_ta')
3085 0 : stats_metadata%ium_ta = k
3086 : call stat_assign( var_index=stats_metadata%ium_ta, var_name="um_ta", &
3087 : var_description="um_ta, um budget: um turbulent advection", &
3088 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3089 0 : k = k + 1
3090 :
3091 : case ('um_f')
3092 0 : stats_metadata%ium_f = k
3093 : call stat_assign( var_index=stats_metadata%ium_f, var_name="um_f", &
3094 : var_description="um_f, um budget: um forcing", var_units="m s^{-2}", &
3095 0 : l_silhs=.false., grid_kind=stats_zt )
3096 0 : k = k + 1
3097 :
3098 : case ('um_sdmp')
3099 0 : stats_metadata%ium_sdmp = k
3100 : call stat_assign( var_index=stats_metadata%ium_sdmp, var_name="um_sdmp", &
3101 : var_description="um_sdmp, um budget: um sponge damping", var_units="m s^{-2}", &
3102 0 : l_silhs=.false., grid_kind=stats_zt )
3103 0 : k = k + 1
3104 :
3105 : case ('um_ndg')
3106 0 : stats_metadata%ium_ndg = k
3107 : call stat_assign( var_index=stats_metadata%ium_ndg, var_name="um_ndg", &
3108 : var_description="um_ndg, um budget: um nudging", var_units="m s^{-2}", &
3109 0 : l_silhs=.false., grid_kind=stats_zt )
3110 0 : k = k + 1
3111 :
3112 : case ('um_mfl')
3113 0 : stats_metadata%ium_mfl = k
3114 : call stat_assign( var_index=stats_metadata%ium_mfl, var_name="um_mfl", &
3115 : var_description="um_mfl, um budget: um monotonic flux limiter", &
3116 : var_units="m s^{-2}",&
3117 0 : l_silhs=.false., grid_kind=stats_zt )
3118 0 : k = k + 1
3119 :
3120 : case ('mixt_frac')
3121 0 : stats_metadata%imixt_frac = k
3122 : call stat_assign( var_index=stats_metadata%imixt_frac, var_name="mixt_frac", &
3123 : var_description="mixt_frac, pdf parameter: mixture fraction", var_units="count", &
3124 0 : l_silhs=.false., grid_kind=stats_zt )
3125 0 : k = k + 1
3126 :
3127 : case ('w_1')
3128 0 : stats_metadata%iw_1 = k
3129 : call stat_assign( var_index=stats_metadata%iw_1, var_name="w_1", &
3130 : var_description="w_1, pdf parameter: mean w of component 1", var_units="m/s", &
3131 0 : l_silhs=.false., grid_kind=stats_zt )
3132 :
3133 0 : k = k + 1
3134 :
3135 : case ('w_2')
3136 0 : stats_metadata%iw_2 = k
3137 :
3138 : call stat_assign( var_index=stats_metadata%iw_2, var_name="w_2", &
3139 : var_description="w_2, pdf paramete: mean w of component 2", var_units="m/s", &
3140 0 : l_silhs=.false., grid_kind=stats_zt )
3141 0 : k = k + 1
3142 :
3143 : case ('varnce_w_1')
3144 0 : stats_metadata%ivarnce_w_1 = k
3145 : call stat_assign( var_index=stats_metadata%ivarnce_w_1, var_name="varnce_w_1", &
3146 : var_description="varnce_w_1, pdf parameter: w variance of component 1", &
3147 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3148 :
3149 0 : k = k + 1
3150 :
3151 : case ('varnce_w_2')
3152 0 : stats_metadata%ivarnce_w_2 = k
3153 :
3154 : call stat_assign( var_index=stats_metadata%ivarnce_w_2, var_name="varnce_w_2", &
3155 : var_description="varnce_w_2, pdf parameter: w variance of component 2", &
3156 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3157 0 : k = k + 1
3158 :
3159 : case ('thl_1')
3160 0 : stats_metadata%ithl_1 = k
3161 :
3162 : call stat_assign( var_index=stats_metadata%ithl_1, var_name="thl_1", &
3163 : var_description="thl_1, pdf parameter: mean thl of component 1", var_units="K", &
3164 0 : l_silhs=.false., grid_kind=stats_zt )
3165 :
3166 0 : k = k + 1
3167 :
3168 : case ('thl_2')
3169 0 : stats_metadata%ithl_2 = k
3170 :
3171 : call stat_assign( var_index=stats_metadata%ithl_2, var_name="thl_2", &
3172 : var_description="thl_2, pdf parameter: mean thl of component 2", var_units="K", &
3173 0 : l_silhs=.false., grid_kind=stats_zt )
3174 0 : k = k + 1
3175 :
3176 : case ('varnce_thl_1')
3177 0 : stats_metadata%ivarnce_thl_1 = k
3178 :
3179 : call stat_assign( var_index=stats_metadata%ivarnce_thl_1, var_name="varnce_thl_1", &
3180 : var_description="varnce_thl_1, pdf parameter: thl variance of component 1", &
3181 : var_units="K^2", &
3182 0 : l_silhs=.false., grid_kind=stats_zt )
3183 :
3184 0 : k = k + 1
3185 :
3186 : case ('varnce_thl_2')
3187 0 : stats_metadata%ivarnce_thl_2 = k
3188 : call stat_assign( var_index=stats_metadata%ivarnce_thl_2, var_name="varnce_thl_2", &
3189 : var_description="varnce_thl_2, pdf parameter: thl variance of component 2", &
3190 : var_units="K^2", &
3191 0 : l_silhs=.false., grid_kind=stats_zt )
3192 :
3193 0 : k = k + 1
3194 :
3195 : case ('rt_1')
3196 0 : stats_metadata%irt_1 = k
3197 : call stat_assign( var_index=stats_metadata%irt_1, var_name="rt_1", &
3198 : var_description="rt_1, pdf parameter: mean rt of component 1", var_units="kg/kg", &
3199 0 : l_silhs=.false., grid_kind=stats_zt )
3200 :
3201 0 : k = k + 1
3202 :
3203 : case ('rt_2')
3204 0 : stats_metadata%irt_2 = k
3205 :
3206 : call stat_assign( var_index=stats_metadata%irt_2, var_name="rt_2", &
3207 : var_description="rt_2, pdf parameter: mean rt of component 2", var_units="kg/kg", &
3208 0 : l_silhs=.false., grid_kind=stats_zt )
3209 0 : k = k + 1
3210 :
3211 : case ('varnce_rt_1')
3212 0 : stats_metadata%ivarnce_rt_1 = k
3213 : call stat_assign( var_index=stats_metadata%ivarnce_rt_1, var_name="varnce_rt_1", &
3214 : var_description="varnce_rt_1, pdf parameter: rt variance of component 1", &
3215 0 : var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
3216 0 : k = k + 1
3217 :
3218 : case ('varnce_rt_2')
3219 0 : stats_metadata%ivarnce_rt_2 = k
3220 :
3221 : call stat_assign( var_index=stats_metadata%ivarnce_rt_2, var_name="varnce_rt_2", &
3222 : var_description="varnce_rt_2, pdf parameter: rt variance of component 2", &
3223 0 : var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
3224 0 : k = k + 1
3225 :
3226 : case ('rc_1')
3227 0 : stats_metadata%irc_1 = k
3228 :
3229 : call stat_assign( var_index=stats_metadata%irc_1, var_name="rc_1", &
3230 : var_description="rc_1, pdf parameter: mean rc of component 1", var_units="kg/kg", &
3231 0 : l_silhs=.false., grid_kind=stats_zt )
3232 0 : k = k + 1
3233 :
3234 : case ('rc_2')
3235 0 : stats_metadata%irc_2 = k
3236 :
3237 : call stat_assign( var_index=stats_metadata%irc_2, var_name="rc_2", &
3238 : var_description="rc_2, pdf parameter: mean rc of component 2", var_units="kg/kg", &
3239 0 : l_silhs=.false., grid_kind=stats_zt )
3240 0 : k = k + 1
3241 :
3242 : case ('rsatl_1')
3243 0 : stats_metadata%irsatl_1 = k
3244 :
3245 : call stat_assign( var_index=stats_metadata%irsatl_1, var_name="rsatl_1", &
3246 : var_description="rsatl_1, pdf parameter: sat mix rat based on tl1", &
3247 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3248 0 : k = k + 1
3249 :
3250 : case ('rsatl_2')
3251 0 : stats_metadata%irsatl_2 = k
3252 :
3253 : call stat_assign( var_index=stats_metadata%irsatl_2, var_name="rsatl_2", &
3254 : var_description="rsatl_2, pdf parameter: sat mix rat based on tl2", &
3255 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3256 0 : k = k + 1
3257 :
3258 : case ('cloud_frac_1')
3259 0 : stats_metadata%icloud_frac_1 = k
3260 : call stat_assign( var_index=stats_metadata%icloud_frac_1, var_name="cloud_frac_1", &
3261 : var_description="cloud_frac_1, pdf parameter cloud_frac_1", var_units="-", &
3262 0 : l_silhs=.false., grid_kind=stats_zt )
3263 0 : k = k + 1
3264 :
3265 : case ('cloud_frac_2')
3266 0 : stats_metadata%icloud_frac_2 = k
3267 :
3268 : call stat_assign( var_index=stats_metadata%icloud_frac_2, var_name="cloud_frac_2", &
3269 : var_description="cloud_frac_2, pdf parameter cloud_frac_2", var_units="-", &
3270 0 : l_silhs=.false., grid_kind=stats_zt )
3271 0 : k = k + 1
3272 :
3273 : case ('chi_1')
3274 0 : stats_metadata%ichi_1 = k
3275 :
3276 : call stat_assign( var_index=stats_metadata%ichi_1, var_name="chi_1", &
3277 : var_description="chi_1, pdf parameter: Mellor's s (extended liq) for component 1", &
3278 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3279 0 : k = k + 1
3280 :
3281 : case ('chi_2')
3282 0 : stats_metadata%ichi_2 = k
3283 :
3284 : call stat_assign( var_index=stats_metadata%ichi_2, var_name="chi_2", &
3285 : var_description="chi_2, pdf parameter: Mellor's s (extended liq) for component 2", &
3286 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3287 0 : k = k + 1
3288 :
3289 : case ('stdev_chi_1')
3290 0 : stats_metadata%istdev_chi_1 = k
3291 :
3292 : call stat_assign( var_index=stats_metadata%istdev_chi_1, var_name="stdev_chi_1", &
3293 : var_description="stdev_chi_1, pdf parameter: Std dev of chi_1", var_units="kg/kg", &
3294 0 : l_silhs=.false., grid_kind=stats_zt )
3295 0 : k = k + 1
3296 :
3297 : case ('stdev_chi_2')
3298 0 : stats_metadata%istdev_chi_2 = k
3299 :
3300 : call stat_assign( var_index=stats_metadata%istdev_chi_2, var_name="stdev_chi_2", &
3301 : var_description="stdev_chi_2, pdf parameter: Std dev of chi_2", var_units="kg/kg", &
3302 0 : l_silhs=.false., grid_kind=stats_zt )
3303 0 : k = k + 1
3304 :
3305 : case ('chip2')
3306 0 : stats_metadata%ichip2 = k
3307 : call stat_assign( var_index=stats_metadata%ichip2, var_name="chip2", &
3308 : var_description="chip2, Variance of chi(s) (overall)", var_units="(kg/kg)^2", &
3309 0 : l_silhs=.false., grid_kind=stats_zt )
3310 0 : k = k + 1
3311 :
3312 : case ('stdev_eta_1')
3313 0 : stats_metadata%istdev_eta_1 = k
3314 :
3315 : call stat_assign( var_index=stats_metadata%istdev_eta_1, var_name="stdev_eta_1", &
3316 : var_description="stdev_eta_1, Standard dev. of eta(t) (1st PDF component)", &
3317 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3318 0 : k = k + 1
3319 :
3320 : case ('stdev_eta_2')
3321 0 : stats_metadata%istdev_eta_2 = k
3322 :
3323 : call stat_assign( var_index=stats_metadata%istdev_eta_2, var_name="stdev_eta_2", &
3324 : var_description="stdev_eta_2, Standard dev. of eta(t) (2nd PDF component)", &
3325 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3326 0 : k = k + 1
3327 :
3328 : case ('covar_chi_eta_1')
3329 0 : stats_metadata%icovar_chi_eta_1 = k
3330 :
3331 : call stat_assign( var_index=stats_metadata%icovar_chi_eta_1, var_name="covar_chi_eta_1", &
3332 : var_description="covar_chi_eta_1, Covariance of chi(s) and eta(t) " &
3333 : // "(1st PDF component)", &
3334 0 : var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
3335 0 : k = k + 1
3336 :
3337 : case ('covar_chi_eta_2')
3338 0 : stats_metadata%icovar_chi_eta_2 = k
3339 :
3340 : call stat_assign( var_index=stats_metadata%icovar_chi_eta_2, var_name="covar_chi_eta_2", &
3341 : var_description="covar_chi_eta_2, Covariance of chi(s) and eta(t) " &
3342 : // "(2nd PDF component)", &
3343 0 : var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
3344 0 : k = k + 1
3345 :
3346 : case ('corr_w_chi_1')
3347 0 : stats_metadata%icorr_w_chi_1 = k
3348 :
3349 : call stat_assign( var_index=stats_metadata%icorr_w_chi_1, var_name="corr_w_chi_1", &
3350 : var_description="corr_w_chi_1, Correlation of w and chi (s)" &
3351 : // " (1st PDF component)", var_units="-", &
3352 0 : l_silhs=.false., grid_kind=stats_zt )
3353 0 : k = k + 1
3354 :
3355 : case ('corr_w_chi_2')
3356 0 : stats_metadata%icorr_w_chi_2 = k
3357 :
3358 : call stat_assign( var_index=stats_metadata%icorr_w_chi_2, var_name="corr_w_chi_2", &
3359 : var_description="corr_w_chi_2, Correlation of w and chi (s)" &
3360 : // " (2nd PDF component)", var_units="-", &
3361 0 : l_silhs=.false., grid_kind=stats_zt )
3362 0 : k = k + 1
3363 :
3364 : case ('corr_w_eta_1')
3365 0 : stats_metadata%icorr_w_eta_1 = k
3366 :
3367 : call stat_assign( var_index=stats_metadata%icorr_w_eta_1, var_name="corr_w_eta_1", &
3368 : var_description="corr_w_eta_1, Correlation of w and eta (t)" &
3369 : // " (1st PDF component)", var_units="-", &
3370 0 : l_silhs=.false., grid_kind=stats_zt )
3371 0 : k = k + 1
3372 :
3373 : case ('corr_w_eta_2')
3374 0 : stats_metadata%icorr_w_eta_2 = k
3375 :
3376 : call stat_assign( var_index=stats_metadata%icorr_w_eta_2, var_name="corr_w_eta_2", &
3377 : var_description="corr_w_eta_2, Correlation of w and eta (t)" &
3378 : // " (2nd PDF component)", var_units="-", &
3379 0 : l_silhs=.false., grid_kind=stats_zt )
3380 0 : k = k + 1
3381 :
3382 : case ('corr_chi_eta_1')
3383 0 : stats_metadata%icorr_chi_eta_1 = k
3384 :
3385 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_1, &
3386 : var_name="corr_chi_eta_1", &
3387 : var_description="corr_chi_eta_1, Correlation of chi (s) and" &
3388 : // " eta (t) (1st PDF component)", &
3389 : var_units="-", &
3390 0 : l_silhs=.false., grid_kind=stats_zt )
3391 0 : k = k + 1
3392 :
3393 : case ('corr_chi_eta_2')
3394 0 : stats_metadata%icorr_chi_eta_2 = k
3395 :
3396 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_2, &
3397 : var_name="corr_chi_eta_2", &
3398 : var_description="corr_chi_eta_2, Correlation of chi (s) and" &
3399 : // " eta (t) (2nd PDF component)", &
3400 : var_units="-", &
3401 0 : l_silhs=.false., grid_kind=stats_zt )
3402 0 : k = k + 1
3403 :
3404 : case ('corr_w_rt_1')
3405 0 : stats_metadata%icorr_w_rt_1 = k
3406 :
3407 : call stat_assign( var_index=stats_metadata%icorr_w_rt_1, var_name="corr_w_rt_1", &
3408 : var_description="corr_w_rt_1, Correlation of w and rt" &
3409 : // " (1st PDF component)", var_units="-", &
3410 0 : l_silhs=.false., grid_kind=stats_zt )
3411 0 : k = k + 1
3412 :
3413 : case ('corr_w_rt_2')
3414 0 : stats_metadata%icorr_w_rt_2 = k
3415 :
3416 : call stat_assign( var_index=stats_metadata%icorr_w_rt_2, var_name="corr_w_rt_2", &
3417 : var_description="corr_w_rt_2, Correlation of w and rt" &
3418 : // " (2nd PDF component)", var_units="-", &
3419 0 : l_silhs=.false., grid_kind=stats_zt )
3420 0 : k = k + 1
3421 :
3422 : case ('corr_w_thl_1')
3423 0 : stats_metadata%icorr_w_thl_1 = k
3424 :
3425 : call stat_assign( var_index=stats_metadata%icorr_w_thl_1, var_name="corr_w_thl_1", &
3426 : var_description="corr_w_thl_1, Correlation of w and thl" &
3427 : // " (1st PDF component)", var_units="-", &
3428 0 : l_silhs=.false., grid_kind=stats_zt )
3429 0 : k = k + 1
3430 :
3431 : case ('corr_w_thl_2')
3432 0 : stats_metadata%icorr_w_thl_2 = k
3433 :
3434 : call stat_assign( var_index=stats_metadata%icorr_w_thl_2, var_name="corr_w_thl_2", &
3435 : var_description="corr_w_thl_2, Correlation of w and thl" &
3436 : // " (2nd PDF component)", var_units="-", &
3437 0 : l_silhs=.false., grid_kind=stats_zt )
3438 0 : k = k + 1
3439 :
3440 : case ('corr_rt_thl_1')
3441 0 : stats_metadata%icorr_rt_thl_1 = k
3442 :
3443 : call stat_assign( var_index=stats_metadata%icorr_rt_thl_1, var_name="corr_rt_thl_1", &
3444 : var_description="corr_rt_thl_1, Correlation of rt and thl" &
3445 : // " (1st PDF component)", var_units="-", &
3446 0 : l_silhs=.false., grid_kind=stats_zt )
3447 0 : k = k + 1
3448 :
3449 : case ('corr_rt_thl_2')
3450 0 : stats_metadata%icorr_rt_thl_2 = k
3451 :
3452 : call stat_assign( var_index=stats_metadata%icorr_rt_thl_2, var_name="corr_rt_thl_2", &
3453 : var_description="corr_rt_thl_2, Correlation of rt and thl" &
3454 : // " (2nd PDF component)", var_units="-", &
3455 0 : l_silhs=.false., grid_kind=stats_zt )
3456 0 : k = k + 1
3457 :
3458 : case ('crt_1')
3459 0 : stats_metadata%icrt_1 = k
3460 :
3461 : call stat_assign( var_index=stats_metadata%icrt_1, var_name="crt_1", &
3462 : var_description="crt_1, Coefficient on rt in chi/eta" &
3463 : // " equations (1st PDF comp.)", &
3464 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3465 0 : k = k + 1
3466 :
3467 : case ('crt_2')
3468 0 : stats_metadata%icrt_2 = k
3469 :
3470 : call stat_assign( var_index=stats_metadata%icrt_2, var_name="crt_2", &
3471 : var_description="crt_2, Coefficient on rt in chi/eta" &
3472 : // " equations (2nd PDF comp.)", &
3473 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3474 0 : k = k + 1
3475 :
3476 : case ('cthl_1')
3477 0 : stats_metadata%icthl_1 = k
3478 :
3479 : call stat_assign( var_index=stats_metadata%icthl_1, var_name="cthl_1", &
3480 : var_description="cthl_1, Coefficient on theta-l in chi/eta" &
3481 : // " equations (1st PDF comp.)", &
3482 0 : var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
3483 0 : k = k + 1
3484 :
3485 : case ('cthl_2')
3486 0 : stats_metadata%icthl_2 = k
3487 :
3488 : call stat_assign( var_index=stats_metadata%icthl_2, var_name="cthl_2", &
3489 : var_description="cthl_2, Coefficient on theta-l in chi/eta" &
3490 : // " equations (2nd PDF comp.)", &
3491 0 : var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
3492 0 : k = k + 1
3493 :
3494 : case('F_w')
3495 0 : stats_metadata%iF_w = k
3496 :
3497 : call stat_assign( var_index=stats_metadata%iF_w, var_name="F_w", &
3498 : var_description="F_w, Parameter for the spread of the" &
3499 : // " PDF component means of w (new PDF)", &
3500 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3501 0 : k = k + 1
3502 :
3503 : case('F_rt')
3504 0 : stats_metadata%iF_rt = k
3505 :
3506 : call stat_assign( var_index=stats_metadata%iF_rt, var_name="F_rt", &
3507 : var_description="F_rt, Parameter for the spread of the" &
3508 : // " PDF component means of rt (new PDF)", &
3509 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3510 0 : k = k + 1
3511 :
3512 : case('F_thl')
3513 0 : stats_metadata%iF_thl = k
3514 :
3515 : call stat_assign( var_index=stats_metadata%iF_thl, var_name="F_thl", &
3516 : var_description="F_thl, Parameter for the spread of the" &
3517 : // " PDF component means of thl (new PDF)", &
3518 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3519 0 : k = k + 1
3520 :
3521 : case('min_F_w')
3522 0 : stats_metadata%imin_F_w = k
3523 :
3524 : call stat_assign( var_index=stats_metadata%imin_F_w, var_name="min_F_w", &
3525 : var_description="min_F_w, Minimum allowable value of the" &
3526 : // " parameter F_w (new PDF)", &
3527 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3528 0 : k = k + 1
3529 :
3530 : case('max_F_w')
3531 0 : stats_metadata%imax_F_w = k
3532 :
3533 : call stat_assign( var_index=stats_metadata%imax_F_w, var_name="max_F_w", &
3534 : var_description="max_F_w, Maximum allowable value of the" &
3535 : // " parameter F_w (new PDF)", &
3536 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3537 0 : k = k + 1
3538 :
3539 : case('min_F_rt')
3540 0 : stats_metadata%imin_F_rt = k
3541 :
3542 : call stat_assign( var_index=stats_metadata%imin_F_rt, var_name="min_F_rt", &
3543 : var_description="min_F_rt, Minimum allowable value of the" &
3544 : // " parameter F_rt (new PDF)", &
3545 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3546 0 : k = k + 1
3547 :
3548 : case('max_F_rt')
3549 0 : stats_metadata%imax_F_rt = k
3550 :
3551 : call stat_assign( var_index=stats_metadata%imax_F_rt, var_name="max_F_rt", &
3552 : var_description="max_F_rt, Maximum allowable value of the" &
3553 : // " parameter F_rt (new PDF)", &
3554 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3555 0 : k = k + 1
3556 :
3557 : case('min_F_thl')
3558 0 : stats_metadata%imin_F_thl = k
3559 :
3560 : call stat_assign( var_index=stats_metadata%imin_F_thl, var_name="min_F_thl", &
3561 : var_description="min_F_thl, Minimum allowable value of the" &
3562 : // " parameter F_thl (new PDF)", &
3563 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3564 0 : k = k + 1
3565 :
3566 : case('max_F_thl')
3567 0 : stats_metadata%imax_F_thl = k
3568 :
3569 : call stat_assign( var_index=stats_metadata%imax_F_thl, var_name="max_F_thl", &
3570 : var_description="max_F_thl, Maximum allowable value of the" &
3571 : // " parameter F_thl (new PDF)", &
3572 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3573 0 : k = k + 1
3574 :
3575 : case ( 'coef_wprtp2_implicit' )
3576 0 : stats_metadata%icoef_wprtp2_implicit = k
3577 : call stat_assign( var_index=stats_metadata%icoef_wprtp2_implicit, &
3578 : var_name="coef_wprtp2_implicit", &
3579 : var_description="coef_wprtp2_implicit, wprtp2" &
3580 : // " = coef_wprtp2_implicit" &
3581 : // " * rtp2" &
3582 : // " + term_wprtp2_explicit", &
3583 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3584 0 : k = k + 1
3585 :
3586 : case ( 'term_wprtp2_explicit' )
3587 0 : stats_metadata%iterm_wprtp2_explicit = k
3588 : call stat_assign( var_index=stats_metadata%iterm_wprtp2_explicit, &
3589 : var_name="term_wprtp2_explicit", &
3590 : var_description="term_wprtp2_explicit, wprtp2" &
3591 : // " = coef_wprtp2_implicit" &
3592 : // " * rtp2" &
3593 : // " + term_wprtp2_explicit", &
3594 : var_units="m/s kg^2/kg^2", l_silhs=.false., &
3595 0 : grid_kind=stats_zt )
3596 0 : k = k + 1
3597 :
3598 : case ( 'coef_wpthlp2_implicit' )
3599 0 : stats_metadata%icoef_wpthlp2_implicit = k
3600 : call stat_assign( var_index=stats_metadata%icoef_wpthlp2_implicit, &
3601 : var_name="coef_wpthlp2_implicit", &
3602 : var_description="coef_wpthlp2_implicit, wpthlp2" &
3603 : // " = coef_wpthlp2_implicit" &
3604 : // " * thlp2" &
3605 : // " + term_wpthlp2_explicit", &
3606 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3607 0 : k = k + 1
3608 :
3609 : case ( 'term_wpthlp2_explicit' )
3610 0 : stats_metadata%iterm_wpthlp2_explicit = k
3611 : call stat_assign( var_index=stats_metadata%iterm_wpthlp2_explicit, &
3612 : var_name="term_wpthlp2_explicit", &
3613 : var_description="term_wpthlp2_explicit, wpthlp2" &
3614 : // " = coef_wpthlp2_implicit" &
3615 : // " * thlp2" &
3616 : // " + term_wpthlp2_explicit", &
3617 : var_units="m/s K^2", l_silhs=.false., &
3618 0 : grid_kind=stats_zt )
3619 0 : k = k + 1
3620 :
3621 : case ( 'coef_wprtpthlp_implicit' )
3622 0 : stats_metadata%icoef_wprtpthlp_implicit = k
3623 : call stat_assign( var_index=stats_metadata%icoef_wprtpthlp_implicit, &
3624 : var_name="coef_wprtpthlp_implicit", &
3625 : var_description="coef_wprtpthlp_implicit, wprtpthlp" &
3626 : // " = coef_wprtpthlp_implicit" &
3627 : // " * rtpthlp" &
3628 : // " + term_wprtpthlp_explicit", &
3629 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3630 0 : k = k + 1
3631 :
3632 : case ( 'term_wprtpthlp_explicit' )
3633 0 : stats_metadata%iterm_wprtpthlp_explicit = k
3634 : call stat_assign( var_index=stats_metadata%iterm_wprtpthlp_explicit, &
3635 : var_name="term_wprtpthlp_explicit", &
3636 : var_description="term_wprtpthlp_explicit, wprtpthlp" &
3637 : // " = coef_wprtpthlp_implicit" &
3638 : // " * rtpthlp" &
3639 : // " + term_wprtpthlp_explicit]", &
3640 : var_units="m/s (kg/kg) K", l_silhs=.false., &
3641 0 : grid_kind=stats_zt )
3642 0 : k = k + 1
3643 :
3644 : case ( 'coef_wp2rtp_implicit' )
3645 0 : stats_metadata%icoef_wp2rtp_implicit = k
3646 : call stat_assign( var_index=stats_metadata%icoef_wp2rtp_implicit, &
3647 : var_name="coef_wp2rtp_implicit", &
3648 : var_description="coef_wp2rtp_implicit, wp2rtp" &
3649 : // " = coef_wp2rtp_implicit" &
3650 : // " * wprtp" &
3651 : // " + term_wp2rtp_explicit", &
3652 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3653 0 : k = k + 1
3654 :
3655 : case ( 'term_wp2rtp_explicit' )
3656 0 : stats_metadata%iterm_wp2rtp_explicit = k
3657 : call stat_assign( var_index=stats_metadata%iterm_wp2rtp_explicit, &
3658 : var_name="term_wp2rtp_explicit", &
3659 : var_description="term_wp2rtp_explicit, wp2rtp" &
3660 : // " = coef_wp2rtp_implicit" &
3661 : // " * wprtp" &
3662 : // " + term_wp2rtp_explicit", &
3663 : var_units="m^2/s^2 kg/kg", l_silhs=.false., &
3664 0 : grid_kind=stats_zt )
3665 0 : k = k + 1
3666 :
3667 : case ( 'coef_wp2thlp_implicit' )
3668 0 : stats_metadata%icoef_wp2thlp_implicit = k
3669 : call stat_assign( var_index=stats_metadata%icoef_wp2thlp_implicit, &
3670 : var_name="coef_wp2thlp_implicit", &
3671 : var_description="coef_wp2thlp_implicit, wp2thlp" &
3672 : // " = coef_wp2thlp_implicit" &
3673 : // " * wpthlp" &
3674 : // " + term_wp2thlp_explicit", &
3675 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3676 0 : k = k + 1
3677 :
3678 : case ( 'term_wp2thlp_explicit' )
3679 0 : stats_metadata%iterm_wp2thlp_explicit = k
3680 : call stat_assign( var_index=stats_metadata%iterm_wp2thlp_explicit, &
3681 : var_name="term_wp2thlp_explicit", &
3682 : var_description="term_wp2thlp_explicit, wp2thlp" &
3683 : // " = coef_wp2thlp_implicit" &
3684 : // " * wpthlp" &
3685 : // " + term_wp2thlp_explicit", &
3686 : var_units="m^2/s^2 K", l_silhs=.false., &
3687 0 : grid_kind=stats_zt )
3688 0 : k = k + 1
3689 :
3690 : case('wp2_zt')
3691 0 : stats_metadata%iwp2_zt = k
3692 :
3693 : call stat_assign( var_index=stats_metadata%iwp2_zt, var_name="wp2_zt", &
3694 : var_description="wp2_zt, w'^2 interpolated to thermodynamic levels", &
3695 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3696 0 : k = k + 1
3697 :
3698 : case('thlp2_zt')
3699 0 : stats_metadata%ithlp2_zt = k
3700 :
3701 : call stat_assign( var_index=stats_metadata%ithlp2_zt, var_name="thlp2_zt", &
3702 : var_description="thlp2_zt, thl'^2 interpolated to thermodynamic levels", &
3703 0 : var_units="K^2", l_silhs=.false., grid_kind=stats_zt )
3704 0 : k = k + 1
3705 :
3706 : case('wpthlp_zt')
3707 0 : stats_metadata%iwpthlp_zt = k
3708 :
3709 : call stat_assign( var_index=stats_metadata%iwpthlp_zt, var_name="wpthlp_zt", &
3710 : var_description="wpthlp_zt, w'thl' interpolated to thermodynamic levels", &
3711 0 : var_units="(m K)/s", l_silhs=.false., grid_kind=stats_zt )
3712 0 : k = k + 1
3713 :
3714 : case('wprtp_zt')
3715 0 : stats_metadata%iwprtp_zt = k
3716 :
3717 : call stat_assign( var_index=stats_metadata%iwprtp_zt, var_name="wprtp_zt", &
3718 : var_description="wprtp_zt, w'rt' interpolated to thermodynamic levels", &
3719 0 : var_units="(m kg)/(s kg)", l_silhs=.false., grid_kind=stats_zt )
3720 0 : k = k + 1
3721 :
3722 : case('rtp2_zt')
3723 0 : stats_metadata%irtp2_zt = k
3724 :
3725 : call stat_assign( var_index=stats_metadata%irtp2_zt, var_name="rtp2_zt", &
3726 : var_description="rtp2_zt, rt'^2 interpolated to thermodynamic levels", &
3727 0 : var_units="(kg/kg)^2", l_silhs=.false., grid_kind=stats_zt )
3728 0 : k = k + 1
3729 :
3730 : case('rtpthlp_zt')
3731 0 : stats_metadata%irtpthlp_zt = k
3732 :
3733 : call stat_assign( var_index=stats_metadata%irtpthlp_zt, var_name="rtpthlp_zt", &
3734 : var_description="rtpthlp_zt, rt'thl' interpolated to thermodynamic levels", &
3735 0 : var_units="(kg K)/kg", l_silhs=.false., grid_kind=stats_zt )
3736 0 : k = k + 1
3737 :
3738 : case ('up2_zt')
3739 0 : stats_metadata%iup2_zt = k
3740 : call stat_assign( var_index=stats_metadata%iup2_zt, var_name="up2_zt", &
3741 : var_description="up2_zt, u'^2 interpolated to thermodynamic levels", &
3742 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3743 0 : k = k + 1
3744 :
3745 : case ('vp2_zt')
3746 0 : stats_metadata%ivp2_zt = k
3747 : call stat_assign( var_index=stats_metadata%ivp2_zt, var_name="vp2_zt", &
3748 : var_description="vp2_zt, v'^2 interpolated to thermodynamic levels", &
3749 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3750 0 : k = k + 1
3751 :
3752 : case ('upwp_zt')
3753 0 : stats_metadata%iupwp_zt = k
3754 : call stat_assign( var_index=stats_metadata%iupwp_zt, var_name="upwp_zt", &
3755 : var_description="upwp_zt, u'w' interpolated to thermodynamic levels", &
3756 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3757 0 : k = k + 1
3758 :
3759 : case ('vpwp_zt')
3760 0 : stats_metadata%ivpwp_zt = k
3761 : call stat_assign( var_index=stats_metadata%ivpwp_zt, var_name="vpwp_zt", &
3762 : var_description="vpwp_zt, v'w' interpolated to thermodynamic levels", &
3763 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3764 0 : k = k + 1
3765 :
3766 : case ('Skw_zt')
3767 0 : stats_metadata%iSkw_zt = k
3768 : call stat_assign( var_index=stats_metadata%iSkw_zt, var_name="Skw_zt", &
3769 : var_description="Skw_zt, Skewness of w on thermodynamic levels", &
3770 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3771 0 : k = k + 1
3772 :
3773 : case ('Skthl_zt')
3774 0 : stats_metadata%iSkthl_zt = k
3775 : call stat_assign( var_index=stats_metadata%iSkthl_zt, var_name="Skthl_zt", &
3776 : var_description="Skthl_zt, Skewness of thl on thermodynamic levels", &
3777 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3778 0 : k = k + 1
3779 :
3780 : case ('Skrt_zt')
3781 0 : stats_metadata%iSkrt_zt = k
3782 : call stat_assign( var_index=stats_metadata%iSkrt_zt, var_name="Skrt_zt", &
3783 : var_description="Skrt_zt, Skewness of rt on thermodynamic levels", &
3784 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3785 0 : k = k + 1
3786 :
3787 : case ('rcm_supersat_adj')
3788 0 : stats_metadata%ircm_supersat_adj = k
3789 : call stat_assign( var_index=stats_metadata%ircm_supersat_adj, var_name="rcm_supersat_adj", &
3790 : var_description="rcm_supersat_adj, rcm adjustment due to spurious supersaturation", &
3791 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3792 0 : k = k + 1
3793 :
3794 : ! Hydrometeor overall variances for each hydrometeor type.
3795 : case('hmp2_zt')
3796 :
3797 0 : do hm_idx = 1, hydromet_dim, 1
3798 :
3799 0 : hm_type = hydromet_list(hm_idx)
3800 :
3801 : ! The overall variance of the hydrometeor.
3802 0 : stats_metadata%ihmp2_zt(hm_idx) = k
3803 :
3804 0 : if ( l_mix_rat_hm(hm_idx) ) then
3805 :
3806 0 : call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
3807 : var_name=trim( hm_type(1:2) )//"p2_zt", &
3808 : var_description="<" &
3809 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3810 : // "'^2> on thermodynamic levels (from " &
3811 : // "integration over PDF) [(kg/kg)^2]", &
3812 : var_units="(kg/kg)^2", &
3813 0 : l_silhs=.false., grid_kind=stats_zt )
3814 :
3815 : else ! Concentration
3816 :
3817 0 : call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
3818 : var_name=trim( hm_type(1:2) )//"p2_zt", &
3819 : var_description="<" &
3820 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3821 : // "'^2> on thermodynamic levels (from " &
3822 : // "integration over PDF) [(num/kg)^2]", &
3823 : var_units="(num/kg)^2", &
3824 0 : l_silhs=.false., grid_kind=stats_zt )
3825 :
3826 : endif ! l_mix_rat_hm(hm_idx)
3827 :
3828 0 : k = k + 1
3829 :
3830 : enddo ! hm_idx = 1, hydromet_dim, 1
3831 :
3832 : case ('C11_Skw_fnc')
3833 0 : stats_metadata%iC11_Skw_fnc = k
3834 :
3835 : call stat_assign( var_index=stats_metadata%iC11_Skw_fnc, var_name="C11_Skw_fnc", &
3836 : var_description="C11_Skw_fnc, C_11 parameter with Sk_w applied", var_units="count", &
3837 0 : l_silhs=.false., grid_kind=stats_zt )
3838 0 : k = k + 1
3839 :
3840 : case ('chi')
3841 0 : stats_metadata%ichi = k
3842 :
3843 : call stat_assign( var_index=stats_metadata%ichi, var_name="chi", &
3844 : var_description="chi, Mellor's s (extended liq)", var_units="kg/kg", &
3845 0 : l_silhs=.false., grid_kind=stats_zt )
3846 0 : k = k + 1
3847 :
3848 : case ( 'a3_coef_zt' )
3849 0 : stats_metadata%ia3_coef_zt = k
3850 : call stat_assign( var_index=stats_metadata%ia3_coef_zt, var_name="a3_coef_zt", &
3851 : var_description="a3_coef_zt, The a3 coefficient interpolated the the zt grid", &
3852 0 : var_units="count", l_silhs=.false., grid_kind=stats_zt )
3853 0 : k = k + 1
3854 :
3855 : case ( 'wp3_on_wp2_zt' )
3856 0 : stats_metadata%iwp3_on_wp2_zt = k
3857 : call stat_assign( var_index=stats_metadata%iwp3_on_wp2_zt, var_name="wp3_on_wp2_zt", &
3858 : var_description="wp3_on_wp2_zt, Smoothed version of wp3 / wp2", var_units="m/s", &
3859 0 : l_silhs=.false., grid_kind=stats_zt )
3860 0 : k = k + 1
3861 :
3862 : ! Hydrometeor component mean values for each PDF component and hydrometeor
3863 : ! type.
3864 : case ( "hm_i" )
3865 :
3866 0 : do hm_idx = 1, hydromet_dim, 1
3867 :
3868 0 : hm_type = hydromet_list(hm_idx)
3869 :
3870 : ! The mean of the hydrometeor in the 1st PDF component.
3871 0 : stats_metadata%ihm_1(hm_idx) = k
3872 :
3873 0 : if ( l_mix_rat_hm(hm_idx) ) then
3874 :
3875 0 : call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
3876 : var_name=trim( hm_type(1:2) )//"_1", &
3877 : var_description="Mean of " &
3878 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3879 : // " (1st PDF component) [kg/kg]", &
3880 : var_units="kg/kg", &
3881 0 : l_silhs=.false., grid_kind=stats_zt )
3882 :
3883 : else ! Concentration
3884 :
3885 0 : call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
3886 : var_name=trim( hm_type(1:2) )//"_1", &
3887 : var_description="Mean of " &
3888 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3889 : // " (1st PDF component) [num/kg]", &
3890 : var_units="num/kg", &
3891 0 : l_silhs=.false., grid_kind=stats_zt )
3892 :
3893 : endif ! l_mix_rat_hm(hm_idx)
3894 :
3895 0 : k = k + 1
3896 :
3897 : ! The mean of the hydrometeor in the 2nd PDF component.
3898 0 : stats_metadata%ihm_2(hm_idx) = k
3899 :
3900 0 : if ( l_mix_rat_hm(hm_idx) ) then
3901 :
3902 0 : call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
3903 : var_name=trim( hm_type(1:2) )//"_2", &
3904 : var_description="Mean of " &
3905 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3906 : // " (2nd PDF component) [kg/kg]", &
3907 : var_units="kg/kg", &
3908 0 : l_silhs=.false., grid_kind=stats_zt )
3909 :
3910 : else ! Concentration
3911 :
3912 0 : call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
3913 : var_name=trim( hm_type(1:2) )//"_2", &
3914 : var_description="Mean of " &
3915 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3916 : // " (2nd PDF component) [num/kg]", &
3917 : var_units="num/kg", &
3918 0 : l_silhs=.false., grid_kind=stats_zt )
3919 :
3920 : endif ! l_mix_rat_hm(hm_idx)
3921 :
3922 0 : k = k + 1
3923 :
3924 : enddo ! hm_idx = 1, hydromet_dim, 1
3925 :
3926 : case ( 'precip_frac' )
3927 0 : stats_metadata%iprecip_frac = k
3928 : call stat_assign( var_index=stats_metadata%iprecip_frac, var_name="precip_frac", &
3929 : var_description="precip_frac, Precipitation Fraction", var_units="-", &
3930 0 : l_silhs=.false., grid_kind=stats_zt )
3931 0 : k = k + 1
3932 :
3933 : case ( 'precip_frac_1' )
3934 0 : stats_metadata%iprecip_frac_1 = k
3935 : call stat_assign( var_index=stats_metadata%iprecip_frac_1, var_name="precip_frac_1", &
3936 : var_description="precip_frac_1, Precipitation Fraction (1st PDF component)", &
3937 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3938 0 : k = k + 1
3939 :
3940 : case ( 'precip_frac_2' )
3941 0 : stats_metadata%iprecip_frac_2 = k
3942 : call stat_assign( var_index=stats_metadata%iprecip_frac_2, var_name="precip_frac_2", &
3943 : var_description="precip_frac_2, Precipitation Fraction (2nd PDF component)", &
3944 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3945 0 : k = k + 1
3946 :
3947 : case ( 'Ncnm' )
3948 0 : stats_metadata%iNcnm = k
3949 : call stat_assign( var_index=stats_metadata%iNcnm, var_name="Ncnm", &
3950 : var_description="Ncnm, Cloud nuclei concentration (PDF)", &
3951 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
3952 0 : k = k + 1
3953 :
3954 : ! Hydrometeor component mean values (in-precip) for each PDF component and
3955 : ! hydrometeor type.
3956 : case ( 'mu_hm_i' )
3957 :
3958 0 : do hm_idx = 1, hydromet_dim, 1
3959 :
3960 0 : hm_type = hydromet_list(hm_idx)
3961 :
3962 : ! The in-precip mean of the hydrometeor in the 1st PDF component.
3963 0 : stats_metadata%imu_hm_1(hm_idx) = k
3964 :
3965 0 : if ( l_mix_rat_hm(hm_idx) ) then
3966 :
3967 0 : call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
3968 : var_name="mu_"//trim( hm_type(1:2) )//"_1", &
3969 : var_description="Mean (in-precip) of " &
3970 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3971 : // " (1st PDF component) [kg/kg]", &
3972 : var_units="kg/kg", &
3973 0 : l_silhs=.false., grid_kind=stats_zt )
3974 :
3975 : else ! Concentration
3976 :
3977 0 : call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
3978 : var_name="mu_"//trim( hm_type(1:2) )//"_1", &
3979 : var_description="Mean (in-precip) of " &
3980 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3981 : // " (1st PDF component) [num/kg]", &
3982 : var_units="num/kg", &
3983 0 : l_silhs=.false., grid_kind=stats_zt )
3984 :
3985 : endif ! l_mix_rat_hm(hm_idx)
3986 :
3987 0 : k = k + 1
3988 :
3989 : ! The in-precip mean of the hydrometeor in the 2nd PDF component.
3990 0 : stats_metadata%imu_hm_2(hm_idx) = k
3991 :
3992 0 : if ( l_mix_rat_hm(hm_idx) ) then
3993 :
3994 0 : call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
3995 : var_name="mu_"//trim( hm_type(1:2) )//"_2", &
3996 : var_description="Mean (in-precip) of " &
3997 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3998 : // " (2nd PDF component) [kg/kg]", &
3999 : var_units="kg/kg", &
4000 0 : l_silhs=.false., grid_kind=stats_zt )
4001 :
4002 : else ! Concentration
4003 :
4004 0 : call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
4005 : var_name="mu_"//trim( hm_type(1:2) )//"_2", &
4006 : var_description="Mean (in-precip) of " &
4007 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4008 : // " (2nd PDF component) [num/kg]", &
4009 : var_units="num/kg", &
4010 0 : l_silhs=.false., grid_kind=stats_zt )
4011 :
4012 : endif ! l_mix_rat_hm(hm_idx)
4013 :
4014 0 : k = k + 1
4015 :
4016 : enddo ! hm_idx = 1, hydromet_dim, 1
4017 :
4018 : case ( 'mu_Ncn_i' )
4019 0 : stats_metadata%imu_Ncn_1 = k
4020 :
4021 : call stat_assign( var_index=stats_metadata%imu_Ncn_1, &
4022 : var_name="mu_Ncn_1", &
4023 : var_description="mu_Ncn_1, Mean of N_cn (1st PDF component) " &
4024 : // "[num/kg]", var_units="num/kg", &
4025 0 : l_silhs=.false., grid_kind=stats_zt )
4026 :
4027 0 : k = k + 1
4028 :
4029 0 : stats_metadata%imu_Ncn_2 = k
4030 :
4031 : call stat_assign( var_index=stats_metadata%imu_Ncn_2, &
4032 : var_name="mu_Ncn_2", &
4033 : var_description="mu_Ncn_2, Mean of N_cn (2nd PDF component)", &
4034 : var_units="num/kg", &
4035 0 : l_silhs=.false., grid_kind=stats_zt )
4036 :
4037 0 : k = k + 1
4038 :
4039 : ! Hydrometeor component mean values (in-precip) for ln hm for each PDF
4040 : ! component and hydrometeor type.
4041 : case ( 'mu_hm_i_n' )
4042 :
4043 0 : do hm_idx = 1, hydromet_dim, 1
4044 :
4045 0 : hm_type = hydromet_list(hm_idx)
4046 :
4047 : ! The in-precip mean of ln hm in the 1st PDF component.
4048 0 : stats_metadata%imu_hm_1_n(hm_idx) = k
4049 :
4050 0 : if ( l_mix_rat_hm(hm_idx) ) then
4051 :
4052 0 : call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
4053 : var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
4054 : var_description="Mean (in-precip) of ln " &
4055 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4056 : // " (1st PDF component) [ln(kg/kg)]", &
4057 : var_units="ln(kg/kg)", &
4058 0 : l_silhs=.false., grid_kind=stats_zt )
4059 :
4060 : else ! Concentration
4061 :
4062 0 : call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
4063 : var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
4064 : var_description="Mean (in-precip) of ln " &
4065 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4066 : // " (1st PDF component) [ln(num/kg)]", &
4067 : var_units="ln(num/kg)", &
4068 0 : l_silhs=.false., grid_kind=stats_zt )
4069 :
4070 : endif ! l_mix_rat_hm(hm_idx)
4071 :
4072 0 : k = k + 1
4073 :
4074 : ! The in-precip mean of ln hm in the 2nd PDF component.
4075 0 : stats_metadata%imu_hm_2_n(hm_idx) = k
4076 :
4077 0 : if ( l_mix_rat_hm(hm_idx) ) then
4078 :
4079 0 : call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
4080 : var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
4081 : var_description="Mean (in-precip) of ln " &
4082 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4083 : // " (2nd PDF component) [ln(kg/kg)]", &
4084 : var_units="ln(kg/kg)", &
4085 0 : l_silhs=.false., grid_kind=stats_zt )
4086 :
4087 : else ! Concentration
4088 :
4089 0 : call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
4090 : var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
4091 : var_description="Mean (in-precip) of ln " &
4092 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4093 : // " (2nd PDF component) [ln(num/kg)]", &
4094 : var_units="ln(num/kg)", &
4095 0 : l_silhs=.false., grid_kind=stats_zt )
4096 :
4097 : endif ! l_mix_rat_hm(hm_idx)
4098 :
4099 0 : k = k + 1
4100 :
4101 : enddo ! hm_idx = 1, hydromet_dim, 1
4102 :
4103 : case ( 'mu_Ncn_i_n' )
4104 :
4105 0 : stats_metadata%imu_Ncn_1_n = k
4106 :
4107 : call stat_assign( var_index=stats_metadata%imu_Ncn_1_n, &
4108 : var_name="mu_Ncn_1_n", &
4109 : var_description="mu_Ncn_1_n, Mean of ln N_cn " &
4110 : // "(1st PDF component)", &
4111 : var_units="ln(num/kg)", &
4112 0 : l_silhs=.false., grid_kind=stats_zt )
4113 :
4114 0 : k = k + 1
4115 :
4116 0 : stats_metadata%imu_Ncn_2_n = k
4117 :
4118 : call stat_assign( var_index=stats_metadata%imu_Ncn_2_n, &
4119 : var_name="mu_Ncn_2_n", &
4120 : var_description="mu_Ncn_2_n, Mean of ln N_cn " &
4121 : // "(2nd PDF component)", &
4122 : var_units="ln(num/kg)", &
4123 0 : l_silhs=.false., grid_kind=stats_zt )
4124 :
4125 0 : k = k + 1
4126 :
4127 : ! Hydrometeor component standard deviations (in-precip) for each PDF
4128 : ! component and hydrometeor type.
4129 : case ( 'sigma_hm_i' )
4130 :
4131 0 : do hm_idx = 1, hydromet_dim, 1
4132 :
4133 0 : hm_type = hydromet_list(hm_idx)
4134 :
4135 : ! The in-precip standard deviation of the hydrometeor in the 1st PDF
4136 : ! component.
4137 0 : stats_metadata%isigma_hm_1(hm_idx) = k
4138 :
4139 0 : if ( l_mix_rat_hm(hm_idx) ) then
4140 :
4141 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
4142 : var_name="sigma_" &
4143 : // trim( hm_type(1:2) )//"_1", &
4144 : var_description="Standard deviation " &
4145 : // "(in-precip) of " &
4146 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4147 : // " (1st PDF component) [kg/kg]", &
4148 : var_units="kg/kg", &
4149 0 : l_silhs=.false., grid_kind=stats_zt )
4150 :
4151 : else ! Concentration
4152 :
4153 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
4154 : var_name="sigma_" &
4155 : // trim( hm_type(1:2) )//"_1", &
4156 : var_description="Standard deviation " &
4157 : // "(in-precip) of " &
4158 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4159 : // " (1st PDF component) [num/kg]", &
4160 : var_units="num/kg", &
4161 0 : l_silhs=.false., grid_kind=stats_zt )
4162 :
4163 : endif ! l_mix_rat_hm(hm_idx)
4164 :
4165 0 : k = k + 1
4166 :
4167 : ! The in-precip standard deviation of the hydrometeor in the 2nd PDF
4168 : ! component.
4169 0 : stats_metadata%isigma_hm_2(hm_idx) = k
4170 :
4171 0 : if ( l_mix_rat_hm(hm_idx) ) then
4172 :
4173 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
4174 : var_name="sigma_" &
4175 : // trim( hm_type(1:2) )//"_2", &
4176 : var_description="Standard deviation " &
4177 : // "(in-precip) of " &
4178 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4179 : // " (2nd PDF component) [kg/kg]", &
4180 : var_units="kg/kg", &
4181 0 : l_silhs=.false., grid_kind=stats_zt )
4182 :
4183 : else ! Concentration
4184 :
4185 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
4186 : var_name="sigma_" &
4187 : // trim( hm_type(1:2) )//"_2", &
4188 : var_description="Standard deviation " &
4189 : // "(in-precip) of " &
4190 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4191 : // " (2nd PDF component) [num/kg]", &
4192 : var_units="num/kg", &
4193 0 : l_silhs=.false., grid_kind=stats_zt )
4194 :
4195 : endif ! l_mix_rat_hm(hm_idx)
4196 :
4197 0 : k = k + 1
4198 :
4199 : enddo ! hm_idx = 1, hydromet_dim, 1
4200 :
4201 : case ( 'sigma_Ncn_i' )
4202 :
4203 0 : stats_metadata%isigma_Ncn_1 = k
4204 :
4205 : call stat_assign( var_index=stats_metadata%isigma_Ncn_1, &
4206 : var_name="sigma_Ncn_1", &
4207 : var_description="sigma_Ncn_1, Standard deviation of N_cn " &
4208 : // "(1st PDF component)", &
4209 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
4210 :
4211 0 : k = k + 1
4212 :
4213 0 : stats_metadata%isigma_Ncn_2 = k
4214 :
4215 : call stat_assign( var_index=stats_metadata%isigma_Ncn_2, &
4216 : var_name="sigma_Ncn_2", &
4217 : var_description="sigma_Ncn_2, Standard deviation of N_cn " &
4218 : // "(2nd PDF component)", &
4219 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
4220 :
4221 0 : k = k + 1
4222 :
4223 : ! Hydrometeor component standard deviations (in-precip) for ln hm for each
4224 : ! PDF component and hydrometeor type.
4225 : case ( 'sigma_hm_i_n' )
4226 :
4227 0 : do hm_idx = 1, hydromet_dim, 1
4228 :
4229 0 : hm_type = hydromet_list(hm_idx)
4230 :
4231 : ! The in-precip standard deviation of ln hm in the 1st PDF
4232 : ! component.
4233 0 : stats_metadata%isigma_hm_1_n(hm_idx) = k
4234 :
4235 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1_n(hm_idx), &
4236 : var_name="sigma_" &
4237 : // trim( hm_type(1:2) )//"_1_n", &
4238 : var_description="Standard deviation " &
4239 : // "(in-precip) of ln " &
4240 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4241 : // " (1st PDF component) [-]", &
4242 : var_units="-", &
4243 0 : l_silhs=.false., grid_kind=stats_zt )
4244 :
4245 0 : k = k + 1
4246 :
4247 : ! The in-precip standard deviation of ln hm in the 2nd PDF
4248 : ! component.
4249 0 : stats_metadata%isigma_hm_2_n(hm_idx) = k
4250 :
4251 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2_n(hm_idx), &
4252 : var_name="sigma_" &
4253 : // trim( hm_type(1:2) )//"_2_n", &
4254 : var_description="Standard deviation " &
4255 : // "(in-precip) of ln " &
4256 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4257 : // " (2nd PDF component) [-]", &
4258 : var_units="-", &
4259 0 : l_silhs=.false., grid_kind=stats_zt )
4260 :
4261 0 : k = k + 1
4262 :
4263 : enddo ! hm_idx = 1, hydromet_dim, 1
4264 :
4265 : case ( 'sigma_Ncn_i_n' )
4266 0 : stats_metadata%isigma_Ncn_1_n = k
4267 :
4268 : call stat_assign( var_index=stats_metadata%isigma_Ncn_1_n, &
4269 : var_name="sigma_Ncn_1_n", &
4270 : var_description="sigma_Ncn_1_n, Standard deviation of ln N_cn " &
4271 : // "(1st PDF component)", &
4272 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4273 :
4274 0 : k = k + 1
4275 :
4276 0 : stats_metadata%isigma_Ncn_2_n = k
4277 :
4278 : call stat_assign( var_index=stats_metadata%isigma_Ncn_2_n, &
4279 : var_name="sigma_Ncn_2_n", &
4280 : var_description="sigma_Ncn_2_n, Standard deviation of ln N_cn " &
4281 : // "(2nd PDF component)", &
4282 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4283 :
4284 0 : k = k + 1
4285 :
4286 : case ('corr_w_chi_1_ca')
4287 0 : stats_metadata%icorr_w_chi_1_ca = k
4288 :
4289 : call stat_assign( var_index=stats_metadata%icorr_w_chi_1_ca, &
4290 : var_name="corr_w_chi_1_ca", &
4291 : var_description="corr_w_chi_1_ca, Correlation of w and chi" &
4292 : // " (1st PDF component) found in the correlation" &
4293 : // " array", var_units="-", &
4294 0 : l_silhs=.false., grid_kind=stats_zt )
4295 0 : k = k + 1
4296 :
4297 : case ('corr_w_chi_2_ca')
4298 0 : stats_metadata%icorr_w_chi_2_ca = k
4299 :
4300 : call stat_assign( var_index=stats_metadata%icorr_w_chi_2_ca, &
4301 : var_name="corr_w_chi_2_ca", &
4302 : var_description="corr_w_chi_2_ca, Correlation of w and chi" &
4303 : // " (2nd PDF component) found in the correlation" &
4304 : // " array", var_units="-", &
4305 0 : l_silhs=.false., grid_kind=stats_zt )
4306 0 : k = k + 1
4307 :
4308 : case ('corr_w_eta_1_ca')
4309 0 : stats_metadata%icorr_w_eta_1_ca = k
4310 :
4311 : call stat_assign( var_index=stats_metadata%icorr_w_eta_1_ca, &
4312 : var_name="corr_w_eta_1_ca", &
4313 : var_description="corr_w_eta_1_ca, Correlation of w and eta" &
4314 : // " (1st PDF component) found in the correlation" &
4315 : // " array", var_units="-", &
4316 0 : l_silhs=.false., grid_kind=stats_zt )
4317 0 : k = k + 1
4318 :
4319 : case ('corr_w_eta_2_ca')
4320 0 : stats_metadata%icorr_w_eta_2_ca = k
4321 :
4322 : call stat_assign( var_index=stats_metadata%icorr_w_eta_2_ca, &
4323 : var_name="corr_w_eta_2_ca", &
4324 : var_description="corr_w_eta_2_ca, Correlation of w and eta" &
4325 : // " (2nd PDF component) found in the correlation" &
4326 : // " array", var_units="-", &
4327 0 : l_silhs=.false., grid_kind=stats_zt )
4328 0 : k = k + 1
4329 :
4330 : ! Correlation of w and a hydrometeor (in-precip) for each PDF
4331 : ! component and hydrometeor type.
4332 : case ( 'corr_w_hm_i' )
4333 :
4334 0 : do hm_idx = 1, hydromet_dim, 1
4335 :
4336 0 : hm_type = hydromet_list(hm_idx)
4337 :
4338 : ! The in-precip correlation of w and the hydrometeor in the
4339 : ! 1st PDF component.
4340 0 : stats_metadata%icorr_w_hm_1(hm_idx) = k
4341 :
4342 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_1(hm_idx), &
4343 : var_name="corr_w_"//trim( hm_type(1:2) )//"_1", &
4344 : var_description="Correlation (in-precip) " &
4345 : // "of w and " &
4346 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4347 : // " (1st PDF component) [-]", &
4348 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4349 :
4350 0 : k = k + 1
4351 :
4352 : ! The in-precip correlation of w and the hydrometeor in the
4353 : ! 2nd PDF component.
4354 0 : stats_metadata%icorr_w_hm_2(hm_idx) = k
4355 :
4356 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_2(hm_idx), &
4357 : var_name="corr_w_"//trim( hm_type(1:2) )//"_2", &
4358 : var_description="Correlation (in-precip) " &
4359 : // "of w and " &
4360 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4361 : // " (2nd PDF component) [-]", &
4362 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4363 :
4364 0 : k = k + 1
4365 :
4366 : enddo ! hm_idx = 1, hydromet_dim, 1
4367 :
4368 : case ( 'corr_w_Ncn_i' )
4369 0 : stats_metadata%icorr_w_Ncn_1 = k
4370 :
4371 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1, &
4372 : var_name="corr_w_Ncn_1", &
4373 : var_description="corr_w_Ncn_1, Correlation of w and N_cn " &
4374 : // "(1st PDF component)", &
4375 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4376 :
4377 0 : k = k + 1
4378 :
4379 0 : stats_metadata%icorr_w_Ncn_2 = k
4380 :
4381 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2, &
4382 : var_name="corr_w_Ncn_2", &
4383 : var_description="corr_w_Ncn_2, Correlation of w and N_cn " &
4384 : // "(2nd PDF component)", &
4385 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4386 :
4387 0 : k = k + 1
4388 :
4389 : case ('corr_chi_eta_1_ca')
4390 0 : stats_metadata%icorr_chi_eta_1_ca = k
4391 :
4392 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_1_ca, &
4393 : var_name="corr_chi_eta_1_ca", &
4394 : var_description="corr_chi_eta_1_ca, Correlation of chi (s) and" &
4395 : // " eta (t) (1st PDF component) found in the" &
4396 : // " correlation array", var_units="-", &
4397 0 : l_silhs=.false., grid_kind=stats_zt )
4398 0 : k = k + 1
4399 :
4400 : case ('corr_chi_eta_2_ca')
4401 0 : stats_metadata%icorr_chi_eta_2_ca = k
4402 :
4403 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_2_ca, &
4404 : var_name="corr_chi_eta_2_ca", &
4405 : var_description="corr_chi_eta_2_ca, Correlation of chi (s) and" &
4406 : // " eta (t) (2nd PDF component) found in the" &
4407 : // " correlation array", var_units="-", &
4408 0 : l_silhs=.false., grid_kind=stats_zt )
4409 0 : k = k + 1
4410 :
4411 : ! Correlation of chi(s) and a hydrometeor (in-precip) for each PDF
4412 : ! component and hydrometeor type.
4413 : case ( 'corr_chi_hm_i' )
4414 :
4415 0 : do hm_idx = 1, hydromet_dim, 1
4416 :
4417 0 : hm_type = hydromet_list(hm_idx)
4418 :
4419 : ! The in-precip correlation of chi and the hydrometeor in the
4420 : ! 1st PDF component.
4421 0 : stats_metadata%icorr_chi_hm_1(hm_idx) = k
4422 :
4423 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_1(hm_idx), &
4424 : var_name="corr_chi_"//trim(hm_type(1:2))//"_1", &
4425 : var_description="Correlation (in-precip) " &
4426 : // "of chi (s) and " &
4427 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4428 : // " (1st PDF component) [-]", &
4429 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4430 :
4431 0 : k = k + 1
4432 :
4433 : ! The in-precip correlation of chi and the hydrometeor in the
4434 : ! 2nd PDF component.
4435 0 : stats_metadata%icorr_chi_hm_2(hm_idx) = k
4436 :
4437 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_2(hm_idx), &
4438 : var_name="corr_chi_"//trim(hm_type(1:2))//"_2", &
4439 : var_description="Correlation (in-precip) " &
4440 : // "of chi (s) and " &
4441 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4442 : // " (2nd PDF component) [-]", &
4443 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4444 :
4445 0 : k = k + 1
4446 :
4447 : enddo ! hm_idx = 1, hydromet_dim, 1
4448 :
4449 : case ( 'corr_chi_Ncn_i' )
4450 0 : stats_metadata%icorr_chi_Ncn_1 = k
4451 :
4452 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1, &
4453 : var_name="corr_chi_Ncn_1", &
4454 : var_description="corr_chi_Ncn_1, Correlation of chi and N_cn " &
4455 : // "(1st PDF component)", &
4456 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4457 :
4458 0 : k = k + 1
4459 :
4460 0 : stats_metadata%icorr_chi_Ncn_2 = k
4461 :
4462 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2, &
4463 : var_name="corr_chi_Ncn_2", &
4464 : var_description="corr_chi_Ncn_2, Correlation of chi and N_cn " &
4465 : // "(2nd PDF component)", &
4466 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4467 :
4468 0 : k = k + 1
4469 :
4470 : ! Correlation of eta(t) and a hydrometeor (in-precip) for each PDF
4471 : ! component and hydrometeor type.
4472 : case ( 'corr_eta_hm_i' )
4473 :
4474 0 : do hm_idx = 1, hydromet_dim, 1
4475 :
4476 0 : hm_type = hydromet_list(hm_idx)
4477 :
4478 : ! The in-precip correlation of eta and the hydrometeor in the
4479 : ! 1st PDF component.
4480 0 : stats_metadata%icorr_eta_hm_1(hm_idx) = k
4481 :
4482 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_1(hm_idx), &
4483 : var_name="corr_eta_"//trim(hm_type(1:2))//"_1", &
4484 : var_description="Correlation (in-precip) " &
4485 : // "of eta (t) and " &
4486 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4487 : // " (1st PDF component) [-]", &
4488 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4489 :
4490 0 : k = k + 1
4491 :
4492 : ! The in-precip correlation of eta and the hydrometeor in the
4493 : ! 2nd PDF component.
4494 0 : stats_metadata%icorr_eta_hm_2(hm_idx) = k
4495 :
4496 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_2(hm_idx), &
4497 : var_name="corr_eta_"//trim(hm_type(1:2))//"_2", &
4498 : var_description="Correlation (in-precip) " &
4499 : // "of eta (t) and " &
4500 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4501 : // " (2nd PDF component) [-]", &
4502 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4503 :
4504 0 : k = k + 1
4505 :
4506 : enddo ! hm_idx = 1, hydromet_dim, 1
4507 :
4508 : case ( 'corr_eta_Ncn_i' )
4509 :
4510 0 : stats_metadata%icorr_eta_Ncn_1 = k
4511 :
4512 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1, &
4513 : var_name="corr_eta_Ncn_1", &
4514 : var_description="corr_eta_Ncn_1, Correlation of eta and N_cn " &
4515 : // "(1st PDF component)", &
4516 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4517 :
4518 0 : k = k + 1
4519 :
4520 0 : stats_metadata%icorr_eta_Ncn_2 = k
4521 :
4522 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2, &
4523 : var_name="corr_eta_Ncn_2", &
4524 : var_description="corr_eta_Ncn_2, Correlation of eta and N_cn " &
4525 : // "(2nd PDF component)", &
4526 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4527 :
4528 0 : k = k + 1
4529 :
4530 : ! Correlation of Ncn and a hydrometeor (in-precip) for each PDF
4531 : ! component and hydrometeor type.
4532 : case ( 'corr_Ncn_hm_i' )
4533 :
4534 0 : do hm_idx = 1, hydromet_dim, 1
4535 :
4536 0 : hm_type = hydromet_list(hm_idx)
4537 :
4538 : ! The in-precip correlation of Ncn and the hydrometeor in the
4539 : ! 1st PDF component.
4540 0 : stats_metadata%icorr_Ncn_hm_1(hm_idx) = k
4541 :
4542 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1(hm_idx), &
4543 : var_name="corr_Ncn_"//trim(hm_type(1:2))//"_1", &
4544 : var_description="Correlation (in-precip) " &
4545 : // "of N_cn and " &
4546 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4547 : // " (1st PDF component) [-]", &
4548 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4549 :
4550 0 : k = k + 1
4551 :
4552 : ! The in-precip correlation of Ncn and the hydrometeor in the
4553 : ! 2nd PDF component.
4554 0 : stats_metadata%icorr_Ncn_hm_2(hm_idx) = k
4555 :
4556 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2(hm_idx), &
4557 : var_name="corr_Ncn_"//trim(hm_type(1:2))//"_2", &
4558 : var_description="Correlation (in-precip) " &
4559 : // "of N_cn and " &
4560 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4561 : // " (2nd PDF component) [-]", &
4562 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4563 :
4564 0 : k = k + 1
4565 :
4566 : enddo ! hm_idx = 1, hydromet_dim, 1
4567 :
4568 : ! Correlation (in-precip) of two different hydrometeors (hmx and hmy)
4569 : ! for each PDF component and hydrometeor type.
4570 : case ( 'corr_hmx_hmy_i' )
4571 :
4572 0 : do hmx_idx = 1, hydromet_dim, 1
4573 :
4574 0 : hmx_type = hydromet_list(hmx_idx)
4575 :
4576 0 : do hmy_idx = hmx_idx+1, hydromet_dim, 1
4577 :
4578 0 : hmy_type = hydromet_list(hmy_idx)
4579 :
4580 : ! The in-precip correlation of hmx and hmy in the 1st PDF
4581 : ! component.
4582 0 : stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx) = k
4583 :
4584 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx), &
4585 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4586 : // trim( hmy_type(1:2) )//"_1", &
4587 : var_description="Correlation (in-precip) " &
4588 : // "of " &
4589 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4590 : // " and " &
4591 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4592 : // " (1st PDF component) [-]", &
4593 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4594 :
4595 0 : k = k + 1
4596 :
4597 : ! The in-precip correlation of hmx and hmy in the 2nd PDF
4598 : ! component.
4599 0 : stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx) = k
4600 :
4601 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx), &
4602 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4603 : // trim( hmy_type(1:2) )//"_2", &
4604 : var_description="Correlation (in-precip) " &
4605 : // "of " &
4606 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4607 : // " and " &
4608 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4609 : // " (2nd PDF component) [-]", &
4610 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4611 :
4612 0 : k = k + 1
4613 :
4614 : enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
4615 :
4616 : enddo ! hmx_idx = 1, hydromet_dim, 1
4617 :
4618 : ! Correlation (in-precip) of w and ln hm for each PDF component and
4619 : ! hydrometeor type.
4620 : case ( 'corr_w_hm_i_n' )
4621 :
4622 0 : do hm_idx = 1, hydromet_dim, 1
4623 :
4624 0 : hm_type = hydromet_list(hm_idx)
4625 :
4626 : ! The in-precip correlation of w and ln hm in the 1st PDF
4627 : ! component.
4628 0 : stats_metadata%icorr_w_hm_1_n(hm_idx) = k
4629 :
4630 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_1_n(hm_idx), &
4631 : var_name="corr_w_"//trim(hm_type(1:2))//"_1_n", &
4632 : var_description="Correlation (in-precip) " &
4633 : // "of w and ln " &
4634 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4635 : // " (1st PDF component) [-]", &
4636 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4637 :
4638 0 : k = k + 1
4639 :
4640 : ! The in-precip correlation of w and ln hm in the 2nd PDF
4641 : ! component.
4642 0 : stats_metadata%icorr_w_hm_2_n(hm_idx) = k
4643 :
4644 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_2_n(hm_idx), &
4645 : var_name="corr_w_"//trim(hm_type(1:2))//"_2_n", &
4646 : var_description="Correlation (in-precip) " &
4647 : // "of w and ln " &
4648 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4649 : // " (2nd PDF component) [-]", &
4650 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4651 :
4652 0 : k = k + 1
4653 :
4654 : enddo ! hm_idx = 1, hydromet_dim, 1
4655 :
4656 : case ( 'corr_w_Ncn_i_n' )
4657 :
4658 0 : stats_metadata%icorr_w_Ncn_1_n = k
4659 :
4660 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1_n, &
4661 : var_name="corr_w_Ncn_1_n", &
4662 : var_description="corr_w_Ncn_1_n, Correlation of w and " &
4663 : // "ln N_cn (1st PDF component)", &
4664 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4665 :
4666 0 : k = k + 1
4667 :
4668 0 : stats_metadata%icorr_w_Ncn_2_n = k
4669 :
4670 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2_n, &
4671 : var_name="corr_w_Ncn_2_n", &
4672 : var_description="corr_w_Ncn_2_n, Correlation of w and " &
4673 : // "ln N_cn (2nd PDF component)", &
4674 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4675 :
4676 0 : k = k + 1
4677 :
4678 : ! Correlation (in-precip) of chi and ln hm for each PDF component and
4679 : ! hydrometeor type.
4680 : case ( 'corr_chi_hm_i_n' )
4681 :
4682 0 : do hm_idx = 1, hydromet_dim, 1
4683 :
4684 0 : hm_type = hydromet_list(hm_idx)
4685 :
4686 : ! The in-precip correlation of chi and ln hm in the 1st PDF
4687 : ! component.
4688 0 : stats_metadata%icorr_chi_hm_1_n(hm_idx) = k
4689 :
4690 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_1_n(hm_idx), &
4691 : var_name="corr_chi_"//trim(hm_type(1:2)) &
4692 : // "_1_n", &
4693 : var_description="Correlation (in-precip) " &
4694 : // "of chi (s) and ln " &
4695 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4696 : // " (1st PDF component) [-]", &
4697 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4698 :
4699 0 : k = k + 1
4700 :
4701 : ! The in-precip correlation of chi(s) and ln hm in the 2nd PDF
4702 : ! component.
4703 0 : stats_metadata%icorr_chi_hm_2_n(hm_idx) = k
4704 :
4705 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_2_n(hm_idx), &
4706 : var_name="corr_chi_"//trim(hm_type(1:2)) &
4707 : // "_2_n", &
4708 : var_description="Correlation (in-precip) " &
4709 : // "of chi (s) and ln " &
4710 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4711 : // " (2nd PDF component) [-]", &
4712 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4713 :
4714 0 : k = k + 1
4715 :
4716 : enddo ! hm_idx = 1, hydromet_dim, 1
4717 :
4718 : case ( 'corr_chi_Ncn_i_n' )
4719 :
4720 0 : stats_metadata%icorr_chi_Ncn_1_n = k
4721 :
4722 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1_n, &
4723 : var_name="corr_chi_Ncn_1_n", &
4724 : var_description="corr_chi_Ncn_1_n, Correlation of chi (s) and " &
4725 : // "ln N_cn (1st PDF component)", &
4726 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4727 :
4728 0 : k = k + 1
4729 :
4730 0 : stats_metadata%icorr_chi_Ncn_2_n = k
4731 :
4732 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2_n, &
4733 : var_name="corr_chi_Ncn_2_n", &
4734 : var_description="corr_chi_Ncn_2_n, Correlation of chi (s) and " &
4735 : // "ln N_cn (2nd PDF component)", &
4736 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4737 :
4738 0 : k = k + 1
4739 :
4740 : ! Correlation (in-precip) of eta and ln hm for each PDF component and
4741 : ! hydrometeor type.
4742 : case ( 'corr_eta_hm_i_n' )
4743 :
4744 0 : do hm_idx = 1, hydromet_dim, 1
4745 :
4746 0 : hm_type = hydromet_list(hm_idx)
4747 :
4748 : ! The in-precip correlation of eta and ln hm in the 1st PDF
4749 : ! component.
4750 0 : stats_metadata%icorr_eta_hm_1_n(hm_idx) = k
4751 :
4752 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_1_n(hm_idx), &
4753 : var_name="corr_eta_"//trim( hm_type(1:2) ) &
4754 : // "_1_n", &
4755 : var_description="Correlation (in-precip) " &
4756 : // "of eta (t) and ln " &
4757 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4758 : // " (1st PDF component) [-]", &
4759 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4760 :
4761 0 : k = k + 1
4762 :
4763 : ! The in-precip correlation of eta and ln hm in the 2nd PDF
4764 : ! component.
4765 0 : stats_metadata%icorr_eta_hm_2_n(hm_idx) = k
4766 :
4767 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_2_n(hm_idx), &
4768 : var_name="corr_eta_"//trim( hm_type(1:2) ) &
4769 : // "_2_n", &
4770 : var_description="Correlation (in-precip) " &
4771 : // "of eta(t) and ln " &
4772 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4773 : // " (2nd PDF component) [-]", &
4774 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4775 :
4776 0 : k = k + 1
4777 :
4778 : enddo ! hm_idx = 1, hydromet_dim, 1
4779 :
4780 : case ( 'corr_eta_Ncn_i_n' )
4781 :
4782 0 : stats_metadata%icorr_eta_Ncn_1_n = k
4783 :
4784 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1_n, &
4785 : var_name="corr_eta_Ncn_1_n", &
4786 : var_description="corr_eta_Ncn_1_n, Correlation of eta (t) and " &
4787 : // "ln N_cn (1st PDF component)", &
4788 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4789 :
4790 0 : k = k + 1
4791 :
4792 0 : stats_metadata%icorr_eta_Ncn_2_n = k
4793 :
4794 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2_n, &
4795 : var_name="corr_eta_Ncn_2_n", &
4796 : var_description="corr_eta_Ncn_2_n, Correlation of eta (t) and " &
4797 : // "ln N_cn (2nd PDF component)", &
4798 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4799 :
4800 0 : k = k + 1
4801 :
4802 : ! Correlation (in-precip) of ln Ncn and ln hm for each PDF component
4803 : ! and hydrometeor type.
4804 : case ( 'corr_Ncn_hm_i_n' )
4805 :
4806 0 : do hm_idx = 1, hydromet_dim, 1
4807 :
4808 0 : hm_type = hydromet_list(hm_idx)
4809 :
4810 : ! The in-precip correlation of ln Ncn and ln hm in the 1st PDF
4811 : ! component.
4812 0 : stats_metadata%icorr_Ncn_hm_1_n(hm_idx) = k
4813 :
4814 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1_n(hm_idx), &
4815 : var_name="corr_Ncn_"//trim(hm_type(1:2)) &
4816 : // "_1_n", &
4817 : var_description="Correlation (in-precip) " &
4818 : // "of ln N_cn and ln " &
4819 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4820 : // " (1st PDF component) [-]", &
4821 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4822 :
4823 0 : k = k + 1
4824 :
4825 : ! The in-precip correlation of ln Ncn and ln hm in the 2nd PDF
4826 : ! component.
4827 0 : stats_metadata%icorr_Ncn_hm_2_n(hm_idx) = k
4828 :
4829 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2_n(hm_idx), &
4830 : var_name="corr_Ncn_"//trim(hm_type(1:2)) &
4831 : // "_2_n", &
4832 : var_description="Correlation (in-precip) " &
4833 : // "of ln N_cn and ln " &
4834 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4835 : // " (2nd PDF component) [-]", &
4836 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4837 :
4838 0 : k = k + 1
4839 :
4840 : enddo ! hm_idx = 1, hydromet_dim, 1
4841 :
4842 : ! Correlation (in-precip) of ln hmx and ln hmy (hmx and hmy are two
4843 : ! different hydrometeors) for each PDF component and hydrometeor type.
4844 : case ( 'corr_hmx_hmy_i_n' )
4845 :
4846 0 : do hmx_idx = 1, hydromet_dim, 1
4847 :
4848 0 : hmx_type = hydromet_list(hmx_idx)
4849 :
4850 0 : do hmy_idx = hmx_idx+1, hydromet_dim, 1
4851 :
4852 0 : hmy_type = hydromet_list(hmy_idx)
4853 :
4854 : ! The in-precip correlation of ln hmx and ln hmy in the 1st
4855 : ! PDF component.
4856 0 : stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx) = k
4857 :
4858 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx), &
4859 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4860 : // trim( hmy_type(1:2) )//"_1_n", &
4861 : var_description="Correlation (in-precip) " &
4862 : // "of ln " &
4863 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4864 : // " and ln " &
4865 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4866 : // " (1st PDF component) [-]", &
4867 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4868 :
4869 0 : k = k + 1
4870 :
4871 : ! The in-precip correlation of ln hmx and ln hmy in the 2nd
4872 : ! PDF component.
4873 0 : stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx) = k
4874 :
4875 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx), &
4876 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4877 : // trim( hmy_type(1:2) )//"_2_n", &
4878 : var_description="Correlation (in-precip) " &
4879 : // "of ln " &
4880 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4881 : // " and ln " &
4882 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4883 : // " (2nd PDF component) [-]", &
4884 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4885 :
4886 0 : k = k + 1
4887 :
4888 : enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
4889 :
4890 : enddo ! hmx_idx = 1, hydromet_dim, 1
4891 :
4892 : ! Third-order mixed moment < w'^2 hm' >, where hm is a hydrometeor.
4893 : case ('wp2hmp')
4894 :
4895 0 : do hm_idx = 1, hydromet_dim, 1
4896 :
4897 0 : hm_type = hydromet_list(hm_idx)
4898 :
4899 0 : stats_metadata%iwp2hmp(hm_idx) = k
4900 :
4901 0 : if ( l_mix_rat_hm(hm_idx) ) then
4902 :
4903 0 : call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
4904 : var_name="wp2"//trim( hm_type(1:2) )//"p", &
4905 : var_description="Third-order moment < w'^2 " &
4906 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4907 : // "' > [(m/s)^2 kg/kg]", &
4908 : var_units="(m/s)^2 kg/kg", &
4909 0 : l_silhs=.false., grid_kind=stats_zt )
4910 :
4911 : else ! Concentration
4912 :
4913 0 : call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
4914 : var_name="wp2"//trim( hm_type(1:2) )//"p", &
4915 : var_description="Third-order moment < w'^2 " &
4916 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4917 : // "' > [(m/s)^2 num/kg]", &
4918 : var_units="(m/s)^2 num/kg", &
4919 0 : l_silhs=.false., grid_kind=stats_zt )
4920 :
4921 : endif ! l_mix_rat_hm(hm_idx)
4922 :
4923 0 : k = k + 1
4924 :
4925 : enddo ! hm_idx = 1, hydromet_dim, 1
4926 :
4927 : case ('cloud_frac_refined')
4928 0 : stats_metadata%icloud_frac_refined = k
4929 : call stat_assign( var_index=stats_metadata%icloud_frac_refined, var_name="cloud_frac_refined", &
4930 : var_description="cloud_frac_refined, Cloud fraction computed on " &
4931 : // "refined grid", &
4932 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4933 0 : k = k + 1
4934 :
4935 : case ('rcm_refined')
4936 0 : stats_metadata%ircm_refined = k
4937 : call stat_assign( var_index=stats_metadata%ircm_refined, var_name="rcm_refined", &
4938 : var_description="rcm_refined, Cloud water mixing ratio computed on " &
4939 : // "refined grid &
4940 0 : &[kg/kg]", var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt)
4941 0 : k = k + 1
4942 :
4943 : case ('hl_on_Cp_residual')
4944 0 : stats_metadata%ihl_on_Cp_residual = k
4945 : call stat_assign( var_index=stats_metadata%ihl_on_Cp_residual, var_name="hl_on_Cp_residual", &
4946 : var_description="hl_on_Cp_residual, Residual change in HL/Cp from " &
4947 : // "Morrison microphysics &
4948 : ¬ due to sedimentation", &
4949 0 : var_units="K", l_silhs=.true., grid_kind=stats_zt)
4950 0 : k = k + 1
4951 :
4952 : case ('qto_residual')
4953 0 : stats_metadata%iqto_residual = k
4954 : call stat_assign( var_index=stats_metadata%iqto_residual, var_name="qto_residual", &
4955 : var_description="qto_residual, Residual change in total water " &
4956 : // "from Morrison &
4957 : µphysics not due to sedimentation", &
4958 0 : var_units="kg/kg", l_silhs=.true., grid_kind=stats_zt)
4959 0 : k = k + 1
4960 :
4961 : case ( 'sclrm' )
4962 0 : do j = 1, sclr_dim, 1
4963 0 : write(sclr_idx, * ) j
4964 0 : sclr_idx = adjustl(sclr_idx)
4965 0 : stats_metadata%isclrm(j) = k
4966 0 : call stat_assign( var_index=stats_metadata%isclrm(j), var_name="sclr"//trim(sclr_idx)//"m", &
4967 : var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
4968 0 : l_silhs=.false., grid_kind=stats_zt )
4969 0 : k = k + 1
4970 : end do
4971 :
4972 : case ( 'sclrm_f' )
4973 0 : do j = 1, sclr_dim, 1
4974 0 : write(sclr_idx, * ) j
4975 0 : sclr_idx = adjustl(sclr_idx)
4976 0 : stats_metadata%isclrm_f(j) = k
4977 0 : call stat_assign( var_index=stats_metadata%isclrm_f(j), var_name="sclr"//trim(sclr_idx)//"m_f", &
4978 : var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
4979 0 : l_silhs=.false., grid_kind=stats_zt )
4980 0 : k = k + 1
4981 : end do
4982 :
4983 : case ( 'edsclrm' )
4984 0 : do j = 1, edsclr_dim, 1
4985 0 : write(sclr_idx, * ) j
4986 0 : sclr_idx = adjustl(sclr_idx)
4987 0 : stats_metadata%iedsclrm(j) = k
4988 0 : call stat_assign( var_index=stats_metadata%iedsclrm(j), var_name="edsclr"//trim(sclr_idx)//"m", &
4989 : var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
4990 0 : l_silhs=.false., grid_kind=stats_zt )
4991 0 : k = k + 1
4992 : end do
4993 :
4994 : case ( 'edsclrm_f' )
4995 0 : do j = 1, edsclr_dim, 1
4996 0 : write(sclr_idx, * ) j
4997 0 : sclr_idx = adjustl(sclr_idx)
4998 0 : stats_metadata%iedsclrm_f(j) = k
4999 0 : call stat_assign( var_index=stats_metadata%iedsclrm_f(j), var_name="edsclr"//trim(sclr_idx)//"m_f", &
5000 : var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
5001 0 : l_silhs=.false., grid_kind=stats_zt )
5002 0 : k = k + 1
5003 : end do
5004 :
5005 : case default
5006 :
5007 0 : write(fstderr,*) 'Error: unrecognized variable in vars_zt: ', trim( vars_zt(i) )
5008 0 : l_error = .true. ! This will stop the run.
5009 :
5010 : end select ! trim( vars_zt )
5011 :
5012 :
5013 : end do ! i=1,stats_zt%num_output_fields
5014 :
5015 :
5016 0 : return
5017 :
5018 : end subroutine stats_init_zt
5019 :
5020 : !===============================================================================
5021 :
5022 : end module stats_zt_module
|