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( hydromet_dim, sclr_dim, edsclr_dim, & ! intent(in)
19 0 : hydromet_list, l_mix_rat_hm, & ! intent(in)
20 0 : vars_zt, & ! intent(in)
21 : l_error, & ! intent(inout)
22 : stats_metadata, stats_zt ) ! intent(inout)
23 :
24 : ! Description:
25 : ! Initializes array indices for stats_zt
26 :
27 : ! Note:
28 : ! All code that is within subroutine stats_init_zt, including variable
29 : ! allocation code, is not called if l_stats is false. This subroutine is
30 : ! called only when l_stats is true.
31 :
32 : !-----------------------------------------------------------------------
33 :
34 : use constants_clubb, only: &
35 : fstderr ! Constant(s)
36 :
37 : use stats_type_utilities, only: &
38 : stat_assign ! Procedure
39 :
40 : use stats_type, only: &
41 : stats ! Type
42 :
43 : use stats_variables, only: &
44 : stats_metadata_type
45 :
46 : implicit none
47 :
48 : ! External
49 : intrinsic :: trim
50 :
51 : ! Local Constants
52 :
53 : !--------------------- Input Variable ---------------------
54 : integer, intent(in) :: &
55 : hydromet_dim, &
56 : sclr_dim, &
57 : edsclr_dim
58 :
59 : character(len=10), dimension(hydromet_dim), intent(in) :: &
60 : hydromet_list
61 :
62 : logical, dimension(hydromet_dim), intent(in) :: &
63 : l_mix_rat_hm ! if true, then the quantity is a hydrometeor mixing ratio
64 :
65 : character(len= * ), dimension(nvarmax_zt), intent(in) :: &
66 : vars_zt
67 :
68 : !--------------------- InOut Variables ---------------------
69 : type (stats_metadata_type), intent(inout) :: &
70 : stats_metadata
71 :
72 : type (stats), target, intent(inout) :: &
73 : stats_zt
74 :
75 : logical, intent(inout) :: l_error
76 :
77 : !--------------------- Local Varables ---------------------
78 : integer :: tot_zt_loops
79 :
80 : integer :: i, j, k
81 :
82 : integer :: hm_idx, hmx_idx, hmy_idx
83 :
84 : character(len=10) :: hm_type, hmx_type, hmy_type
85 :
86 : character(len=50) :: sclr_idx
87 :
88 : !--------------------- Begin Code ---------------------
89 :
90 : ! The default initialization for array indices for stats_zt is zero (see module
91 : ! stats_variables)
92 :
93 : ! If any of the index arrays are allocated, then we have called this before
94 : ! to set up stats_metadata, so all we want to do is set stats_zt via stats_assign
95 0 : if ( .not. allocated(stats_metadata%ihm_1) ) then
96 :
97 : ! Allocate and initialize hydrometeor statistical variables.
98 0 : allocate( stats_metadata%ihm_1(1:hydromet_dim) )
99 0 : allocate( stats_metadata%ihm_2(1:hydromet_dim) )
100 0 : allocate( stats_metadata%imu_hm_1(1:hydromet_dim) )
101 0 : allocate( stats_metadata%imu_hm_2(1:hydromet_dim) )
102 0 : allocate( stats_metadata%imu_hm_1_n(1:hydromet_dim) )
103 0 : allocate( stats_metadata%imu_hm_2_n(1:hydromet_dim) )
104 0 : allocate( stats_metadata%isigma_hm_1(1:hydromet_dim) )
105 0 : allocate( stats_metadata%isigma_hm_2(1:hydromet_dim) )
106 0 : allocate( stats_metadata%isigma_hm_1_n(1:hydromet_dim) )
107 0 : allocate( stats_metadata%isigma_hm_2_n(1:hydromet_dim) )
108 :
109 0 : allocate( stats_metadata%icorr_w_hm_1(1:hydromet_dim) )
110 0 : allocate( stats_metadata%icorr_w_hm_2(1:hydromet_dim) )
111 0 : allocate( stats_metadata%icorr_chi_hm_1(1:hydromet_dim) )
112 0 : allocate( stats_metadata%icorr_chi_hm_2(1:hydromet_dim) )
113 0 : allocate( stats_metadata%icorr_eta_hm_1(1:hydromet_dim) )
114 0 : allocate( stats_metadata%icorr_eta_hm_2(1:hydromet_dim) )
115 0 : allocate( stats_metadata%icorr_Ncn_hm_1(1:hydromet_dim) )
116 0 : allocate( stats_metadata%icorr_Ncn_hm_2(1:hydromet_dim) )
117 0 : allocate( stats_metadata%icorr_hmx_hmy_1(1:hydromet_dim,1:hydromet_dim) )
118 0 : allocate( stats_metadata%icorr_hmx_hmy_2(1:hydromet_dim,1:hydromet_dim) )
119 :
120 0 : allocate( stats_metadata%icorr_w_hm_1_n(1:hydromet_dim) )
121 0 : allocate( stats_metadata%icorr_w_hm_2_n(1:hydromet_dim) )
122 0 : allocate( stats_metadata%icorr_chi_hm_1_n(1:hydromet_dim) )
123 0 : allocate( stats_metadata%icorr_chi_hm_2_n(1:hydromet_dim) )
124 0 : allocate( stats_metadata%icorr_eta_hm_1_n(1:hydromet_dim) )
125 0 : allocate( stats_metadata%icorr_eta_hm_2_n(1:hydromet_dim) )
126 0 : allocate( stats_metadata%icorr_Ncn_hm_1_n(1:hydromet_dim) )
127 0 : allocate( stats_metadata%icorr_Ncn_hm_2_n(1:hydromet_dim) )
128 0 : allocate( stats_metadata%icorr_hmx_hmy_1_n(1:hydromet_dim,1:hydromet_dim) )
129 0 : allocate( stats_metadata%icorr_hmx_hmy_2_n(1:hydromet_dim,1:hydromet_dim) )
130 :
131 0 : allocate( stats_metadata%ihmp2_zt(1:hydromet_dim) )
132 :
133 0 : allocate( stats_metadata%iwp2hmp(1:hydromet_dim) )
134 :
135 0 : stats_metadata%ihm_1(:) = 0
136 0 : stats_metadata%ihm_2(:) = 0
137 0 : stats_metadata%imu_hm_1(:) = 0
138 0 : stats_metadata%imu_hm_2(:) = 0
139 0 : stats_metadata%imu_hm_1_n(:) = 0
140 0 : stats_metadata%imu_hm_2_n(:) = 0
141 0 : stats_metadata%isigma_hm_1(:) = 0
142 0 : stats_metadata%isigma_hm_2(:) = 0
143 0 : stats_metadata%isigma_hm_1_n(:) = 0
144 0 : stats_metadata%isigma_hm_2_n(:) = 0
145 :
146 0 : stats_metadata%icorr_w_hm_1(:) = 0
147 0 : stats_metadata%icorr_w_hm_2(:) = 0
148 0 : stats_metadata%icorr_chi_hm_1(:) = 0
149 0 : stats_metadata%icorr_chi_hm_2(:) = 0
150 0 : stats_metadata%icorr_eta_hm_1(:) = 0
151 0 : stats_metadata%icorr_eta_hm_2(:) = 0
152 0 : stats_metadata%icorr_Ncn_hm_1(:) = 0
153 0 : stats_metadata%icorr_Ncn_hm_2(:) = 0
154 0 : stats_metadata%icorr_hmx_hmy_1(:,:) = 0
155 0 : stats_metadata%icorr_hmx_hmy_2(:,:) = 0
156 :
157 0 : stats_metadata%icorr_w_hm_1_n(:) = 0
158 0 : stats_metadata%icorr_w_hm_2_n(:) = 0
159 0 : stats_metadata%icorr_chi_hm_1_n(:) = 0
160 0 : stats_metadata%icorr_chi_hm_2_n(:) = 0
161 0 : stats_metadata%icorr_eta_hm_1_n(:) = 0
162 0 : stats_metadata%icorr_eta_hm_2_n(:) = 0
163 0 : stats_metadata%icorr_Ncn_hm_1_n(:) = 0
164 0 : stats_metadata%icorr_Ncn_hm_2_n(:) = 0
165 0 : stats_metadata%icorr_hmx_hmy_1_n(:,:) = 0
166 0 : stats_metadata%icorr_hmx_hmy_2_n(:,:) = 0
167 :
168 0 : stats_metadata%ihmp2_zt(:) = 0
169 :
170 0 : stats_metadata%iwp2hmp(:) = 0
171 :
172 : ! Allocate and then zero out passive scalar arrays
173 0 : allocate( stats_metadata%isclrm(1:sclr_dim) )
174 0 : allocate( stats_metadata%isclrm_f(1:sclr_dim) )
175 :
176 0 : stats_metadata%isclrm(:) = 0
177 0 : stats_metadata%isclrm_f(:) = 0
178 :
179 0 : allocate( stats_metadata%iedsclrm(1:edsclr_dim) )
180 0 : allocate( stats_metadata%iedsclrm_f(1:edsclr_dim) )
181 :
182 0 : stats_metadata%iedsclrm(:) = 0
183 0 : stats_metadata%iedsclrm_f(:) = 0
184 :
185 : end if
186 :
187 : ! Assign pointers for statistics variables stats_zt using stat_assign
188 :
189 0 : tot_zt_loops = stats_zt%num_output_fields
190 :
191 0 : if ( any( vars_zt == "hm_i" ) ) then
192 : ! Correct for number of variables found under "hm_i".
193 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
194 : ! for each hydrometeor.
195 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
196 : ! Add 1 for "hm_i" to the loop size.
197 0 : tot_zt_loops = tot_zt_loops + 1
198 : endif
199 0 : if ( any( vars_zt == "mu_hm_i" ) ) then
200 : ! Correct for number of variables found under "mu_hm_i".
201 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
202 : ! for each hydrometeor.
203 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
204 : ! Add 1 for "mu_hm_i" to the loop size.
205 0 : tot_zt_loops = tot_zt_loops + 1
206 : endif
207 0 : if ( any( vars_zt == "mu_Ncn_i" ) ) then
208 : ! Correct for number of variables found under "mu_Ncn_i".
209 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
210 0 : tot_zt_loops = tot_zt_loops - 2
211 : ! Add 1 for "mu_Ncn_i" to the loop size.
212 0 : tot_zt_loops = tot_zt_loops + 1
213 : endif
214 0 : if ( any( vars_zt == "mu_hm_i_n" ) ) then
215 : ! Correct for number of variables found under "mu_hm_i_n".
216 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
217 : ! for each hydrometeor.
218 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
219 : ! Add 1 for "mu_hm_i_n" to the loop size.
220 0 : tot_zt_loops = tot_zt_loops + 1
221 : endif
222 0 : if ( any( vars_zt == "mu_Ncn_i_n" ) ) then
223 : ! Correct for number of variables found under "mu_Ncn_i_n".
224 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
225 0 : tot_zt_loops = tot_zt_loops - 2
226 : ! Add 1 for "mu_Ncn_i_n" to the loop size.
227 0 : tot_zt_loops = tot_zt_loops + 1
228 : endif
229 0 : if ( any( vars_zt == "sigma_hm_i" ) ) then
230 : ! Correct for number of variables found under "sigma_hm_i".
231 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
232 : ! for each hydrometeor.
233 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
234 : ! Add 1 for "sigma_hm_i" to the loop size.
235 0 : tot_zt_loops = tot_zt_loops + 1
236 : endif
237 0 : if ( any( vars_zt == "sigma_Ncn_i" ) ) then
238 : ! Correct for number of variables found under "sigma_Ncn_i".
239 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
240 0 : tot_zt_loops = tot_zt_loops - 2
241 : ! Add 1 for "sigma_Ncn_i" to the loop size.
242 0 : tot_zt_loops = tot_zt_loops + 1
243 : endif
244 0 : if ( any( vars_zt == "sigma_hm_i_n" ) ) then
245 : ! Correct for number of variables found under "sigma_hm_i_n".
246 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
247 : ! for each hydrometeor.
248 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
249 : ! Add 1 for "sigma_hm_i_n" to the loop size.
250 0 : tot_zt_loops = tot_zt_loops + 1
251 : endif
252 0 : if ( any( vars_zt == "sigma_Ncn_i_n" ) ) then
253 : ! Correct for number of variables found under "sigma_Ncn_i_n".
254 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
255 0 : tot_zt_loops = tot_zt_loops - 2
256 : ! Add 1 for "sigma_Ncn_i_n" to the loop size.
257 0 : tot_zt_loops = tot_zt_loops + 1
258 : endif
259 :
260 0 : if ( any( vars_zt == "corr_w_hm_i" ) ) then
261 : ! Correct for number of variables found under "corr_whm_i".
262 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
263 : ! for each hydrometeor.
264 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
265 : ! Add 1 for "corr_whm_i" to the loop size.
266 0 : tot_zt_loops = tot_zt_loops + 1
267 : endif
268 0 : if ( any( vars_zt == "corr_w_Ncn_i" ) ) then
269 : ! Correct for number of variables found under "corr_wNcn_i".
270 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
271 0 : tot_zt_loops = tot_zt_loops - 2
272 : ! Add 1 for "corr_wNcn_i" to the loop size.
273 0 : tot_zt_loops = tot_zt_loops + 1
274 : endif
275 0 : if ( any( vars_zt == "corr_chi_hm_i" ) ) then
276 : ! Correct for number of variables found under "corr_chi_hm_i".
277 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
278 : ! for each hydrometeor.
279 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
280 : ! Add 1 for "corr_chi_hm_i" to the loop size.
281 0 : tot_zt_loops = tot_zt_loops + 1
282 : endif
283 0 : if ( any( vars_zt == "corr_chi_Ncn_i" ) ) then
284 : ! Correct for number of variables found under "corr_chi_Ncn_i".
285 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
286 0 : tot_zt_loops = tot_zt_loops - 2
287 : ! Add 1 for "corr_chi_Ncn_i" to the loop size.
288 0 : tot_zt_loops = tot_zt_loops + 1
289 : endif
290 0 : if ( any( vars_zt == "corr_eta_hm_i" ) ) then
291 : ! Correct for number of variables found under "corr_eta_hm_i".
292 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
293 : ! for each hydrometeor.
294 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
295 : ! Add 1 for "corr_eta_hm_i" to the loop size.
296 0 : tot_zt_loops = tot_zt_loops + 1
297 : endif
298 0 : if ( any( vars_zt == "corr_eta_Ncn_i" ) ) then
299 : ! Correct for number of variables found under "corr_eta_Ncn_i".
300 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
301 0 : tot_zt_loops = tot_zt_loops - 2
302 : ! Add 1 for "corr_eta_Ncn_i" to the loop size.
303 0 : tot_zt_loops = tot_zt_loops + 1
304 : endif
305 0 : if ( any( vars_zt == "corr_Ncn_hm_i" ) ) then
306 : ! Correct for number of variables found under "corr_Ncnhm_i".
307 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
308 : ! for each hydrometeor.
309 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
310 : ! Add 1 for "corr_Ncnhm_i" to the loop size.
311 0 : tot_zt_loops = tot_zt_loops + 1
312 : endif
313 0 : if ( any( vars_zt == "corr_hmx_hmy_i" ) ) then
314 : ! Correct for number of variables found under "corr_hmxhmy_i".
315 : ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
316 : ! number of correlations of two hydrometeors, which is found by:
317 : ! (1/2) * hydromet_dim * ( hydromet_dim - 1 ); from the loop size.
318 0 : tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
319 : ! Add 1 for "corr_hmxhmy_i" to the loop size.
320 0 : tot_zt_loops = tot_zt_loops + 1
321 : endif
322 :
323 0 : if ( any( vars_zt == "corr_w_hm_i_n" ) ) then
324 : ! Correct for number of variables found under "corr_whm_i_n".
325 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
326 : ! for each hydrometeor.
327 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
328 : ! Add 1 for "corr_whm_i_n" to the loop size.
329 0 : tot_zt_loops = tot_zt_loops + 1
330 : endif
331 0 : if ( any( vars_zt == "corr_w_Ncn_i_n" ) ) then
332 : ! Correct for number of variables found under "corr_wNcn_i_n".
333 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
334 0 : tot_zt_loops = tot_zt_loops - 2
335 : ! Add 1 for "corr_wNcn_i_n" to the loop size.
336 0 : tot_zt_loops = tot_zt_loops + 1
337 : endif
338 0 : if ( any( vars_zt == "corr_chi_hm_i_n" ) ) then
339 : ! Correct for number of variables found under "corr_chi_hm_i_n".
340 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
341 : ! for each hydrometeor.
342 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
343 : ! Add 1 for "corr_chi_hm_i_n" to the loop size.
344 0 : tot_zt_loops = tot_zt_loops + 1
345 : endif
346 0 : if ( any( vars_zt == "corr_chi_Ncn_i_n" ) ) then
347 : ! Correct for number of variables found under "corr_chi_Ncn_i_n".
348 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
349 0 : tot_zt_loops = tot_zt_loops - 2
350 : ! Add 1 for "corr_chi_Ncn_i_n" to the loop size.
351 0 : tot_zt_loops = tot_zt_loops + 1
352 : endif
353 0 : if ( any( vars_zt == "corr_eta_hm_i_n" ) ) then
354 : ! Correct for number of variables found under "corr_eta_hm_i_n".
355 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
356 : ! for each hydrometeor.
357 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
358 : ! Add 1 for "corr_eta_hm_i_n" to the loop size.
359 0 : tot_zt_loops = tot_zt_loops + 1
360 : endif
361 0 : if ( any( vars_zt == "corr_eta_Ncn_i_n" ) ) then
362 : ! Correct for number of variables found under "corr_eta_Ncn_i_n".
363 : ! Subtract 2 from the loop size (1st PDF comp. and 2nd PDF comp.).
364 0 : tot_zt_loops = tot_zt_loops - 2
365 : ! Add 1 for "corr_eta_Ncn_i_n" to the loop size.
366 0 : tot_zt_loops = tot_zt_loops + 1
367 : endif
368 0 : if ( any( vars_zt == "corr_Ncn_hm_i_n" ) ) then
369 : ! Correct for number of variables found under "corr_Ncnhm_i_n".
370 : ! Subtract 2 from the loop size (1st PDF component and 2nd PDF component)
371 : ! for each hydrometeor.
372 0 : tot_zt_loops = tot_zt_loops - 2 * hydromet_dim
373 : ! Add 1 for "corr_Ncnhm_i_n" to the loop size.
374 0 : tot_zt_loops = tot_zt_loops + 1
375 : endif
376 0 : if ( any( vars_zt == "corr_hmx_hmy_i_n" ) ) then
377 : ! Correct for number of variables found under "corr_hmxhmy_i_n".
378 : ! Subtract 2 (1st PDF component and 2nd PDF component) multipled by the
379 : ! number of normal space correlations of two hydrometeors, which is found
380 : ! by: (1/2) * hydromet_dim * ( hydromet_dim - 1 );
381 : ! from the loop size.
382 0 : tot_zt_loops = tot_zt_loops - hydromet_dim * ( hydromet_dim - 1 )
383 : ! Add 1 for "corr_hmxhmy_i_n" to the loop size.
384 0 : tot_zt_loops = tot_zt_loops + 1
385 : endif
386 :
387 0 : if ( any( vars_zt == "hmp2_zt" ) ) then
388 : ! Correct for number of variables found under "hmp2_zt".
389 : ! Subtract 1 from the loop size for each hydrometeor.
390 0 : tot_zt_loops = tot_zt_loops - hydromet_dim
391 : ! Add 1 for "hmp2_zt" to the loop size.
392 0 : tot_zt_loops = tot_zt_loops + 1
393 : endif
394 :
395 0 : if ( any( vars_zt == "wp2hmp" ) ) then
396 : ! Correct for number of variables found under "wp2hmp".
397 : ! Subtract 1 from the loop size for each hydrometeor.
398 0 : tot_zt_loops = tot_zt_loops - hydromet_dim
399 : ! Add 1 for "wp2hmp" to the loop size.
400 0 : tot_zt_loops = tot_zt_loops + 1
401 : endif
402 :
403 0 : if ( any( vars_zt == "sclrm" ) ) then
404 : ! Correct for number of variables found under "sclrm".
405 : ! Subtract 1 from the loop size for each scalar.
406 0 : tot_zt_loops = tot_zt_loops - sclr_dim
407 :
408 : ! Add 1 for "sclrm" to the loop size.
409 0 : tot_zt_loops = tot_zt_loops + 1
410 : endif
411 :
412 0 : if ( any( vars_zt == "sclrm_f" ) ) then
413 : ! Correct for number of variables found under "sclrm_f".
414 : ! Subtract 1 from the loop size for each scalar.
415 0 : tot_zt_loops = tot_zt_loops - sclr_dim
416 : ! Add 1 for "sclrm_f" to the loop size.
417 0 : tot_zt_loops = tot_zt_loops + 1
418 : endif
419 :
420 0 : if ( any( vars_zt == "edsclrm" ) ) then
421 : ! Correct for number of variables found under "edsclrm".
422 : ! Subtract 1 from the loop size for each scalar.
423 0 : tot_zt_loops = tot_zt_loops - edsclr_dim
424 : ! Add 1 for "edsclrm" to the loop size.
425 0 : tot_zt_loops = tot_zt_loops + 1
426 : endif
427 :
428 0 : if ( any( vars_zt == "edsclrm_f" ) ) then
429 : ! Correct for number of variables found under "edsclrm_f".
430 : ! Subtract 1 from the loop size for each scalar.
431 0 : tot_zt_loops = tot_zt_loops - edsclr_dim
432 : ! Add 1 for "edsclrm_f" to the loop size.
433 0 : tot_zt_loops = tot_zt_loops + 1
434 : endif
435 :
436 0 : k = 1
437 :
438 0 : do i = 1, tot_zt_loops
439 :
440 0 : select case ( trim( vars_zt(i) ) )
441 : case ('thlm')
442 0 : stats_metadata%ithlm = k
443 : call stat_assign( var_index=stats_metadata%ithlm, var_name="thlm", &
444 : var_description="thlm, Liquid water potential temperature (theta_l)", var_units="K", &
445 0 : l_silhs=.false., grid_kind=stats_zt )
446 0 : k = k + 1
447 :
448 : case ('T_in_K')
449 0 : stats_metadata%iT_in_K = k
450 : call stat_assign( var_index=stats_metadata%iT_in_K, var_name="T_in_K", &
451 : var_description="T_in_K, Absolute temperature", var_units="K", l_silhs=.false., &
452 0 : grid_kind=stats_zt )
453 0 : k = k + 1
454 :
455 : case ('thvm')
456 0 : stats_metadata%ithvm = k
457 : call stat_assign( var_index=stats_metadata%ithvm, var_name="thvm", &
458 : var_description="thvm, Virtual potential temperature", &
459 : var_units="K", l_silhs=.false., &
460 0 : grid_kind=stats_zt )
461 0 : k = k + 1
462 :
463 : case ('rtm')
464 0 : stats_metadata%irtm = k
465 :
466 : call stat_assign( var_index=stats_metadata%irtm, var_name="rtm", &
467 : var_description="rtm, Total (vapor+liquid) water mixing ratio", &
468 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
469 :
470 0 : k = k + 1
471 :
472 : case ('rcm')
473 0 : stats_metadata%ircm = k
474 : call stat_assign( var_index=stats_metadata%ircm, var_name="rcm", &
475 : var_description="rcm, Cloud water mixing ratio", var_units="kg/kg", &
476 0 : l_silhs=.false., grid_kind=stats_zt )
477 0 : k = k + 1
478 :
479 : case ('rfrzm')
480 0 : stats_metadata%irfrzm = k
481 : call stat_assign( var_index=stats_metadata%irfrzm, var_name="rfrzm", &
482 : var_description="rfrzm, Total ice phase water mixing ratio", var_units="kg/kg", &
483 0 : l_silhs=.false., grid_kind=stats_zt )
484 0 : k = k + 1
485 :
486 : case ('rvm')
487 0 : stats_metadata%irvm = k
488 : call stat_assign( var_index=stats_metadata%irvm, var_name="rvm", &
489 : var_description="rvm, Vapor water mixing ratio", var_units="kg/kg", &
490 0 : l_silhs=.false., grid_kind=stats_zt )
491 0 : k = k + 1
492 : case ('rel_humidity')
493 0 : stats_metadata%irel_humidity = k
494 : call stat_assign( var_index=stats_metadata%irel_humidity, var_name="rel_humidity", &
495 : var_description="rel_humidity, Relative humidity w.r.t. liquid (between 0 and 1)", &
496 0 : var_units="[-]", l_silhs=.false., grid_kind=stats_zt )
497 0 : k = k + 1
498 : case ('um')
499 0 : stats_metadata%ium = k
500 : call stat_assign( var_index=stats_metadata%ium, var_name="um", &
501 : var_description="u_bar, Grid-mean eastward (u) wind", &
502 : var_units="m/s", l_silhs=.false., &
503 0 : grid_kind=stats_zt )
504 0 : k = k + 1
505 : case ('vm')
506 0 : stats_metadata%ivm = k
507 : call stat_assign( var_index=stats_metadata%ivm, var_name="vm", &
508 : var_description="v_bar, Grid-mean northward (v) wind", &
509 : var_units="m/s", l_silhs=.false., &
510 0 : grid_kind=stats_zt )
511 0 : k = k + 1
512 : case ('wm_zt')
513 0 : stats_metadata%iwm_zt = k
514 : call stat_assign( var_index=stats_metadata%iwm_zt, var_name="wm_zt", &
515 : var_description="w_bar, Grid-mean upward (w) wind", &
516 : var_units="m/s", l_silhs=.false., &
517 0 : grid_kind=stats_zt )
518 0 : k = k + 1
519 : case ('um_ref')
520 0 : stats_metadata%ium_ref = k
521 : call stat_assign( var_index=stats_metadata%ium_ref, var_name="um_ref", &
522 : var_description="um_ref, Reference u wind", var_units="m/s", l_silhs=.false., &
523 0 : grid_kind=stats_zt )
524 0 : k = k + 1
525 : case ('vm_ref')
526 0 : stats_metadata%ivm_ref = k
527 : call stat_assign( var_index=stats_metadata%ivm_ref, var_name="vm_ref", &
528 : var_description="vm_ref, Reference v wind", var_units="m/s", l_silhs=.false., &
529 0 : grid_kind=stats_zt )
530 0 : k = k + 1
531 : case ('ug')
532 0 : stats_metadata%iug = k
533 : call stat_assign( var_index=stats_metadata%iug, var_name="ug", &
534 : var_description="ug, u geostrophic wind", var_units="m/s", l_silhs=.false., &
535 0 : grid_kind=stats_zt )
536 0 : k = k + 1
537 : case ('vg')
538 0 : stats_metadata%ivg = k
539 : call stat_assign( var_index=stats_metadata%ivg, var_name="vg", &
540 : var_description="vg, v geostrophic wind", var_units="m/s", l_silhs=.false., &
541 0 : grid_kind=stats_zt )
542 0 : k = k + 1
543 : case ('cloud_frac')
544 0 : stats_metadata%icloud_frac = k
545 : call stat_assign( var_index=stats_metadata%icloud_frac, var_name="cloud_frac", &
546 : var_description="cloud_frac, Cloud fraction (between 0 and 1)", var_units="-", &
547 0 : l_silhs=.false., grid_kind=stats_zt )
548 0 : k = k + 1
549 :
550 : case ('ice_supersat_frac')
551 0 : stats_metadata%iice_supersat_frac = k
552 : call stat_assign( var_index=stats_metadata%iice_supersat_frac, var_name="ice_supersat_frac", &
553 : var_description="ice_supersat_frac, Ice cloud fraction (between 0 and 1)", &
554 : var_units="count", &
555 0 : l_silhs=.false., grid_kind=stats_zt )
556 0 : k = k + 1
557 :
558 : case ('rcm_in_layer')
559 0 : stats_metadata%ircm_in_layer = k
560 : call stat_assign( var_index=stats_metadata%ircm_in_layer, var_name="rcm_in_layer", &
561 : var_description="rcm_in_layer, rcm in cloud layer", &
562 : var_units="kg/kg", l_silhs=.false., &
563 0 : grid_kind=stats_zt )
564 0 : k = k + 1
565 :
566 : case ('rcm_in_cloud')
567 0 : stats_metadata%ircm_in_cloud = k
568 : call stat_assign( var_index=stats_metadata%ircm_in_cloud, var_name="rcm_in_cloud", &
569 : var_description="rcm_in_cloud, In-cloud value of rcm (for microphysics)", &
570 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
571 0 : k = k + 1
572 :
573 : case ('cloud_cover')
574 0 : stats_metadata%icloud_cover = k
575 : call stat_assign( var_index=stats_metadata%icloud_cover, var_name="cloud_cover", &
576 : var_description="cloud_cover, Cloud cover (between 0 and 1)", var_units="count", &
577 0 : l_silhs=.false., grid_kind=stats_zt )
578 0 : k = k + 1
579 : case ('p_in_Pa')
580 0 : stats_metadata%ip_in_Pa = k
581 : call stat_assign( var_index=stats_metadata%ip_in_Pa, var_name="p_in_Pa", &
582 : var_description="p_in_Pa, Pressure", &
583 0 : var_units="Pa", l_silhs=.false., grid_kind=stats_zt )
584 0 : k = k + 1
585 : case ('exner')
586 0 : stats_metadata%iexner = k
587 : call stat_assign( var_index=stats_metadata%iexner, var_name="exner", &
588 : var_description="exner, Exner function = (p/p0)**(rd/cp)", var_units="count", &
589 0 : l_silhs=.false., grid_kind=stats_zt )
590 0 : k = k + 1
591 : case ('rho_ds_zt')
592 0 : stats_metadata%irho_ds_zt = k
593 : call stat_assign( var_index=stats_metadata%irho_ds_zt, var_name="rho_ds_zt", &
594 : var_description="rho_ds_zt, Dry static base-state density", var_units="kg m^{-3}", &
595 0 : l_silhs=.false., grid_kind=stats_zt )
596 0 : k = k + 1
597 : case ('thv_ds_zt')
598 0 : stats_metadata%ithv_ds_zt = k
599 : call stat_assign( var_index=stats_metadata%ithv_ds_zt, var_name="thv_ds_zt", &
600 : var_description="thv_ds_zt, Dry base-state theta_v", &
601 : var_units="K", l_silhs=.false., &
602 0 : grid_kind=stats_zt )
603 0 : k = k + 1
604 : case ('Lscale')
605 0 : stats_metadata%iLscale = k
606 : call stat_assign( var_index=stats_metadata%iLscale, var_name="Lscale", &
607 : var_description="L, Turbulent mixing length", &
608 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
609 0 : k = k + 1
610 : case ('thlm_forcing')
611 0 : stats_metadata%ithlm_forcing = k
612 : call stat_assign( var_index=stats_metadata%ithlm_forcing, var_name="thlm_forcing", &
613 : var_description="thlm_forcing, thlm budget: thetal forcing " &
614 : // "(includes thlm_mc and radht)",&
615 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
616 0 : k = k + 1
617 : case ('thlm_mc')
618 0 : stats_metadata%ithlm_mc = k
619 : call stat_assign( var_index=stats_metadata%ithlm_mc, var_name="thlm_mc", &
620 : var_description="thlm_mc, Change in thlm due to microphysics (not in budget)", &
621 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
622 0 : k = k + 1
623 : case ('rtm_forcing')
624 0 : stats_metadata%irtm_forcing = k
625 : call stat_assign( var_index=stats_metadata%irtm_forcing, var_name="rtm_forcing", &
626 : var_description="rtm_forcing, rtm budget: rt forcing (includes rtm_mc)", &
627 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
628 0 : k = k + 1
629 :
630 : case ('rtm_mc')
631 0 : stats_metadata%irtm_mc = k
632 : call stat_assign( var_index=stats_metadata%irtm_mc, var_name="rtm_mc", &
633 : var_description="rtm_mc, Change in rt due to microphysics (not in budget)", &
634 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
635 0 : k = k + 1
636 :
637 : case ('rvm_mc')
638 0 : stats_metadata%irvm_mc = k
639 : call stat_assign( var_index=stats_metadata%irvm_mc, var_name="rvm_mc", &
640 : var_description="rvm_mc, Time tendency of vapor mixing ratio due to microphysics", &
641 0 : var_units="kg/(kg s)", l_silhs=.false., grid_kind=stats_zt )
642 0 : k = k + 1
643 :
644 : case ('rcm_mc')
645 0 : stats_metadata%ircm_mc = k
646 : call stat_assign( var_index=stats_metadata%ircm_mc, var_name="rcm_mc", &
647 : var_description="rcm_mc, Time tendency of liquid water mixing ratio " &
648 : // "due microphysics",&
649 0 : var_units="kg/kg/s", l_silhs=.false., grid_kind=stats_zt )
650 0 : k = k + 1
651 :
652 : case ('rcm_sd_mg_morr')
653 0 : stats_metadata%ircm_sd_mg_morr = k
654 : call stat_assign( var_index=stats_metadata%ircm_sd_mg_morr, var_name="rcm_sd_mg_morr", &
655 : var_description="rcm_sd_mg_morr, rcm sedimentation when using morrision or MG " &
656 : // "microphysics (not in budget, included in rcm_mc)", &
657 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
658 0 : k = k + 1
659 :
660 : case ('thlm_mfl_min')
661 0 : stats_metadata%ithlm_mfl_min = k
662 : call stat_assign( var_index=stats_metadata%ithlm_mfl_min, var_name="thlm_mfl_min", &
663 : var_description="thlm_mfl_min, Minimum allowable thlm", var_units="K", &
664 : l_silhs=.false., &
665 0 : grid_kind=stats_zt )
666 0 : k = k + 1
667 :
668 : case ('thlm_mfl_max')
669 0 : stats_metadata%ithlm_mfl_max = k
670 : call stat_assign( var_index=stats_metadata%ithlm_mfl_max, var_name="thlm_mfl_max", &
671 : var_description="thlm_mfl_max, Maximum allowable thlm", var_units="K", &
672 : l_silhs=.false., &
673 0 : grid_kind=stats_zt )
674 0 : k = k + 1
675 :
676 : case ('thlm_enter_mfl')
677 0 : stats_metadata%ithlm_enter_mfl = k
678 : call stat_assign( var_index=stats_metadata%ithlm_enter_mfl, var_name="thlm_enter_mfl", &
679 : var_description="thlm_enter_mfl, Thlm before flux-limiter", var_units="K", &
680 : l_silhs=.false., &
681 0 : grid_kind=stats_zt )
682 0 : k = k + 1
683 :
684 : case ('thlm_exit_mfl')
685 0 : stats_metadata%ithlm_exit_mfl = k
686 : call stat_assign( var_index=stats_metadata%ithlm_exit_mfl, var_name="thlm_exit_mfl", &
687 : var_description="thlm_exit_mfl, Thlm exiting flux-limiter", var_units="K", &
688 : l_silhs=.false., &
689 0 : grid_kind=stats_zt )
690 0 : k = k + 1
691 :
692 : case ('thlm_old')
693 0 : stats_metadata%ithlm_old = k
694 : call stat_assign( var_index=stats_metadata%ithlm_old, var_name="thlm_old", &
695 : var_description="thlm_old, Thlm at previous timestep", var_units="K", &
696 : l_silhs=.false., &
697 0 : grid_kind=stats_zt )
698 0 : k = k + 1
699 :
700 : case ('thlm_without_ta')
701 0 : stats_metadata%ithlm_without_ta = k
702 : call stat_assign( var_index=stats_metadata%ithlm_without_ta, var_name="thlm_without_ta", &
703 : var_description="thlm_without_ta, Thlm without turbulent advection contribution", &
704 : var_units="K", &
705 0 : l_silhs=.false., grid_kind=stats_zt )
706 0 : k = k + 1
707 :
708 : case ('rtm_mfl_min')
709 0 : stats_metadata%irtm_mfl_min = k
710 : call stat_assign( var_index=stats_metadata%irtm_mfl_min, var_name="rtm_mfl_min", &
711 : var_description="rtm_mfl_min, Minimum allowable rtm", var_units="kg/kg", &
712 0 : l_silhs=.false., grid_kind=stats_zt )
713 0 : k = k + 1
714 :
715 : case ('rtm_mfl_max')
716 0 : stats_metadata%irtm_mfl_max = k
717 : call stat_assign( var_index=stats_metadata%irtm_mfl_max, var_name="rtm_mfl_max", &
718 : var_description="rtm_mfl_max, Maximum allowable rtm", var_units="kg/kg", &
719 0 : l_silhs=.false., grid_kind=stats_zt )
720 0 : k = k + 1
721 :
722 : case ('rtm_enter_mfl')
723 0 : stats_metadata%irtm_enter_mfl = k
724 : call stat_assign( var_index=stats_metadata%irtm_enter_mfl, var_name="rtm_enter_mfl", &
725 : var_description="rtm_enter_mfl, Rtm before flux-limiter", var_units="kg/kg", &
726 0 : l_silhs=.false., grid_kind=stats_zt )
727 0 : k = k + 1
728 :
729 : case ('rtm_exit_mfl')
730 0 : stats_metadata%irtm_exit_mfl = k
731 : call stat_assign( var_index=stats_metadata%irtm_exit_mfl, var_name="rtm_exit_mfl", &
732 : var_description="rtm_exit_mfl, Rtm exiting flux-limiter", var_units="kg/kg", &
733 0 : l_silhs=.false., grid_kind=stats_zt )
734 0 : k = k + 1
735 :
736 : case ('rtm_old')
737 0 : stats_metadata%irtm_old = k
738 : call stat_assign( var_index=stats_metadata%irtm_old, var_name="rtm_old", &
739 : var_description="rtm_old, Rtm at previous timestep", var_units="kg/kg", &
740 0 : l_silhs=.false., grid_kind=stats_zt )
741 0 : k = k + 1
742 :
743 : case ('rtm_without_ta')
744 0 : stats_metadata%irtm_without_ta = k
745 : call stat_assign( var_index=stats_metadata%irtm_without_ta, var_name="rtm_without_ta", &
746 : var_description="rtm_without_ta, Rtm without turbulent advection contribution", &
747 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
748 0 : k = k + 1
749 :
750 : case ('wp3')
751 0 : stats_metadata%iwp3 = k
752 : call stat_assign( var_index=stats_metadata%iwp3, var_name="wp3", &
753 : var_description="w'^3, Third-order moment of vertical air velocity", &
754 : var_units="m^3/s^3", &
755 0 : l_silhs=.false., grid_kind=stats_zt )
756 0 : k = k + 1
757 :
758 : case ('wpup2')
759 0 : stats_metadata%iwpup2 = k
760 : call stat_assign( var_index=stats_metadata%iwpup2, var_name="wpup2", &
761 : var_description="w'u'^2, Third-order moment from PDF", &
762 : var_units="m^3/s^3", &
763 0 : l_silhs=.false., grid_kind=stats_zt )
764 0 : k = k + 1
765 :
766 : case ('wpvp2')
767 0 : stats_metadata%iwpvp2 = k
768 : call stat_assign( var_index=stats_metadata%iwpvp2, var_name="wpvp2", &
769 : var_description="w'v'^2, Third-order moment from PDF", &
770 : var_units="m^3/s^3", &
771 0 : l_silhs=.false., grid_kind=stats_zt )
772 0 : k = k + 1
773 :
774 : case ('thlp3')
775 0 : stats_metadata%ithlp3 = k
776 : call stat_assign( var_index=stats_metadata%ithlp3, var_name="thlp3", &
777 : var_description="thl'^3, Third-order moment of theta_l", var_units="K^3", &
778 0 : l_silhs=.false., grid_kind=stats_zt )
779 0 : k = k + 1
780 :
781 : case ('rtp3')
782 0 : stats_metadata%irtp3 = k
783 : call stat_assign( var_index=stats_metadata%irtp3, var_name="rtp3", &
784 : var_description="rt'^3, Third-order moment of total water, rt", var_units="(kg/kg)^3", &
785 0 : l_silhs=.false., grid_kind=stats_zt )
786 0 : k = k + 1
787 :
788 : case ('wpthlp2')
789 0 : stats_metadata%iwpthlp2 = k
790 : call stat_assign( var_index=stats_metadata%iwpthlp2, var_name="wpthlp2", &
791 : var_description="w'theta_l'^2, Vertical turbulent flux of theta_l'^2", &
792 : var_units="(m K^2)/s", l_silhs=.false., &
793 0 : grid_kind=stats_zt )
794 0 : k = k + 1
795 :
796 : case ('wp2thlp')
797 0 : stats_metadata%iwp2thlp = k
798 : call stat_assign( var_index=stats_metadata%iwp2thlp, var_name="wp2thlp", &
799 : var_description="w'^2theta_l', Vertical turbulent flux of w'theta_l'", &
800 : var_units="(m^2 K)/s^2", l_silhs=.false., &
801 0 : grid_kind=stats_zt )
802 0 : k = k + 1
803 :
804 : case ('wprtp2')
805 0 : stats_metadata%iwprtp2 = k
806 : call stat_assign( var_index=stats_metadata%iwprtp2, var_name="wprtp2", &
807 : var_description="w'rt'^2, Vertical turbulent flux of rt'^2", &
808 : var_units="(m kg)/(s kg)", &
809 0 : l_silhs=.false., grid_kind=stats_zt )
810 0 : k = k + 1
811 :
812 : case ('wp2rtp')
813 0 : stats_metadata%iwp2rtp = k
814 : call stat_assign( var_index=stats_metadata%iwp2rtp, var_name="wp2rtp", &
815 : var_description="w'^2rt', Vertical turbulent flux of w'rt'", &
816 : var_units="(m^2 kg)/(s^2 kg)", &
817 0 : l_silhs=.false., grid_kind=stats_zt )
818 0 : k = k + 1
819 :
820 : case ('Lscale_up')
821 0 : stats_metadata%iLscale_up = k
822 : call stat_assign( var_index=stats_metadata%iLscale_up, var_name="Lscale_up", &
823 : var_description="Lscale_up, Upward mixing length", var_units="m", l_silhs=.false., &
824 0 : grid_kind=stats_zt )
825 0 : k = k + 1
826 :
827 : case ('Lscale_down')
828 0 : stats_metadata%iLscale_down = k
829 : call stat_assign( var_index=stats_metadata%iLscale_down, var_name="Lscale_down", &
830 : var_description="Lscale_down, Downward mixing length", var_units="m", &
831 : l_silhs=.false.,&
832 0 : grid_kind=stats_zt )
833 0 : k = k + 1
834 :
835 : case ('Lscale_pert_1')
836 0 : stats_metadata%iLscale_pert_1 = k
837 : call stat_assign( var_index=stats_metadata%iLscale_pert_1, var_name="Lscale_pert_1", &
838 : var_description="Lscale_pert_1, Mixing length using a perturbed value of rtm/thlm", &
839 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
840 0 : k = k + 1
841 :
842 : case ('Lscale_pert_2')
843 0 : stats_metadata%iLscale_pert_2 = k
844 : call stat_assign( var_index=stats_metadata%iLscale_pert_2, var_name="Lscale_pert_2", &
845 : var_description="Lscale_pert_2, Mixing length using a perturbed value of rtm/thlm", &
846 0 : var_units="m", l_silhs=.false., grid_kind=stats_zt )
847 0 : k = k + 1
848 :
849 : case ('tau_zt')
850 0 : stats_metadata%itau_zt = k
851 : call stat_assign( var_index=stats_metadata%itau_zt, var_name="tau_zt", &
852 : var_description="tau_zt, Dissipation time", var_units="s", l_silhs=.false., &
853 0 : grid_kind=stats_zt )
854 0 : k = k + 1
855 :
856 : case ('invrs_tau_zt')
857 0 : stats_metadata%iinvrs_tau_zt = k
858 : call stat_assign( var_index=stats_metadata%iinvrs_tau_zt, var_name="invrs_tau_zt", &
859 : var_description="invrs_tau_zt, Inverse of dissipation time", var_units="s^-1", &
860 0 : l_silhs=.false., grid_kind=stats_zt )
861 0 : k = k + 1
862 :
863 : case ('Kh_zt')
864 0 : stats_metadata%iKh_zt = k
865 : call stat_assign( var_index=stats_metadata%iKh_zt, var_name="Kh_zt", &
866 : var_description="Kh_zt, Eddy diffusivity", var_units="m^2/s", l_silhs=.false., &
867 0 : grid_kind=stats_zt )
868 0 : k = k + 1
869 :
870 : case ('wp2thvp')
871 0 : stats_metadata%iwp2thvp = k
872 : call stat_assign( var_index=stats_metadata%iwp2thvp, var_name="wp2thvp", &
873 : var_description="w'^2theta_v', Vertical turbulent flux of w'theta_v'", &
874 : var_units="K m^2/s^2", l_silhs=.false., &
875 0 : grid_kind=stats_zt )
876 0 : k = k + 1
877 :
878 : case ('wp2rcp')
879 0 : stats_metadata%iwp2rcp = k
880 : call stat_assign( var_index=stats_metadata%iwp2rcp, var_name="wp2rcp", &
881 : var_description="w'^2rc'", var_units="(m^2 kg)/(s^2 kg)", &
882 0 : l_silhs=.false., grid_kind=stats_zt )
883 0 : k = k + 1
884 :
885 : case ('w_up_in_cloud')
886 0 : stats_metadata%iw_up_in_cloud = k
887 : call stat_assign( var_index=stats_metadata%iw_up_in_cloud, var_name="w_up_in_cloud", &
888 : var_description="Mean W in saturated updrafts", &
889 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
890 0 : k = k + 1
891 :
892 : case ('w_down_in_cloud')
893 0 : stats_metadata%iw_down_in_cloud = k
894 : call stat_assign( var_index=stats_metadata%iw_down_in_cloud, var_name="w_down_in_cloud", &
895 : var_description="Mean W in saturated downdrafts", &
896 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
897 0 : k = k + 1
898 :
899 : case ('cld_updr_frac')
900 0 : stats_metadata%icld_updr_frac = k
901 : call stat_assign( var_index=stats_metadata%icld_updr_frac, var_name="cld_updr_frac", &
902 : var_description="Cloudy Updraft Fraction", &
903 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
904 0 : k = k + 1
905 :
906 : case ('cld_downdr_frac')
907 0 : stats_metadata%icld_downdr_frac = k
908 : call stat_assign( var_index=stats_metadata%icld_downdr_frac, var_name="cld_downdr_frac", &
909 : var_description="Cloudy Downdraft Fraction", &
910 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
911 0 : k = k + 1
912 :
913 : case ('wprtpthlp')
914 0 : stats_metadata%iwprtpthlp = k
915 : call stat_assign( var_index=stats_metadata%iwprtpthlp, var_name="wprtpthlp", &
916 : var_description="w'rt'theta_l', Vertical turbulent flux of rt'theta_l'", &
917 : var_units="(m kg K)/(s kg)", &
918 0 : l_silhs=.false., grid_kind=stats_zt )
919 0 : k = k + 1
920 :
921 : case ('rc_coef')
922 0 : stats_metadata%irc_coef = k
923 : call stat_assign( var_index=stats_metadata%irc_coef, var_name="rc_coef", &
924 : var_description="rc_coef, Coefficient of X'r_c'", &
925 0 : var_units="K/(kg/kg)", l_silhs=.false., grid_kind=stats_zt )
926 0 : k = k + 1
927 :
928 : case ('sigma_sqd_w_zt')
929 0 : stats_metadata%isigma_sqd_w_zt = k
930 : call stat_assign( var_index=stats_metadata%isigma_sqd_w_zt, var_name="sigma_sqd_w_zt", &
931 : var_description="sigma_sqd_w_zt, Nondimensionalized w variance of " &
932 : // "Gaussian component",&
933 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
934 0 : k = k + 1
935 :
936 : case ('rho')
937 0 : stats_metadata%irho = k
938 : call stat_assign( var_index=stats_metadata%irho, var_name="rho", var_description="rho, Air density", &
939 0 : var_units="kg m^{-3}", l_silhs=.false., grid_kind=stats_zt )
940 0 : k = k + 1
941 :
942 : case ('Ncm') ! Brian
943 0 : stats_metadata%iNcm = k
944 : call stat_assign( var_index=stats_metadata%iNcm, var_name="Ncm", &
945 : var_description="Ncm, Cloud droplet number concentration", var_units="num/kg", &
946 0 : l_silhs=.false., grid_kind=stats_zt )
947 0 : k = k + 1
948 :
949 : case ('Nc_in_cloud')
950 0 : stats_metadata%iNc_in_cloud = k
951 :
952 : call stat_assign( var_index=stats_metadata%iNc_in_cloud, var_name="Nc_in_cloud", &
953 : var_description="Nc_in_cloud, In cloud droplet concentration", var_units="num/kg", &
954 0 : l_silhs=.false., grid_kind=stats_zt )
955 :
956 0 : k = k + 1
957 :
958 : case ('Nc_activated')
959 0 : stats_metadata%iNc_activated = k
960 :
961 : call stat_assign( var_index=stats_metadata%iNc_activated, var_name="Nc_activated", &
962 : var_description="Nc_activated, Droplets activated by GFDL activation", &
963 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
964 :
965 0 : k = k + 1
966 :
967 : case ('Nccnm')
968 0 : stats_metadata%iNccnm = k
969 : call stat_assign( var_index=stats_metadata%iNccnm, var_name="Nccnm", &
970 : var_description="Nccnm, Cloud condensation nuclei concentration", &
971 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
972 0 : k = k + 1
973 :
974 : case ('Nim') ! Brian
975 0 : stats_metadata%iNim = k
976 : call stat_assign( var_index=stats_metadata%iNim, var_name="Nim", &
977 : var_description="Nim, Ice crystal number concentration", var_units="num/kg", &
978 0 : l_silhs=.false., grid_kind=stats_zt )
979 0 : k = k + 1
980 :
981 : case ('snowslope') ! Adam Smith, 22 April 2008
982 0 : stats_metadata%isnowslope = k
983 : call stat_assign( var_index=stats_metadata%isnowslope, var_name="snowslope", &
984 : var_description="snowslope, COAMPS microphysics snow slope parameter", &
985 : var_units="1/m", &
986 0 : l_silhs=.false., grid_kind=stats_zt )
987 0 : k = k + 1
988 :
989 : case ('Nsm') ! Adam Smith, 22 April 2008
990 0 : stats_metadata%iNsm = k
991 : call stat_assign( var_index=stats_metadata%iNsm, var_name="Nsm", &
992 : var_description="Nsm, Snow particle number concentration", var_units="num/kg", &
993 0 : l_silhs=.false., grid_kind=stats_zt )
994 0 : k = k + 1
995 :
996 : case ('Ngm')
997 0 : stats_metadata%iNgm = k
998 : call stat_assign( var_index=stats_metadata%iNgm, var_name="Ngm", &
999 : var_description="Ngm, Graupel number concentration", var_units="num/kg", &
1000 0 : l_silhs=.false., grid_kind=stats_zt )
1001 0 : k = k + 1
1002 :
1003 : case ('sed_rcm') ! Brian
1004 0 : stats_metadata%ised_rcm = k
1005 : call stat_assign( var_index=stats_metadata%ised_rcm, var_name="sed_rcm", &
1006 : var_description="sed_rcm, d(rcm)/dt due to cloud sedimentation", &
1007 0 : var_units="kg / [m^2 s]", l_silhs=.false., grid_kind=stats_zt )
1008 0 : k = k + 1
1009 :
1010 : case ('rsat') ! Brian
1011 0 : stats_metadata%irsat = k
1012 : call stat_assign( var_index=stats_metadata%irsat, var_name="rsat", &
1013 : var_description="rsat, Saturation mixing ratio over liquid", var_units="kg/kg", &
1014 0 : l_silhs=.false., grid_kind=stats_zt )
1015 0 : k = k + 1
1016 :
1017 : case ('rsati')
1018 0 : stats_metadata%irsati = k
1019 : call stat_assign( var_index=stats_metadata%irsati, var_name="rsati", &
1020 : var_description="rsati, Saturation mixing ratio over ice", var_units="kg/kg", &
1021 0 : l_silhs=.false., grid_kind=stats_zt )
1022 0 : k = k + 1
1023 :
1024 : case ('rrm') ! Brian
1025 0 : stats_metadata%irrm = k
1026 : call stat_assign( var_index=stats_metadata%irrm, var_name="rrm", &
1027 : var_description="rrm, Rain water mixing ratio", var_units="kg/kg", &
1028 0 : l_silhs=.false., grid_kind=stats_zt )
1029 0 : k = k + 1
1030 :
1031 : case ('rsm')
1032 0 : stats_metadata%irsm = k
1033 : call stat_assign( var_index=stats_metadata%irsm, var_name="rsm", &
1034 : var_description="rsm, Snow water mixing ratio", var_units="kg/kg", &
1035 0 : l_silhs=.false., grid_kind=stats_zt )
1036 0 : k = k + 1
1037 :
1038 : case ('rim')
1039 0 : stats_metadata%irim = k
1040 : call stat_assign( var_index=stats_metadata%irim, var_name="rim", &
1041 : var_description="rim, Pristine ice water mixing ratio", var_units="kg/kg", &
1042 0 : l_silhs=.false., grid_kind=stats_zt )
1043 0 : k = k + 1
1044 :
1045 : case ('rgm')
1046 0 : stats_metadata%irgm = k
1047 : call stat_assign( var_index=stats_metadata%irgm, var_name="rgm", &
1048 : var_description="rgm, Graupel water mixing ratio", var_units="kg/kg", &
1049 0 : l_silhs=.false., grid_kind=stats_zt )
1050 0 : k = k + 1
1051 :
1052 : case ('Nrm') ! Brian
1053 0 : stats_metadata%iNrm = k
1054 : call stat_assign( var_index=stats_metadata%iNrm, var_name="Nrm", &
1055 : var_description="Nrm, Rain drop number concentration", var_units="num/kg", &
1056 0 : l_silhs=.false., grid_kind=stats_zt )
1057 0 : k = k + 1
1058 :
1059 : case ('m_vol_rad_rain') ! Brian
1060 0 : stats_metadata%im_vol_rad_rain = k
1061 : call stat_assign( var_index=stats_metadata%im_vol_rad_rain, var_name="mvrr", &
1062 : var_description="mvrr, Rain drop mean volume radius", &
1063 : var_units="m", l_silhs=.false.,&
1064 0 : grid_kind=stats_zt )
1065 0 : k = k + 1
1066 :
1067 : case ('m_vol_rad_cloud')
1068 0 : stats_metadata%im_vol_rad_cloud = k
1069 : call stat_assign( var_index=stats_metadata%im_vol_rad_cloud, var_name="m_vol_rad_cloud", &
1070 : var_description="m_vol_rad_cloud, Cloud drop mean volume radius", var_units="m", &
1071 : l_silhs=.false., &
1072 0 : grid_kind=stats_zt )
1073 0 : k = k + 1
1074 :
1075 : case ('eff_rad_cloud')
1076 0 : stats_metadata%ieff_rad_cloud = k
1077 : call stat_assign( var_index=stats_metadata%ieff_rad_cloud, var_name="eff_rad_cloud", &
1078 : var_description="eff_rad_cloud, Cloud drop effective volume radius", &
1079 : var_units="microns", &
1080 0 : l_silhs=.false., grid_kind=stats_zt )
1081 0 : k = k + 1
1082 :
1083 : case ('eff_rad_ice')
1084 0 : stats_metadata%ieff_rad_ice = k
1085 :
1086 : call stat_assign( var_index=stats_metadata%ieff_rad_ice, var_name="eff_rad_ice", &
1087 : var_description="eff_rad_ice, Ice effective volume radius", var_units="microns", &
1088 0 : l_silhs=.false., grid_kind=stats_zt )
1089 0 : k = k + 1
1090 :
1091 : case ('eff_rad_snow')
1092 0 : stats_metadata%ieff_rad_snow = k
1093 : call stat_assign( var_index=stats_metadata%ieff_rad_snow, var_name="eff_rad_snow", &
1094 : var_description="eff_rad_snow, Snow effective volume radius", &
1095 : var_units="microns", &
1096 0 : l_silhs=.false., grid_kind=stats_zt )
1097 0 : k = k + 1
1098 :
1099 : case ('eff_rad_rain')
1100 0 : stats_metadata%ieff_rad_rain = k
1101 : call stat_assign( var_index=stats_metadata%ieff_rad_rain, var_name="eff_rad_rain", &
1102 : var_description="eff_rad_rain, Rain drop effective volume radius", &
1103 : var_units="microns", &
1104 0 : l_silhs=.false., grid_kind=stats_zt )
1105 0 : k = k + 1
1106 :
1107 : case ('eff_rad_graupel')
1108 0 : stats_metadata%ieff_rad_graupel = k
1109 : call stat_assign( var_index=stats_metadata%ieff_rad_graupel, var_name="eff_rad_graupel", &
1110 : var_description="eff_rad_graupel, Graupel effective volume radius", &
1111 : var_units="microns", &
1112 0 : l_silhs=.false., grid_kind=stats_zt )
1113 0 : k = k + 1
1114 :
1115 : case ('precip_rate_zt') ! Brian
1116 0 : stats_metadata%iprecip_rate_zt = k
1117 :
1118 : call stat_assign( var_index=stats_metadata%iprecip_rate_zt, var_name="precip_rate_zt", &
1119 : var_description="precip_rate_zt, Rain rate", var_units="mm/day", l_silhs=.false., &
1120 0 : grid_kind=stats_zt )
1121 0 : k = k + 1
1122 :
1123 : case ('radht')
1124 0 : stats_metadata%iradht = k
1125 :
1126 : call stat_assign( var_index=stats_metadata%iradht, var_name="radht", &
1127 : var_description="radht, Total (sw+lw) radiative heating rate", var_units="K/s", &
1128 0 : l_silhs=.false., grid_kind=stats_zt )
1129 0 : k = k + 1
1130 :
1131 : case ('radht_LW')
1132 0 : stats_metadata%iradht_LW = k
1133 :
1134 : call stat_assign( var_index=stats_metadata%iradht_LW, var_name="radht_LW", &
1135 : var_description="radht_LW, Long-wave radiative heating rate", var_units="K/s", &
1136 0 : l_silhs=.false., grid_kind=stats_zt )
1137 :
1138 0 : k = k + 1
1139 :
1140 : case ('radht_SW')
1141 0 : stats_metadata%iradht_SW = k
1142 : call stat_assign( var_index=stats_metadata%iradht_SW, var_name="radht_SW", &
1143 : var_description="radht_SW, Short-wave radiative heating rate", var_units="K/s", &
1144 0 : l_silhs=.false., grid_kind=stats_zt )
1145 0 : k = k + 1
1146 :
1147 : case ('diam')
1148 0 : stats_metadata%idiam = k
1149 :
1150 : call stat_assign( var_index=stats_metadata%idiam, var_name="diam", &
1151 : var_description="diam, Ice crystal diameter", var_units="m", l_silhs=.false., &
1152 0 : grid_kind=stats_zt )
1153 0 : k = k + 1
1154 :
1155 : case ('mass_ice_cryst')
1156 0 : stats_metadata%imass_ice_cryst = k
1157 : call stat_assign( var_index=stats_metadata%imass_ice_cryst, var_name="mass_ice_cryst", &
1158 : var_description="mass_ice_cryst, Mass of a single ice crystal", var_units="kg", &
1159 0 : l_silhs=.false., grid_kind=stats_zt )
1160 0 : k = k + 1
1161 :
1162 : case ('rcm_icedfs')
1163 0 : stats_metadata%ircm_icedfs = k
1164 : call stat_assign( var_index=stats_metadata%ircm_icedfs, var_name="rcm_icedfs", &
1165 : var_description="rcm_icedfs, Change in liquid due to ice", var_units="kg/kg/s", &
1166 0 : l_silhs=.false., grid_kind=stats_zt )
1167 0 : k = k + 1
1168 :
1169 : case ('u_T_cm')
1170 0 : stats_metadata%iu_T_cm = k
1171 : call stat_assign( var_index=stats_metadata%iu_T_cm, var_name="u_T_cm", &
1172 : var_description="u_T_cm, Ice crystal fallspeed", var_units="cm s^{-1}", &
1173 0 : l_silhs=.false., grid_kind=stats_zt )
1174 0 : k = k + 1
1175 :
1176 : case ('rtm_bt')
1177 0 : stats_metadata%irtm_bt = k
1178 :
1179 : call stat_assign( var_index=stats_metadata%irtm_bt, var_name="rtm_bt", &
1180 : var_description="rtm_bt, rtm budget: rtm time tendency", &
1181 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1182 0 : k = k + 1
1183 :
1184 : case ('rtm_ma')
1185 0 : stats_metadata%irtm_ma = k
1186 :
1187 : call stat_assign( var_index=stats_metadata%irtm_ma, var_name="rtm_ma", &
1188 : var_description="rtm_ma, rtm budget: rtm vertical mean advection", &
1189 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1190 0 : k = k + 1
1191 :
1192 : case ('rtm_ta')
1193 0 : stats_metadata%irtm_ta = k
1194 :
1195 : call stat_assign( var_index=stats_metadata%irtm_ta, var_name="rtm_ta", &
1196 : var_description="rtm_ta, rtm budget: rtm turbulent advection", &
1197 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1198 0 : k = k + 1
1199 :
1200 : case ('rtm_mfl')
1201 0 : stats_metadata%irtm_mfl = k
1202 :
1203 : call stat_assign( var_index=stats_metadata%irtm_mfl, var_name="rtm_mfl", &
1204 : var_description="rtm_mfl, rtm budget: rtm correction due to monotonic flux limiter", &
1205 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
1206 0 : k = k + 1
1207 :
1208 : case ('rtm_tacl')
1209 0 : stats_metadata%irtm_tacl = k
1210 :
1211 : call stat_assign( var_index=stats_metadata%irtm_tacl, var_name="rtm_tacl", &
1212 : var_description="rtm_tacl, rtm budget: rtm correction due to ta term" &
1213 : // " (wprtp) clipping", &
1214 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt)
1215 :
1216 0 : k = k + 1
1217 :
1218 : case ('rtm_cl')
1219 0 : stats_metadata%irtm_cl = k
1220 :
1221 : call stat_assign( var_index=stats_metadata%irtm_cl, var_name="rtm_cl", &
1222 : var_description="rtm_cl, rtm budget: rtm clipping", &
1223 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1224 :
1225 0 : k = k + 1
1226 : case ('rtm_sdmp')
1227 0 : stats_metadata%irtm_sdmp = k
1228 :
1229 : call stat_assign( var_index=stats_metadata%irtm_sdmp, var_name="rtm_sdmp", &
1230 : var_description="rtm_sdmp, rtm budget: rtm correction due to sponge damping", &
1231 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1232 0 : k = k + 1
1233 :
1234 :
1235 : case ('rtm_pd')
1236 0 : stats_metadata%irtm_pd = k
1237 :
1238 : call stat_assign( var_index=stats_metadata%irtm_pd, var_name="rtm_pd", &
1239 : var_description="rtm_pd, rtm budget: rtm positive definite adjustment", &
1240 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1241 :
1242 0 : k = k + 1
1243 :
1244 : case ('thlm_bt')
1245 0 : stats_metadata%ithlm_bt = k
1246 :
1247 : call stat_assign( var_index=stats_metadata%ithlm_bt, var_name="thlm_bt", &
1248 : var_description="thlm_bt, thlm budget: thlm time tendency", var_units="K s^{-1}", &
1249 0 : l_silhs=.false., grid_kind=stats_zt )
1250 0 : k = k + 1
1251 :
1252 : case ('thlm_ma')
1253 0 : stats_metadata%ithlm_ma = k
1254 :
1255 : call stat_assign( var_index=stats_metadata%ithlm_ma, var_name="thlm_ma", &
1256 : var_description="thlm_ma, thlm budget: thlm vertical mean advection", &
1257 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1258 0 : k = k + 1
1259 :
1260 : case ('thlm_sdmp')
1261 0 : stats_metadata%ithlm_sdmp = k
1262 :
1263 : call stat_assign( var_index=stats_metadata%ithlm_sdmp, var_name="thlm_sdmp", &
1264 : var_description="thlm_sdmp, thlm budget: thlm correction due to sponge damping", &
1265 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1266 0 : k = k + 1
1267 :
1268 :
1269 : case ('thlm_ta')
1270 0 : stats_metadata%ithlm_ta = k
1271 :
1272 : call stat_assign( var_index=stats_metadata%ithlm_ta, var_name="thlm_ta", &
1273 : var_description="thlm_ta, thlm budget: thlm turbulent advection", &
1274 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1275 0 : k = k + 1
1276 :
1277 : case ('thlm_mfl')
1278 0 : stats_metadata%ithlm_mfl = k
1279 :
1280 : call stat_assign( var_index=stats_metadata%ithlm_mfl, var_name="thlm_mfl", &
1281 : var_description="thlm_mfl, thlm budget: thlm correction due to monotonic " &
1282 : // "flux limiter", &
1283 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1284 0 : k = k + 1
1285 :
1286 : case ('thlm_tacl')
1287 0 : stats_metadata%ithlm_tacl = k
1288 :
1289 : call stat_assign( var_index=stats_metadata%ithlm_tacl, var_name="thlm_tacl", &
1290 : var_description="thlm_tacl, thlm budget: thlm correction due to ta term " &
1291 : // "(wpthlp) clipping", &
1292 0 : var_units="K s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1293 0 : k = k + 1
1294 :
1295 : case ('thlm_cl')
1296 0 : stats_metadata%ithlm_cl = k
1297 :
1298 : call stat_assign( var_index=stats_metadata%ithlm_cl, var_name="thlm_cl", &
1299 : var_description="thlm_cl, thlm budget: thlm_cl", var_units="K s^{-1}", &
1300 0 : l_silhs=.false., grid_kind=stats_zt )
1301 0 : k = k + 1
1302 :
1303 : case ('wp3_bt')
1304 0 : stats_metadata%iwp3_bt = k
1305 :
1306 : call stat_assign( var_index=stats_metadata%iwp3_bt, var_name="wp3_bt", &
1307 : var_description="wp3_bt, wp3 budget: wp3 time tendency", &
1308 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1309 0 : k = k + 1
1310 :
1311 : case ('wp3_ma')
1312 0 : stats_metadata%iwp3_ma = k
1313 :
1314 : call stat_assign( var_index=stats_metadata%iwp3_ma, var_name="wp3_ma", &
1315 : var_description="wp3_ma, wp3 budget: wp3 vertical mean advection", &
1316 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1317 0 : k = k + 1
1318 :
1319 : case ('wp3_ta')
1320 0 : stats_metadata%iwp3_ta = k
1321 :
1322 : call stat_assign( var_index=stats_metadata%iwp3_ta, var_name="wp3_ta", &
1323 : var_description="wp3_ta, wp3 budget: wp3 turbulent advection", &
1324 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1325 :
1326 0 : k = k + 1
1327 :
1328 : case ('wp3_tp')
1329 0 : stats_metadata%iwp3_tp = k
1330 : call stat_assign( var_index=stats_metadata%iwp3_tp, var_name="wp3_tp", &
1331 : var_description="wp3_tp, wp3 budget: wp3 turbulent transport", &
1332 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1333 0 : k = k + 1
1334 :
1335 : case ('wp3_ac')
1336 0 : stats_metadata%iwp3_ac = k
1337 : call stat_assign( var_index=stats_metadata%iwp3_ac, var_name="wp3_ac", &
1338 : var_description="wp3_ac, wp3 budget: wp3 accumulation term", &
1339 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1340 0 : k = k + 1
1341 :
1342 : case ('wp3_bp1')
1343 0 : stats_metadata%iwp3_bp1 = k
1344 : call stat_assign( var_index=stats_metadata%iwp3_bp1, var_name="wp3_bp1", &
1345 : var_description="wp3_bp1, wp3 budget: wp3 buoyancy production", &
1346 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1347 0 : k = k + 1
1348 :
1349 : case ('wp3_pr_tp')
1350 0 : stats_metadata%iwp3_pr_tp = k
1351 : call stat_assign( var_index=stats_metadata%iwp3_pr_tp, var_name="wp3_pr_tp", &
1352 : var_description= &
1353 : "wp3_pr_tp, wp3 budget: wp3 pressure damping of turbulent production", &
1354 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1355 0 : k = k + 1
1356 :
1357 : case ('wp3_pr_turb')
1358 0 : stats_metadata%iwp3_pr_turb = k
1359 : call stat_assign( var_index=stats_metadata%iwp3_pr_turb, var_name="wp3_pr_turb", &
1360 : var_description="wp3_pr_turb, wp3 budget: wp3 pressure-turbulence correlation term", &
1361 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1362 0 : k = k + 1
1363 :
1364 : case ('wp3_pr_dfsn')
1365 0 : stats_metadata%iwp3_pr_dfsn = k
1366 : call stat_assign( var_index=stats_metadata%iwp3_pr_dfsn, var_name="wp3_pr_dfsn", &
1367 : var_description="wp3_pr_dfsn, wp3 budget: wp3 pressure diffusion term", &
1368 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1369 0 : k = k + 1
1370 :
1371 : case ('wp3_pr1')
1372 0 : stats_metadata%iwp3_pr1 = k
1373 : call stat_assign( var_index=stats_metadata%iwp3_pr1, var_name="wp3_pr1", &
1374 : var_description="wp3_pr1, wp3 budget: wp3 pressure term 1", &
1375 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1376 0 : k = k + 1
1377 :
1378 : case ('wp3_pr2')
1379 0 : stats_metadata%iwp3_pr2 = k
1380 : call stat_assign( var_index=stats_metadata%iwp3_pr2, var_name="wp3_pr2", &
1381 : var_description="wp3_pr2, wp3 budget: wp3 pressure term 2", &
1382 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1383 :
1384 0 : k = k + 1
1385 :
1386 : case ('wp3_pr3')
1387 0 : stats_metadata%iwp3_pr3 = k
1388 : call stat_assign( var_index=stats_metadata%iwp3_pr3, var_name="wp3_pr3", &
1389 : var_description="wp3_pr3, wp3 budget: wp3 pressure term 3", &
1390 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1391 :
1392 0 : k = k + 1
1393 :
1394 : case ('wp3_dp1')
1395 0 : stats_metadata%iwp3_dp1 = k
1396 : call stat_assign( var_index=stats_metadata%iwp3_dp1, var_name="wp3_dp1", &
1397 : var_description="wp3_dp1, wp3 budget: wp3 dissipation term 1", &
1398 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1399 0 : k = k + 1
1400 :
1401 : case ('wp3_sdmp')
1402 0 : stats_metadata%iwp3_sdmp = k
1403 : call stat_assign( var_index=stats_metadata%iwp3_sdmp, var_name="wp3_sdmp", &
1404 : var_description="wp3_sdmp, wp3 budget: wp3 sponge damping term", &
1405 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1406 0 : k = k + 1
1407 :
1408 : case ('wp3_cl')
1409 0 : stats_metadata%iwp3_cl = k
1410 : call stat_assign( var_index=stats_metadata%iwp3_cl, var_name="wp3_cl", &
1411 : var_description="wp3_cl, wp3 budget: wp3 clipping term", &
1412 0 : var_units="m^{3} s^{-4}", l_silhs=.false., grid_kind=stats_zt )
1413 0 : k = k + 1
1414 :
1415 : case ('wp3_splat')
1416 0 : stats_metadata%iwp3_splat = k
1417 : call stat_assign( var_index=stats_metadata%iwp3_splat, var_name="wp3_splat", &
1418 : var_description="wp3_splat, wp3 budget: wp3 splatting term", &
1419 0 : var_units="m^3 s^-4", l_silhs=.false., grid_kind=stats_zt )
1420 0 : k = k + 1
1421 :
1422 : case ('rtp3_bt')
1423 0 : stats_metadata%irtp3_bt = k
1424 :
1425 : call stat_assign( var_index=stats_metadata%irtp3_bt, var_name="rtp3_bt", &
1426 : var_description="rtp3_bt, rtp3 budget: rtp3 time tendency" &
1427 : // "[kg^{3} kg^{-3} s^{-1}]", &
1428 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1429 0 : grid_kind=stats_zt )
1430 0 : k = k + 1
1431 :
1432 : case ('rtp3_tp')
1433 0 : stats_metadata%irtp3_tp = k
1434 :
1435 : call stat_assign( var_index=stats_metadata%irtp3_tp, var_name="rtp3_tp", &
1436 : var_description="rtp3_tp, rtp3 budget: rtp3 turbulent production" &
1437 : // "[kg^{3} kg^{-3} s^{-1}]", &
1438 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1439 0 : grid_kind=stats_zt )
1440 0 : k = k + 1
1441 :
1442 : case ('rtp3_ac')
1443 0 : stats_metadata%irtp3_ac = k
1444 :
1445 : call stat_assign( var_index=stats_metadata%irtp3_ac, var_name="rtp3_ac", &
1446 : var_description="rtp3_ac, rtp3 budget: rtp3 accumulation" &
1447 : // "[kg^{3} kg^{-3} s^{-1}]", &
1448 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1449 0 : grid_kind=stats_zt )
1450 0 : k = k + 1
1451 :
1452 : case ('rtp3_dp')
1453 0 : stats_metadata%irtp3_dp = k
1454 :
1455 : call stat_assign( var_index=stats_metadata%irtp3_dp, var_name="rtp3_dp", &
1456 : var_description="rtp3_dp, rtp3 budget: rtp3 dissipation" &
1457 : // "[kg^{3} kg^{-3} s^{-1}]", &
1458 : var_units="kg^{3} kg^{-3} s^{-1}", l_silhs=.false., &
1459 0 : grid_kind=stats_zt )
1460 0 : k = k + 1
1461 :
1462 : case ('thlp3_bt')
1463 0 : stats_metadata%ithlp3_bt = k
1464 :
1465 : call stat_assign( var_index=stats_metadata%ithlp3_bt, var_name="thlp3_bt", &
1466 : var_description="thlp3_bt, thlp3 budget: thlp3 time tendency" &
1467 : // "[K^{3} s^{-1}]", &
1468 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1469 0 : k = k + 1
1470 :
1471 : case ('thlp3_tp')
1472 0 : stats_metadata%ithlp3_tp = k
1473 :
1474 : call stat_assign( var_index=stats_metadata%ithlp3_tp, var_name="thlp3_tp", &
1475 : var_description="thlp3_tp, thlp3 budget: thlp3 turbulent production" &
1476 : // "[K^{3} s^{-1}]", &
1477 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1478 0 : k = k + 1
1479 :
1480 : case ('thlp3_ac')
1481 0 : stats_metadata%ithlp3_ac = k
1482 :
1483 : call stat_assign( var_index=stats_metadata%ithlp3_ac, var_name="thlp3_ac", &
1484 : var_description="thlp3_ac, thlp3 budget: thlp3 accumulation" &
1485 : // "[K^{3} s^{-1}]", &
1486 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1487 0 : k = k + 1
1488 :
1489 : case ('thlp3_dp')
1490 0 : stats_metadata%ithlp3_dp = k
1491 :
1492 : call stat_assign( var_index=stats_metadata%ithlp3_dp, var_name="thlp3_dp", &
1493 : var_description="thlp3_dp, thlp3 budget: thlp3 dissipation", &
1494 0 : var_units="K^{3} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1495 0 : k = k + 1
1496 :
1497 : case ('rrm_bt')
1498 0 : stats_metadata%irrm_bt = k
1499 : call stat_assign( var_index=stats_metadata%irrm_bt, var_name="rrm_bt", &
1500 : var_description="rrm_bt, rrm budget: rrm time tendency", &
1501 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1502 0 : k = k + 1
1503 :
1504 : case ('rrm_ma')
1505 0 : stats_metadata%irrm_ma = k
1506 :
1507 : call stat_assign( var_index=stats_metadata%irrm_ma, var_name="rrm_ma", &
1508 : var_description="rrm_ma, rrm budget: rrm vertical mean advection", &
1509 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1510 0 : k = k + 1
1511 :
1512 : case ('rrm_sd')
1513 0 : stats_metadata%irrm_sd = k
1514 :
1515 : call stat_assign( var_index=stats_metadata%irrm_sd, var_name="rrm_sd", &
1516 : var_description="rrm_sd, rrm budget: rrm sedimentation", &
1517 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1518 0 : k = k + 1
1519 :
1520 : case ('rrm_ts')
1521 0 : stats_metadata%irrm_ts = k
1522 :
1523 : call stat_assign( var_index=stats_metadata%irrm_ts, var_name="rrm_ts", &
1524 : var_description="rrm_ts, rrm budget: rrm turbulent sedimentation", &
1525 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1526 0 : k = k + 1
1527 :
1528 : case ('rrm_sd_morr')
1529 0 : stats_metadata%irrm_sd_morr = k
1530 :
1531 : call stat_assign( var_index=stats_metadata%irrm_sd_morr, var_name="rrm_sd_morr", &
1532 : var_description="rrm_sd_morr, rrm sedimentation when using morrision microphysics &
1533 : &(not in budget, included in rrm_mc)", &
1534 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.true., grid_kind=stats_zt )
1535 0 : k = k + 1
1536 :
1537 : case ('rrm_ta')
1538 0 : stats_metadata%irrm_ta = k
1539 :
1540 : call stat_assign( var_index=stats_metadata%irrm_ta, var_name="rrm_ta", &
1541 : var_description="rrm_ta, rrm budget: rrm turbulent advection", &
1542 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1543 0 : k = k + 1
1544 :
1545 : case ('rrm_evap')
1546 0 : stats_metadata%irrm_evap = k
1547 :
1548 : call stat_assign( var_index=stats_metadata%irrm_evap, var_name="rrm_evap", &
1549 : var_description="rrm_evap, rrm evaporation rate", &
1550 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1551 0 : k = k + 1
1552 :
1553 : case ('rrm_auto')
1554 0 : stats_metadata%irrm_auto = k
1555 :
1556 : call stat_assign( var_index=stats_metadata%irrm_auto, var_name="rrm_auto", &
1557 : var_description="rrm_auto, rrm autoconversion rate", &
1558 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1559 0 : k = k + 1
1560 :
1561 : case ('rrm_accr')
1562 0 : stats_metadata%irrm_accr = k
1563 : call stat_assign( var_index=stats_metadata%irrm_accr, var_name="rrm_accr", &
1564 : var_description="rrm_accr, rrm accretion rate", &
1565 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1566 0 : k = k + 1
1567 :
1568 : case ('rrm_evap_adj')
1569 0 : stats_metadata%irrm_evap_adj = k
1570 :
1571 : call stat_assign( var_index=stats_metadata%irrm_evap_adj, var_name="rrm_evap_adj", &
1572 : var_description="rrm_evap_adj, rrm evaporation adjustment due to over-evaporation", &
1573 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1574 0 : k = k + 1
1575 :
1576 : case ('rrm_src_adj')
1577 0 : stats_metadata%irrm_src_adj = k
1578 :
1579 : call stat_assign( var_index=stats_metadata%irrm_src_adj, var_name="rrm_src_adj", &
1580 : var_description="rrm_src_adj, rrm source term adjustment due to over-depletion", &
1581 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1582 0 : k = k + 1
1583 :
1584 : case ('rrm_mc_nonadj')
1585 0 : stats_metadata%irrm_mc_nonadj = k
1586 :
1587 : call stat_assign( var_index=stats_metadata%irrm_mc_nonadj, var_name="rrm_mc_nonadj", &
1588 : var_description="rrm_mc_nonadj, Value of rrm_mc tendency before adjustment", &
1589 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1590 0 : k = k + 1
1591 :
1592 : case ('rrm_hf')
1593 0 : stats_metadata%irrm_hf = k
1594 : call stat_assign( var_index=stats_metadata%irrm_hf, var_name="rrm_hf", &
1595 : var_description="rrm_hf, rrm budget: rrm hole-filling term", &
1596 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1597 0 : k = k + 1
1598 :
1599 : case ('rrm_wvhf')
1600 0 : stats_metadata%irrm_wvhf = k
1601 : call stat_assign( var_index=stats_metadata%irrm_wvhf, var_name="rrm_wvhf", &
1602 : var_description="rrm_wvhf, rrm budget: rrm water vapor hole-filling term", &
1603 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1604 0 : k = k + 1
1605 :
1606 : case ('rrm_cl')
1607 0 : stats_metadata%irrm_cl = k
1608 : call stat_assign( var_index=stats_metadata%irrm_cl, var_name="rrm_cl", &
1609 : var_description="rrm_cl, rrm budget: rrm clipping term", &
1610 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1611 0 : k = k + 1
1612 :
1613 : case ('rrm_mc')
1614 0 : stats_metadata%irrm_mc = k
1615 :
1616 : call stat_assign( var_index=stats_metadata%irrm_mc, var_name="rrm_mc", &
1617 : var_description="rrm_mc, rrm budget: Change in rrm due to microphysics", &
1618 0 : var_units="kg kg^{-1} s^{-1}", l_silhs=.false., grid_kind=stats_zt )
1619 0 : k = k + 1
1620 :
1621 : case ('Nrm_bt')
1622 0 : stats_metadata%iNrm_bt = k
1623 : call stat_assign( var_index=stats_metadata%iNrm_bt, var_name="Nrm_bt", &
1624 : var_description="Nrm_bt, Nrm budget: Nrm time tendency", &
1625 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1626 :
1627 0 : k = k + 1
1628 :
1629 : case ('Nrm_ma')
1630 0 : stats_metadata%iNrm_ma = k
1631 :
1632 : call stat_assign( var_index=stats_metadata%iNrm_ma, var_name="Nrm_ma", &
1633 : var_description="Nrm_ma, Nrm budget: Nrm vertical mean advection", &
1634 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1635 0 : k = k + 1
1636 :
1637 : case ('Nrm_sd')
1638 0 : stats_metadata%iNrm_sd = k
1639 :
1640 : call stat_assign( var_index=stats_metadata%iNrm_sd, var_name="Nrm_sd", &
1641 : var_description="Nrm_sd, Nrm budget: Nrm sedimentation", &
1642 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1643 :
1644 0 : k = k + 1
1645 :
1646 : case ('Nrm_ts')
1647 0 : stats_metadata%iNrm_ts = k
1648 :
1649 : call stat_assign( var_index=stats_metadata%iNrm_ts, var_name="Nrm_ts", &
1650 : var_description="Nrm_ts, Nrm budget: Nrm turbulent sedimentation", &
1651 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1652 0 : k = k + 1
1653 :
1654 : case ('Nrm_ta')
1655 0 : stats_metadata%iNrm_ta = k
1656 : call stat_assign( var_index=stats_metadata%iNrm_ta, var_name="Nrm_ta", &
1657 : var_description="Nrm_ta, Nrm budget: Nrm turbulent advection", &
1658 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1659 :
1660 0 : k = k + 1
1661 :
1662 : case ('Nrm_evap')
1663 0 : stats_metadata%iNrm_evap = k
1664 :
1665 : call stat_assign( var_index=stats_metadata%iNrm_evap, var_name="Nrm_evap", &
1666 : var_description="Nrm_evap, Nrm evaporation rate", var_units="(num/kg)/s", &
1667 0 : l_silhs=.false., grid_kind=stats_zt )
1668 0 : k = k + 1
1669 :
1670 : case ('Nrm_auto')
1671 0 : stats_metadata%iNrm_auto = k
1672 :
1673 : call stat_assign( var_index=stats_metadata%iNrm_auto, var_name="Nrm_auto", &
1674 : var_description="Nrm_auto, Nrm autoconversion rate", var_units="(num/kg)/s", &
1675 0 : l_silhs=.false., grid_kind=stats_zt )
1676 :
1677 0 : k = k + 1
1678 :
1679 : case ('Nrm_evap_adj')
1680 0 : stats_metadata%iNrm_evap_adj = k
1681 :
1682 : call stat_assign( var_index=stats_metadata%iNrm_evap_adj, var_name="Nrm_evap_adj", &
1683 : var_description="Nrm_evap_adj, Nrm evaporation adjustment due to over-evaporation", &
1684 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1685 0 : k = k + 1
1686 :
1687 : case ('Nrm_src_adj')
1688 0 : stats_metadata%iNrm_src_adj = k
1689 :
1690 : call stat_assign( var_index=stats_metadata%iNrm_src_adj, var_name="Nrm_src_adj", &
1691 : var_description="Nrm_src_adj, Nrm source term adjustment due to over-depletion", &
1692 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1693 0 : k = k + 1
1694 :
1695 : case ('Nrm_cl')
1696 0 : stats_metadata%iNrm_cl = k
1697 : call stat_assign( var_index=stats_metadata%iNrm_cl, var_name="Nrm_cl", &
1698 : var_description="Nrm_cl, Nrm budget: Nrm clipping term", &
1699 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1700 0 : k = k + 1
1701 :
1702 : case ('Nrm_mc')
1703 0 : stats_metadata%iNrm_mc = k
1704 : call stat_assign( var_index=stats_metadata%iNrm_mc, var_name="Nrm_mc", &
1705 : var_description="Nrm_mc, Nrm budget: Change in Nrm due to microphysics " &
1706 : // "(Not in budget)", &
1707 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1708 0 : k = k + 1
1709 :
1710 : case ('rsm_bt')
1711 0 : stats_metadata%irsm_bt = k
1712 : call stat_assign( var_index=stats_metadata%irsm_bt, var_name="rsm_bt", &
1713 : var_description="rsm_bt, rsm budget: rsm time tendency", &
1714 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1715 :
1716 0 : k = k + 1
1717 :
1718 : case ('rsm_ma')
1719 0 : stats_metadata%irsm_ma = k
1720 :
1721 : call stat_assign( var_index=stats_metadata%irsm_ma, var_name="rsm_ma", &
1722 : var_description="rsm_ma, rsm budget: rsm vertical mean advection", &
1723 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1724 0 : k = k + 1
1725 :
1726 : case ('rsm_sd')
1727 0 : stats_metadata%irsm_sd = k
1728 : call stat_assign( var_index=stats_metadata%irsm_sd, var_name="rsm_sd", &
1729 : var_description="rsm_sd, rsm budget: rsm sedimentation", &
1730 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1731 0 : k = k + 1
1732 :
1733 : case ('rsm_sd_morr')
1734 0 : stats_metadata%irsm_sd_morr = k
1735 : call stat_assign( var_index=stats_metadata%irsm_sd_morr, var_name="rsm_sd_morr", &
1736 : var_description="rsm_sd_morr, rsm sedimentation when using morrison microphysics &
1737 : &(Not in budget, included in rsm_mc)", &
1738 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1739 0 : k = k + 1
1740 :
1741 : case ('rsm_ta')
1742 0 : stats_metadata%irsm_ta = k
1743 :
1744 : call stat_assign( var_index=stats_metadata%irsm_ta, var_name="rsm_ta", &
1745 : var_description="rsm_ta, rsm budget: rsm turbulent advection", &
1746 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1747 0 : k = k + 1
1748 :
1749 : case ('rsm_mc')
1750 0 : stats_metadata%irsm_mc = k
1751 :
1752 : call stat_assign( var_index=stats_metadata%irsm_mc, var_name="rsm_mc", &
1753 : var_description="rsm_mc, rsm budget: Change in rsm due to microphysics", &
1754 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1755 0 : k = k + 1
1756 :
1757 : case ('rsm_hf')
1758 0 : stats_metadata%irsm_hf = k
1759 :
1760 : call stat_assign( var_index=stats_metadata%irsm_hf, var_name="rsm_hf", &
1761 : var_description="rsm_hf, rsm budget: rsm hole-filling term", &
1762 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1763 0 : k = k + 1
1764 :
1765 : case ('rsm_wvhf')
1766 0 : stats_metadata%irsm_wvhf = k
1767 :
1768 : call stat_assign( var_index=stats_metadata%irsm_wvhf, var_name="rsm_wvhf", &
1769 : var_description="rsm_wvhf, rsm budget: rsm water vapor hole-filling term", &
1770 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1771 0 : k = k + 1
1772 :
1773 : case ('rsm_cl')
1774 0 : stats_metadata%irsm_cl = k
1775 :
1776 : call stat_assign( var_index=stats_metadata%irsm_cl, var_name="rsm_cl", &
1777 : var_description="rsm_cl, rsm budget: rsm clipping term", &
1778 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1779 0 : k = k + 1
1780 :
1781 : case ('Nsm_bt')
1782 0 : stats_metadata%iNsm_bt = k
1783 : call stat_assign( var_index=stats_metadata%iNsm_bt, var_name="Nsm_bt", &
1784 : var_description="Nsm_bt, Nsm budget", var_units="(num/kg)/s", &
1785 0 : l_silhs=.false., grid_kind=stats_zt )
1786 :
1787 0 : k = k + 1
1788 :
1789 : case ('Nsm_ma')
1790 0 : stats_metadata%iNsm_ma = k
1791 :
1792 : call stat_assign( var_index=stats_metadata%iNsm_ma, var_name="Nsm_ma", &
1793 : var_description="Nsm_ma, Nsm budget: Nsm mean advection", &
1794 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1795 0 : k = k + 1
1796 :
1797 : case ('Nsm_sd')
1798 0 : stats_metadata%iNsm_sd = k
1799 :
1800 : call stat_assign( var_index=stats_metadata%iNsm_sd, var_name="Nsm_sd", &
1801 : var_description="Nsm_sd, Nsm budget: Nsm sedimentation", &
1802 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1803 :
1804 0 : k = k + 1
1805 :
1806 : case ('Nsm_ta')
1807 0 : stats_metadata%iNsm_ta = k
1808 : call stat_assign( var_index=stats_metadata%iNsm_ta, var_name="Nsm_ta", &
1809 : var_description="Nsm_ta, Nsm budget: Nsm turbulent advection", &
1810 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1811 :
1812 0 : k = k + 1
1813 :
1814 : case ('Nsm_mc')
1815 0 : stats_metadata%iNsm_mc = k
1816 : call stat_assign( var_index=stats_metadata%iNsm_mc, var_name="Nsm_mc", &
1817 : var_description="Nsm_mc, Nsm budget: Nsm microphysics", &
1818 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1819 :
1820 0 : k = k + 1
1821 :
1822 : case ('Nsm_cl')
1823 0 : stats_metadata%iNsm_cl = k
1824 :
1825 : call stat_assign( var_index=stats_metadata%iNsm_cl, var_name="Nsm_cl", &
1826 : var_description="Nsm_cl, Nsm budget: Nsm clipping term", &
1827 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1828 0 : k = k + 1
1829 :
1830 : case ('rim_bt')
1831 0 : stats_metadata%irim_bt = k
1832 :
1833 : call stat_assign( var_index=stats_metadata%irim_bt, var_name="rim_bt", &
1834 : var_description="rim_bt, rim budget: rim time tendency", &
1835 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1836 :
1837 0 : k = k + 1
1838 :
1839 : case ('rim_ma')
1840 0 : stats_metadata%irim_ma = k
1841 :
1842 : call stat_assign( var_index=stats_metadata%irim_ma, var_name="rim_ma", &
1843 : var_description="rim_ma, rim budget: rim vertical mean advection", &
1844 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1845 0 : k = k + 1
1846 :
1847 : case ('rim_sd')
1848 0 : stats_metadata%irim_sd = k
1849 :
1850 : call stat_assign( var_index=stats_metadata%irim_sd, var_name="rim_sd", &
1851 : var_description="rim_sd, rim budget: rim sedimentation", &
1852 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1853 0 : k = k + 1
1854 :
1855 : case ('rim_sd_mg_morr')
1856 0 : stats_metadata%irim_sd_mg_morr = k
1857 :
1858 : call stat_assign( var_index=stats_metadata%irim_sd_mg_morr, var_name="rim_sd_mg_morr", &
1859 : var_description="rim_sd_mg_morr, rim sedimentation when using morrison or MG " &
1860 : // "microphysics" &
1861 : // "(not in budget, included in rim_mc)", &
1862 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1863 0 : k = k + 1
1864 :
1865 : case ('rim_ta')
1866 0 : stats_metadata%irim_ta = k
1867 :
1868 : call stat_assign( var_index=stats_metadata%irim_ta, var_name="rim_ta", &
1869 : var_description="rim_ta, rim budget: rim turbulent advection", &
1870 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1871 0 : k = k + 1
1872 :
1873 : case ('rim_mc')
1874 0 : stats_metadata%irim_mc = k
1875 :
1876 : call stat_assign( var_index=stats_metadata%irim_mc, var_name="rim_mc", &
1877 : var_description="rim_mc, rim budget: Change in rim due to microphysics", &
1878 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1879 0 : k = k + 1
1880 :
1881 : case ('rim_hf')
1882 0 : stats_metadata%irim_hf = k
1883 :
1884 : call stat_assign( var_index=stats_metadata%irim_hf, var_name="rim_hf", &
1885 : var_description="rim_hf, rim budget: rim hole-filling term", &
1886 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1887 0 : k = k + 1
1888 :
1889 : case ('rim_wvhf')
1890 0 : stats_metadata%irim_wvhf = k
1891 :
1892 : call stat_assign( var_index=stats_metadata%irim_wvhf, var_name="rim_wvhf", &
1893 : var_description="rim_wvhf, rim budget: rim water vapor hole-filling term", &
1894 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1895 0 : k = k + 1
1896 :
1897 : case ('rim_cl')
1898 0 : stats_metadata%irim_cl = k
1899 :
1900 : call stat_assign( var_index=stats_metadata%irim_cl, var_name="rim_cl", &
1901 : var_description="rim_cl, rim budget: rim clipping term", &
1902 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1903 0 : k = k + 1
1904 :
1905 : case ('rgm_bt')
1906 0 : stats_metadata%irgm_bt = k
1907 :
1908 : call stat_assign( var_index=stats_metadata%irgm_bt, var_name="rgm_bt", &
1909 : var_description="rgm_bt, rgm budget: rgm time tendency", &
1910 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1911 0 : k = k + 1
1912 :
1913 : case ('rgm_ma')
1914 0 : stats_metadata%irgm_ma = k
1915 :
1916 : call stat_assign( var_index=stats_metadata%irgm_ma, var_name="rgm_ma", &
1917 : var_description="rgm_ma, rgm budget: rgm vertical mean advection", &
1918 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1919 0 : k = k + 1
1920 :
1921 : case ('rgm_sd')
1922 0 : stats_metadata%irgm_sd = k
1923 :
1924 : call stat_assign( var_index=stats_metadata%irgm_sd, var_name="rgm_sd", &
1925 : var_description="rgm_sd, rgm budget: rgm sedimentation", &
1926 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1927 0 : k = k + 1
1928 :
1929 : case ('rgm_sd_morr')
1930 0 : stats_metadata%irgm_sd_morr = k
1931 :
1932 : call stat_assign( var_index=stats_metadata%irgm_sd_morr, var_name="rgm_sd_morr", &
1933 : var_description="rgm_sd_morr, rgm sedimentation when using morrison microphysics &
1934 : &(not in budget, included in rgm_mc)", &
1935 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
1936 0 : k = k + 1
1937 :
1938 : case ('rgm_ta')
1939 0 : stats_metadata%irgm_ta = k
1940 :
1941 : call stat_assign( var_index=stats_metadata%irgm_ta, var_name="rgm_ta", &
1942 : var_description="rgm_ta, rgm budget: rgm turbulent advection", &
1943 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1944 0 : k = k + 1
1945 :
1946 : case ('rgm_mc')
1947 0 : stats_metadata%irgm_mc = k
1948 :
1949 : call stat_assign( var_index=stats_metadata%irgm_mc, var_name="rgm_mc", &
1950 : var_description="rgm_mc, rgm budget: Change in rgm due to microphysics", &
1951 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1952 0 : k = k + 1
1953 :
1954 : case ('rgm_hf')
1955 0 : stats_metadata%irgm_hf = k
1956 :
1957 : call stat_assign( var_index=stats_metadata%irgm_hf, var_name="rgm_hf", &
1958 : var_description="rgm_hf, rgm budget: rgm hole-filling term", &
1959 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1960 0 : k = k + 1
1961 :
1962 : case ('rgm_wvhf')
1963 0 : stats_metadata%irgm_wvhf = k
1964 :
1965 : call stat_assign( var_index=stats_metadata%irgm_wvhf, var_name="rgm_wvhf", &
1966 : var_description="rgm_wvhf, rgm budget: rgm water vapor hole-filling term", &
1967 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1968 0 : k = k + 1
1969 :
1970 : case ('rgm_cl')
1971 0 : stats_metadata%irgm_cl = k
1972 :
1973 : call stat_assign( var_index=stats_metadata%irgm_cl, var_name="rgm_cl", &
1974 : var_description="rgm_cl, rgm budget: rgm clipping term", &
1975 0 : var_units="(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1976 0 : k = k + 1
1977 :
1978 : case ('Ngm_bt')
1979 0 : stats_metadata%iNgm_bt = k
1980 : call stat_assign( var_index=stats_metadata%iNgm_bt, var_name="Ngm_bt", &
1981 : var_description="Ngm_bt, Ngm budget:", var_units="(num/kg)/s", &
1982 0 : l_silhs=.false., grid_kind=stats_zt )
1983 :
1984 0 : k = k + 1
1985 :
1986 : case ('Ngm_ma')
1987 0 : stats_metadata%iNgm_ma = k
1988 :
1989 : call stat_assign( var_index=stats_metadata%iNgm_ma, var_name="Ngm_ma", &
1990 : var_description="Ngm_ma, Ngm budget: Ngm mean advection", &
1991 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
1992 0 : k = k + 1
1993 :
1994 : case ('Ngm_sd')
1995 0 : stats_metadata%iNgm_sd = k
1996 :
1997 : call stat_assign( var_index=stats_metadata%iNgm_sd, var_name="Ngm_sd", &
1998 : var_description="Ngm_sd, Ngm budget: Ngm sedimentation", &
1999 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2000 :
2001 0 : k = k + 1
2002 :
2003 : case ('Ngm_ta')
2004 0 : stats_metadata%iNgm_ta = k
2005 : call stat_assign( var_index=stats_metadata%iNgm_ta, var_name="Ngm_ta", &
2006 : var_description="Ngm_ta, Ngm budget: Ngm turbulent advection", &
2007 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2008 :
2009 0 : k = k + 1
2010 :
2011 : case ('Ngm_mc')
2012 0 : stats_metadata%iNgm_mc = k
2013 :
2014 : call stat_assign( var_index=stats_metadata%iNgm_mc, var_name="Ngm_mc", &
2015 : var_description="Ngm_mc, Ngm budget: Ngm microphysics term", &
2016 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2017 0 : k = k + 1
2018 :
2019 : case ('Ngm_cl')
2020 0 : stats_metadata%iNgm_cl = k
2021 :
2022 : call stat_assign( var_index=stats_metadata%iNgm_cl, var_name="Ngm_cl", &
2023 : var_description="Ngm_cl, Ngm budget: Ngm clipping term", &
2024 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2025 0 : k = k + 1
2026 :
2027 : case ('Nim_bt')
2028 0 : stats_metadata%iNim_bt = k
2029 : call stat_assign( var_index=stats_metadata%iNim_bt, var_name="Nim_bt", &
2030 : var_description="Nim_bt, Nim budget", var_units="(num/kg)/s", l_silhs=.false., &
2031 0 : grid_kind=stats_zt )
2032 :
2033 0 : k = k + 1
2034 :
2035 : case ('Nim_ma')
2036 0 : stats_metadata%iNim_ma = k
2037 :
2038 : call stat_assign( var_index=stats_metadata%iNim_ma, var_name="Nim_ma", &
2039 : var_description="Nim_ma, Nim budget: Nim mean advection", &
2040 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2041 0 : k = k + 1
2042 :
2043 : case ('Nim_sd')
2044 0 : stats_metadata%iNim_sd = k
2045 :
2046 : call stat_assign( var_index=stats_metadata%iNim_sd, var_name="Nim_sd", &
2047 : var_description="Nim_sd, Nim budget: Nim sedimentation", &
2048 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2049 :
2050 0 : k = k + 1
2051 :
2052 : case ('Nim_ta')
2053 0 : stats_metadata%iNim_ta = k
2054 : call stat_assign( var_index=stats_metadata%iNim_ta, var_name="Nim_ta", &
2055 : var_description="Nim_ta, Nim budget: Nim turbulent advection", &
2056 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2057 :
2058 0 : k = k + 1
2059 :
2060 : case ('Nim_mc')
2061 0 : stats_metadata%iNim_mc = k
2062 :
2063 : call stat_assign( var_index=stats_metadata%iNim_mc, var_name="Nim_mc", &
2064 : var_description="Nim_mc, Nim budget: Nim microphysics term", &
2065 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2066 0 : k = k + 1
2067 :
2068 : case ('Nim_cl')
2069 0 : stats_metadata%iNim_cl = k
2070 :
2071 : call stat_assign( var_index=stats_metadata%iNim_cl, var_name="Nim_cl", &
2072 : var_description="Nim_cl, Nim budget: Nim clipping term", &
2073 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2074 0 : k = k + 1
2075 :
2076 : case ('Ncm_bt')
2077 0 : stats_metadata%iNcm_bt = k
2078 : call stat_assign( var_index=stats_metadata%iNcm_bt, var_name="Ncm_bt", &
2079 : var_description="Ncm_bt, Ncm budget: Cloud droplet number concentration budget", &
2080 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2081 :
2082 0 : k = k + 1
2083 :
2084 : case ('Ncm_ma')
2085 0 : stats_metadata%iNcm_ma = k
2086 :
2087 : call stat_assign( var_index=stats_metadata%iNcm_ma, var_name="Ncm_ma", &
2088 : var_description="Ncm_ma, Ncm budget: Ncm vertical mean advection", &
2089 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2090 0 : k = k + 1
2091 :
2092 : case ('Ncm_act')
2093 0 : stats_metadata%iNcm_act = k
2094 :
2095 : call stat_assign( var_index=stats_metadata%iNcm_act, var_name="Ncm_act", &
2096 : var_description="Ncm_act, Ncm budget: Change in Ncm due to activation", &
2097 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2098 :
2099 0 : k = k + 1
2100 :
2101 : case ('Ncm_ta')
2102 0 : stats_metadata%iNcm_ta = k
2103 : call stat_assign( var_index=stats_metadata%iNcm_ta, var_name="Ncm_ta", &
2104 : var_description="Ncm_ta, Ncm budget: Ncm turbulent advection", &
2105 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2106 :
2107 0 : k = k + 1
2108 :
2109 : case ('Ncm_mc')
2110 0 : stats_metadata%iNcm_mc = k
2111 :
2112 : call stat_assign( var_index=stats_metadata%iNcm_mc, var_name="Ncm_mc", &
2113 : var_description="Ncm_mc, Ncm budget: Change in Ncm due to microphysics", &
2114 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2115 0 : k = k + 1
2116 :
2117 : case ('Ncm_cl')
2118 0 : stats_metadata%iNcm_cl = k
2119 :
2120 : call stat_assign( var_index=stats_metadata%iNcm_cl, var_name="Ncm_cl", &
2121 : var_description="Ncm_cl, Ncm budget: Ncm clipping term", &
2122 0 : var_units="(num/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2123 0 : k = k + 1
2124 :
2125 : case ('PSMLT')
2126 0 : stats_metadata%iPSMLT = k
2127 :
2128 : call stat_assign( var_index=stats_metadata%iPSMLT, var_name="PSMLT", &
2129 : var_description="PSMLT, Freezing of rain to form snow, +rsm, -rrm", &
2130 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2131 0 : k = k + 1
2132 :
2133 : case ('EVPMS')
2134 0 : stats_metadata%iEVPMS = k
2135 :
2136 : call stat_assign( var_index=stats_metadata%iEVPMS, var_name="EVPMS", &
2137 : var_description="EVPMS, Evaporation of melted snow, +rsm, -rvm", &
2138 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2139 0 : k = k + 1
2140 :
2141 : case ('PRACS')
2142 0 : stats_metadata%iPRACS = k
2143 :
2144 : call stat_assign( var_index=stats_metadata%iPRACS, var_name="PRACS", &
2145 : var_description="PRACS, Collection of rain by snow, +rsm, -rrm", &
2146 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2147 0 : k = k + 1
2148 :
2149 : case ('EVPMG')
2150 0 : stats_metadata%iEVPMG = k
2151 :
2152 : call stat_assign( var_index=stats_metadata%iEVPMG, var_name="EVPMG", &
2153 : var_description="EVPMG, Evaporation of melted graupel, +rgm, -rvm", &
2154 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2155 0 : k = k + 1
2156 :
2157 : case ('PRACG')
2158 0 : stats_metadata%iPRACG = k
2159 :
2160 : call stat_assign( var_index=stats_metadata%iPRACG, var_name="PRACG", &
2161 : var_description="PRACG, Negative of collection of rain by graupel, +rrm, -rgm", &
2162 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2163 0 : k = k + 1
2164 :
2165 : case ('PGMLT')
2166 0 : stats_metadata%iPGMLT = k
2167 :
2168 : call stat_assign( var_index=stats_metadata%iPGMLT, var_name="PGMLT", &
2169 : var_description="PGMLT, Negative of melting of graupel, +rgm, -rrm", &
2170 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2171 0 : k = k + 1
2172 :
2173 : case ('MNUCCC')
2174 0 : stats_metadata%iMNUCCC = k
2175 :
2176 : call stat_assign( var_index=stats_metadata%iMNUCCC, var_name="MNUCCC", &
2177 : var_description="MNUCCC, Contact freezing of cloud droplets, +rim, -rcm", &
2178 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2179 0 : k = k + 1
2180 :
2181 : case ('PSACWS')
2182 0 : stats_metadata%iPSACWS = k
2183 :
2184 : call stat_assign( var_index=stats_metadata%iPSACWS, var_name="PSACWS", &
2185 : var_description="PSACWS, Collection of cloud water by snow, +rsm, -rcm", &
2186 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2187 0 : k = k + 1
2188 :
2189 : case ('PSACWI')
2190 0 : stats_metadata%iPSACWI = k
2191 :
2192 : call stat_assign( var_index=stats_metadata%iPSACWI, var_name="PSACWI", &
2193 : var_description="PSACWI, Collection of cloud water by cloud ice, +rim, -rcm", &
2194 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2195 0 : k = k + 1
2196 :
2197 : case ('QMULTS')
2198 0 : stats_metadata%iQMULTS = k
2199 :
2200 : call stat_assign( var_index=stats_metadata%iQMULTS, var_name="QMULTS", &
2201 : var_description="QMULTS, Splintering from cloud droplets accreted onto snow, " &
2202 : // "+rim, -rcm", &
2203 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2204 0 : k = k + 1
2205 :
2206 : case ('QMULTG')
2207 0 : stats_metadata%iQMULTG = k
2208 :
2209 : call stat_assign( var_index=stats_metadata%iQMULTG, var_name="QMULTG", &
2210 : var_description="QMULTG, Splintering from droplets accreted onto graupel, " &
2211 : // "+rim, -rcm", &
2212 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2213 0 : k = k + 1
2214 :
2215 : case ('PSACWG')
2216 0 : stats_metadata%iPSACWG = k
2217 :
2218 : call stat_assign( var_index=stats_metadata%iPSACWG, var_name="PSACWG", &
2219 : var_description="PSACWG, Collection of cloud water by graupel, +rgm, -rcm", &
2220 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2221 0 : k = k + 1
2222 :
2223 : case ('PGSACW')
2224 0 : stats_metadata%iPGSACW = k
2225 :
2226 : call stat_assign( var_index=stats_metadata%iPGSACW, var_name="PGSACW", &
2227 : var_description="PGSACW, Reclassification of rimed snow as graupel, +rgm, -rcm", &
2228 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2229 0 : k = k + 1
2230 :
2231 : case ('PRD')
2232 0 : stats_metadata%iPRD = k
2233 :
2234 : call stat_assign( var_index=stats_metadata%iPRD, var_name="PRD", &
2235 : var_description="PRD, Depositional growth of cloud ice, +rim, -rvm", &
2236 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2237 0 : k = k + 1
2238 :
2239 : case ('PRCI')
2240 0 : stats_metadata%iPRCI = k
2241 :
2242 : call stat_assign( var_index=stats_metadata%iPRCI, var_name="PRCI", &
2243 : var_description="PRCI, Autoconversion of cloud ice to snow, +rsm, -rim", &
2244 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2245 0 : k = k + 1
2246 :
2247 : case ('PRAI')
2248 0 : stats_metadata%iPRAI = k
2249 :
2250 : call stat_assign( var_index=stats_metadata%iPRAI, var_name="PRAI", &
2251 : var_description="PRAI, Collection of cloud ice by snow, +rsm, -rim", &
2252 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2253 0 : k = k + 1
2254 :
2255 : case ('QMULTR')
2256 0 : stats_metadata%iQMULTR = k
2257 :
2258 : call stat_assign( var_index=stats_metadata%iQMULTR, var_name="QMULTR", &
2259 : var_description="QMULTR, Splintering from rain droplets accreted onto snow, " &
2260 : // "+rim, -rrm", &
2261 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2262 0 : k = k + 1
2263 :
2264 : case ('QMULTRG')
2265 0 : stats_metadata%iQMULTRG = k
2266 :
2267 : call stat_assign( var_index=stats_metadata%iQMULTRG, var_name="QMULTRG", &
2268 : var_description="QMULTRG, Splintering from rain droplets accreted onto graupel, " &
2269 : // "+rim, -rrm", &
2270 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2271 0 : k = k + 1
2272 :
2273 : case ('MNUCCD')
2274 0 : stats_metadata%iMNUCCD = k
2275 :
2276 : call stat_assign( var_index=stats_metadata%iMNUCCD, var_name="MNUCCD", &
2277 : var_description="MNUCCD, Freezing of aerosol, +rim, -rvm", &
2278 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2279 0 : k = k + 1
2280 :
2281 : case ('PRACI')
2282 0 : stats_metadata%iPRACI = k
2283 :
2284 : call stat_assign( var_index=stats_metadata%iPRACI, var_name="PRACI", &
2285 : var_description="PRACI, Collection of cloud ice by rain, +rgm, -rim", &
2286 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2287 0 : k = k + 1
2288 :
2289 : case ('PRACIS')
2290 0 : stats_metadata%iPRACIS = k
2291 :
2292 : call stat_assign( var_index=stats_metadata%iPRACIS, var_name="PRACIS", &
2293 : var_description="PRACIS, Collection of cloud ice by rain, +rsm, -rim", &
2294 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2295 0 : k = k + 1
2296 :
2297 : case ('EPRD')
2298 0 : stats_metadata%iEPRD = k
2299 :
2300 : call stat_assign( var_index=stats_metadata%iEPRD, var_name="EPRD", &
2301 : var_description="EPRD, Negative of sublimation of cloud ice, +rim, -rvm", &
2302 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2303 0 : k = k + 1
2304 :
2305 : case ('MNUCCR')
2306 0 : stats_metadata%iMNUCCR = k
2307 :
2308 : call stat_assign( var_index=stats_metadata%iMNUCCR, var_name="MNUCCR", &
2309 : var_description="MNUCCR, Contact freezing of rain droplets, +rgm, -rrm", &
2310 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2311 0 : k = k + 1
2312 :
2313 : case ('PIACR')
2314 0 : stats_metadata%iPIACR = k
2315 :
2316 : call stat_assign( var_index=stats_metadata%iPIACR, var_name="PIACR", &
2317 : var_description="PIACR, Collection of cloud ice by rain, +rgm, -rrm", &
2318 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2319 0 : k = k + 1
2320 :
2321 : case ('PIACRS')
2322 0 : stats_metadata%iPIACRS = k
2323 :
2324 : call stat_assign( var_index=stats_metadata%iPIACRS, var_name="PIACRS", &
2325 : var_description="PIACRS, Collection of cloud ice by rain, +rsm, -rrm", &
2326 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2327 0 : k = k + 1
2328 :
2329 : case ('PGRACS')
2330 0 : stats_metadata%iPGRACS = k
2331 :
2332 : call stat_assign( var_index=stats_metadata%iPGRACS, var_name="PGRACS", &
2333 : var_description="PGRACS, Collection of rain by snow, +rgm, -rrm", &
2334 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2335 0 : k = k + 1
2336 :
2337 : case ('PRDS')
2338 0 : stats_metadata%iPRDS = k
2339 :
2340 : call stat_assign( var_index=stats_metadata%iPRDS, var_name="PRDS", &
2341 : var_description="PRDS, Depositional growth of snow, +rsm, -rvm", &
2342 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2343 0 : k = k + 1
2344 :
2345 : case ('EPRDS')
2346 0 : stats_metadata%iEPRDS = k
2347 :
2348 : call stat_assign( var_index=stats_metadata%iEPRDS, var_name="EPRDS", &
2349 : var_description="EPRDS, Negative of sublimation of snow, +rsm, -rvm", &
2350 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2351 0 : k = k + 1
2352 :
2353 : case ('PSACR')
2354 0 : stats_metadata%iPSACR = k
2355 :
2356 : call stat_assign( var_index=stats_metadata%iPSACR, var_name="PSACR", &
2357 : var_description="PSACR, Collection of snow by rain, +rgm, -rsm", &
2358 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2359 0 : k = k + 1
2360 :
2361 : case ('PRDG')
2362 0 : stats_metadata%iPRDG = k
2363 :
2364 : call stat_assign( var_index=stats_metadata%iPRDG, var_name="PRDG", &
2365 : var_description="PRDG, Depositional growth of graupel, +rgm, -rvm", &
2366 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2367 0 : k = k + 1
2368 :
2369 : case ('EPRDG')
2370 0 : stats_metadata%iEPRDG = k
2371 :
2372 : call stat_assign( var_index=stats_metadata%iEPRDG, var_name="EPRDG", &
2373 : var_description="EPRDG, Negative of sublimation of graupel, +rgm, -rvm", &
2374 0 : var_units="(kg/kg)/s", l_silhs=.true., grid_kind=stats_zt )
2375 0 : k = k + 1
2376 :
2377 : case ('NGSTEN')
2378 0 : stats_metadata%iNGSTEN = k
2379 :
2380 : call stat_assign( var_index=stats_metadata%iNGSTEN, var_name="NGSTEN", &
2381 : var_description="NGSTEN, Graupel sedimentation tendency", var_units="(#/kg/s)", &
2382 0 : l_silhs=.true., grid_kind=stats_zt )
2383 0 : k = k + 1
2384 :
2385 : case ('NRSTEN')
2386 0 : stats_metadata%iNRSTEN = k
2387 :
2388 : call stat_assign( var_index=stats_metadata%iNRSTEN, var_name="NRSTEN", &
2389 : var_description="NRSTEN, Rain sedimentation tendency", var_units="(#/kg/s)", &
2390 0 : l_silhs=.true., grid_kind=stats_zt )
2391 0 : k = k + 1
2392 :
2393 : case ('NISTEN')
2394 0 : stats_metadata%iNISTEN = k
2395 :
2396 : call stat_assign( var_index=stats_metadata%iNISTEN, var_name="NISTEN", &
2397 : var_description="NISTEN, Cloud ice sedimentation tendency", var_units="(#/kg/s)", &
2398 0 : l_silhs=.true., grid_kind=stats_zt )
2399 0 : k = k + 1
2400 :
2401 : case ('NSSTEN')
2402 0 : stats_metadata%iNSSTEN = k
2403 :
2404 : call stat_assign( var_index=stats_metadata%iNSSTEN, var_name="NSSTEN", &
2405 : var_description="NSSTEN, Snow sedimentation tendency", var_units="(#/kg/s)", &
2406 0 : l_silhs=.true., grid_kind=stats_zt )
2407 0 : k = k + 1
2408 :
2409 : case ('NCSTEN')
2410 0 : stats_metadata%iNCSTEN = k
2411 :
2412 : call stat_assign( var_index=stats_metadata%iNCSTEN, var_name="NCSTEN", &
2413 : var_description="NCSTEN, Cloud water sedimentation tendency", &
2414 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2415 0 : k = k + 1
2416 :
2417 : case ('NPRC1')
2418 0 : stats_metadata%iNPRC1 = k
2419 :
2420 : call stat_assign( var_index=stats_metadata%iNPRC1, var_name="NPRC1", &
2421 : var_description="NPRC1, Change in Nrm due to autoconversion of droplets, +Nrm", &
2422 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2423 0 : k = k + 1
2424 :
2425 : case ('NRAGG')
2426 0 : stats_metadata%iNRAGG = k
2427 :
2428 : call stat_assign( var_index=stats_metadata%iNRAGG, var_name="NRAGG", &
2429 : var_description="NRAGG, Change in Nrm due to self-collection of raindrops, +Nrm", &
2430 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2431 0 : k = k + 1
2432 :
2433 : case ('NPRACG')
2434 0 : stats_metadata%iNPRACG = k
2435 :
2436 : call stat_assign( var_index=stats_metadata%iNPRACG, var_name="NPRACG", &
2437 : var_description="NPRACG, Collection of rainwater by graupel, -Nrm", &
2438 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2439 0 : k = k + 1
2440 :
2441 : case ('NSUBR')
2442 0 : stats_metadata%iNSUBR = k
2443 :
2444 : call stat_assign( var_index=stats_metadata%iNSUBR, var_name="NSUBR", &
2445 : var_description="NSUBR, Loss of Nrm by evaporation, +Nrm", var_units="(#/kg/s)", &
2446 0 : l_silhs=.true., grid_kind=stats_zt )
2447 0 : k = k + 1
2448 :
2449 : case ('NSMLTR')
2450 0 : stats_metadata%iNSMLTR = k
2451 :
2452 : call stat_assign( var_index=stats_metadata%iNSMLTR, var_name="NSMLTR", &
2453 : var_description="NSMLTR, Melting of snow to form rain, -Nrm", &
2454 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2455 0 : k = k + 1
2456 :
2457 : case ('NGMLTR')
2458 0 : stats_metadata%iNGMLTR = k
2459 :
2460 : call stat_assign( var_index=stats_metadata%iNGMLTR, var_name="NGMLTR", &
2461 : var_description="NGMLTR, Melting of graupel to form rain, -Nrm", &
2462 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2463 0 : k = k + 1
2464 :
2465 : case ('NPRACS')
2466 0 : stats_metadata%iNPRACS = k
2467 :
2468 : call stat_assign( var_index=stats_metadata%iNPRACS, var_name="NPRACS", &
2469 : var_description="NPRACS, Collection of rainwater by snow, -Nrm", &
2470 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2471 0 : k = k + 1
2472 :
2473 : case ('NNUCCR')
2474 0 : stats_metadata%iNNUCCR = k
2475 :
2476 : call stat_assign( var_index=stats_metadata%iNNUCCR, var_name="NNUCCR", &
2477 : var_description="NNUCCR, Contact freezing of rain, +Ngm, -Nrm", &
2478 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2479 0 : k = k + 1
2480 :
2481 : case ('NIACR')
2482 0 : stats_metadata%iNIACR = k
2483 :
2484 : call stat_assign( var_index=stats_metadata%iNIACR, var_name="NIACR", &
2485 : var_description="NIACR, Collection of cloud ice by rain, +Ngm, -Nrm, -Nim", &
2486 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2487 0 : k = k + 1
2488 :
2489 : case ('NIACRS')
2490 0 : stats_metadata%iNIACRS = k
2491 :
2492 : call stat_assign( var_index=stats_metadata%iNIACRS, var_name="NIACRS", &
2493 : var_description="NIACRS, Collection of cloud ice by rain, +Nsm, -Nrm, -Nim", &
2494 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2495 0 : k = k + 1
2496 :
2497 : case ('NGRACS')
2498 0 : stats_metadata%iNGRACS = k
2499 :
2500 : call stat_assign( var_index=stats_metadata%iNGRACS, var_name="NGRACS", &
2501 : var_description="NGRACS, Collection of rain by snow, +Ngm, -Nrm, -Nsm", &
2502 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2503 0 : k = k + 1
2504 :
2505 : case ('NSMLTS')
2506 0 : stats_metadata%iNSMLTS= k
2507 :
2508 : call stat_assign( var_index=stats_metadata%iNSMLTS, var_name="NSMLTS", &
2509 : var_description="NSMLTS, Melting of snow, +Nsm", var_units="(#/kg/s)", &
2510 0 : l_silhs=.true., grid_kind=stats_zt )
2511 0 : k = k + 1
2512 :
2513 : case ('NSAGG')
2514 0 : stats_metadata%iNSAGG= k
2515 :
2516 : call stat_assign( var_index=stats_metadata%iNSAGG, var_name="NSAGG", &
2517 : var_description="NSAGG, Self collection of snow, +Nsm", var_units="(#/kg/s)", &
2518 0 : l_silhs=.true., grid_kind=stats_zt )
2519 :
2520 0 : k = k + 1
2521 :
2522 : case ('NPRCI')
2523 0 : stats_metadata%iNPRCI= k
2524 :
2525 : call stat_assign( var_index=stats_metadata%iNPRCI, var_name="NPRCI", &
2526 : var_description="NPRCI, Autoconversion of cloud ice to snow, -Nim, +Nsm", &
2527 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2528 0 : k = k + 1
2529 :
2530 : case ('NSCNG')
2531 0 : stats_metadata%iNSCNG= k
2532 :
2533 : call stat_assign( var_index=stats_metadata%iNSCNG, var_name="NSCNG", &
2534 : var_description="NSCNG, Conversion of snow to graupel, +Ngm, -Nsm", &
2535 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2536 0 : k = k + 1
2537 :
2538 : case ('NSUBS')
2539 0 : stats_metadata%iNSUBS= k
2540 :
2541 : call stat_assign( var_index=stats_metadata%iNSUBS, var_name="NSUBS", &
2542 : var_description="NSUBS, Loss of snow due to sublimation, +Nsm", &
2543 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2544 0 : k = k + 1
2545 :
2546 : case ('PRC')
2547 0 : stats_metadata%iPRC= k
2548 :
2549 : call stat_assign( var_index=stats_metadata%iPRC, var_name="PRC", &
2550 : var_description="PRC, Autoconversion +rrm -rcm", var_units="(kg/kg/s)", &
2551 0 : l_silhs=.true., grid_kind=stats_zt )
2552 0 : k = k + 1
2553 :
2554 : case ('PRA')
2555 0 : stats_metadata%iPRA= k
2556 :
2557 : call stat_assign( var_index=stats_metadata%iPRA, var_name="PRA", &
2558 : var_description="PRA, Accretion +rrm -rcm", var_units="(kg/kg/s)", &
2559 0 : l_silhs=.true., grid_kind=stats_zt )
2560 0 : k = k + 1
2561 :
2562 : case ('PRE')
2563 0 : stats_metadata%iPRE= k
2564 :
2565 : call stat_assign( var_index=stats_metadata%iPRE, var_name="PRE", &
2566 : var_description="PRE, Evaporation of rain -rrm", var_units="(kg/kg/s)", &
2567 0 : l_silhs=.true., grid_kind=stats_zt )
2568 0 : k = k + 1
2569 :
2570 : case ('PCC')
2571 0 : stats_metadata%iPCC= k
2572 :
2573 : call stat_assign( var_index=stats_metadata%iPCC, var_name="PCC", &
2574 : var_description="PCC, Satuation adjustment -rvm +rcm", var_units="(kg/kg/s)", &
2575 0 : l_silhs=.true., grid_kind=stats_zt )
2576 0 : k = k + 1
2577 :
2578 : case ('NNUCCC')
2579 0 : stats_metadata%iNNUCCC= k
2580 :
2581 : call stat_assign( var_index=stats_metadata%iNNUCCC, var_name="NNUCCC", &
2582 : var_description="NNUCCC, Contact freezing of drops, -Ncm + Nim", &
2583 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2584 0 : k = k + 1
2585 :
2586 : case ('NPSACWS')
2587 0 : stats_metadata%iNPSACWS= k
2588 :
2589 : call stat_assign( var_index=stats_metadata%iNPSACWS, var_name="NPSACWS", &
2590 : var_description="NPSACWS, Droplet accretion by snow, -Ncm", var_units="(#/kg/s)", &
2591 0 : l_silhs=.true., grid_kind=stats_zt )
2592 0 : k = k + 1
2593 :
2594 : case ('NPRA')
2595 0 : stats_metadata%iNPRA= k
2596 :
2597 : call stat_assign( var_index=stats_metadata%iNPRA, var_name="NPRA", &
2598 : var_description="NPRA, Droplet accretion by rain, -Ncm", var_units="(#/kg/s)", &
2599 0 : l_silhs=.true., grid_kind=stats_zt )
2600 0 : k = k + 1
2601 :
2602 : case ('NPRC')
2603 0 : stats_metadata%iNPRC= k
2604 :
2605 : call stat_assign( var_index=stats_metadata%iNPRC, var_name="NPRC", &
2606 : var_description="NPRC, Autoconversion of cloud drops, -Ncm", &
2607 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2608 0 : k = k + 1
2609 :
2610 : case ('NPSACWI')
2611 0 : stats_metadata%iNPSACWI= k
2612 :
2613 : call stat_assign( var_index=stats_metadata%iNPSACWI, var_name="NPSACWI", &
2614 : var_description="NPSACWI, Droplet accretion by cloud ice, -Ncm", &
2615 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2616 0 : k = k + 1
2617 :
2618 : case ('NPSACWG')
2619 0 : stats_metadata%iNPSACWG= k
2620 :
2621 : call stat_assign( var_index=stats_metadata%iNPSACWG, var_name="NPSACWG", &
2622 : var_description="NPSACWG, Collection of cloud droplets by graupel, -Ncm", &
2623 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2624 0 : k = k + 1
2625 :
2626 : case ('NPRAI')
2627 0 : stats_metadata%iNPRAI= k
2628 :
2629 : call stat_assign( var_index=stats_metadata%iNPRAI, var_name="NPRAI", &
2630 : var_description="NPRAI, Accretion of cloud ice by snow, -Nim", &
2631 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2632 0 : k = k + 1
2633 :
2634 : case ('NMULTS')
2635 0 : stats_metadata%iNMULTS= k
2636 :
2637 : call stat_assign( var_index=stats_metadata%iNMULTS, var_name="NMULTS", &
2638 : var_description="NMULTS, Ice multiplication due to riming of cloud droplets " &
2639 : // "by snow, +Nim", &
2640 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2641 0 : k = k + 1
2642 :
2643 : case ('NMULTG')
2644 0 : stats_metadata%iNMULTG= k
2645 :
2646 : call stat_assign( var_index=stats_metadata%iNMULTG, var_name="NMULTG", &
2647 : var_description="NMULTG, Ice multiplication due to accretion of droplets " &
2648 : // "by graupel, +Nim", &
2649 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2650 0 : k = k + 1
2651 :
2652 : case ('NMULTR')
2653 0 : stats_metadata%iNMULTR= k
2654 :
2655 : call stat_assign( var_index=stats_metadata%iNMULTR, var_name="NMULTR", &
2656 : var_description="Ice multiplication due to riming of rain by snow, +Nim", &
2657 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2658 0 : k = k + 1
2659 :
2660 : case ('NMULTRG')
2661 0 : stats_metadata%iNMULTRG= k
2662 :
2663 : call stat_assign( var_index=stats_metadata%iNMULTRG, var_name="NMULTRG", &
2664 : var_description="NMULTR, Ice multiplication due to accretion of rain by " &
2665 : // "graupel, +Nim", &
2666 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2667 0 : k = k + 1
2668 :
2669 : case ('NNUCCD')
2670 0 : stats_metadata%iNNUCCD= k
2671 :
2672 : call stat_assign( var_index=stats_metadata%iNNUCCD, var_name="NNUCCD", &
2673 : var_description="NNUCCD, Primary ice nucleation, freezing of aerosol, +Nim", &
2674 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2675 0 : k = k + 1
2676 :
2677 : case ('NSUBI')
2678 0 : stats_metadata%iNSUBI= k
2679 :
2680 : call stat_assign( var_index=stats_metadata%iNSUBI, var_name="NSUBI", &
2681 : var_description="NSUBI, Loss of ice due to sublimation, -Nim", &
2682 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2683 0 : k = k + 1
2684 :
2685 : case ('NGMLTG')
2686 0 : stats_metadata%iNGMLTG= k
2687 :
2688 : call stat_assign( var_index=stats_metadata%iNGMLTG, var_name="NGMLTG", &
2689 : var_description="NGMLTG, Loss of graupel due to melting, -Ngm", &
2690 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2691 0 : k = k + 1
2692 :
2693 : case ('NSUBG')
2694 0 : stats_metadata%iNSUBG= k
2695 :
2696 : call stat_assign( var_index=stats_metadata%iNSUBG, var_name="NSUBG", &
2697 : var_description="NSUBG, Loss of graupel due to sublimation, -Ngm", &
2698 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2699 0 : k = k + 1
2700 :
2701 : case ('NACT')
2702 0 : stats_metadata%iNACT= k
2703 :
2704 : call stat_assign( var_index=stats_metadata%iNACT, var_name="NACT", &
2705 : var_description="NACT, Cloud drop formation by aerosol activation, +Ncm", &
2706 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2707 0 : k = k + 1
2708 :
2709 : case ('SIZEFIX_NR')
2710 0 : stats_metadata%iSIZEFIX_NR= k
2711 :
2712 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NR, var_name="SIZEFIX_NR", &
2713 : var_description="SIZEFIX_NR, Adjust rain # conc. for large/small drops, +Nrm", &
2714 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2715 0 : k = k + 1
2716 :
2717 : case ('SIZEFIX_NC')
2718 0 : stats_metadata%iSIZEFIX_NC= k
2719 :
2720 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NC, var_name="SIZEFIX_NC", &
2721 : var_description="SIZEFIX_NC, Adjust cloud # conc. for large/small drops, +Ncm", &
2722 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2723 0 : k = k + 1
2724 :
2725 : case ('SIZEFIX_NI')
2726 0 : stats_metadata%iSIZEFIX_NI= k
2727 :
2728 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NI, var_name="SIZEFIX_NI", &
2729 : var_description="SIZEFIX_NI, Adjust ice # conc. for large/small drops, +Nim", &
2730 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2731 0 : k = k + 1
2732 :
2733 : case ('SIZEFIX_NS')
2734 0 : stats_metadata%iSIZEFIX_NS= k
2735 :
2736 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NS, var_name="SIZEFIX_NS", &
2737 : var_description="SIZEFIX_NS, Adjust snow # conc. for large/small drops, +Nsm", &
2738 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2739 0 : k = k + 1
2740 :
2741 : case ('SIZEFIX_NG')
2742 0 : stats_metadata%iSIZEFIX_NG= k
2743 :
2744 : call stat_assign( var_index=stats_metadata%iSIZEFIX_NG, var_name="SIZEFIX_NG", &
2745 : var_description="SIZEFIX_NG, Adjust graupel # conc. for large/small drops,+Ngm",&
2746 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2747 0 : k = k + 1
2748 :
2749 : case ('NEGFIX_NR')
2750 0 : stats_metadata%iNEGFIX_NR= k
2751 :
2752 : call stat_assign( var_index=stats_metadata%iNEGFIX_NR, var_name="NEGFIX_NR", &
2753 : var_description="NEGFIX_NR, Removal of negative rain drop number conc., -Nrm", &
2754 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2755 0 : k = k + 1
2756 :
2757 : case ('NEGFIX_NC')
2758 0 : stats_metadata%iNEGFIX_NC= k
2759 :
2760 : call stat_assign( var_index=stats_metadata%iNEGFIX_NC, var_name="NEGFIX_NC", &
2761 : var_description="NEGFIX_NC, Removal of negative cloud drop number conc., -Ncm", &
2762 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2763 0 : k = k + 1
2764 :
2765 : case ('NEGFIX_NI')
2766 0 : stats_metadata%iNEGFIX_NI= k
2767 :
2768 : call stat_assign( var_index=stats_metadata%iNEGFIX_NI, var_name="NEGFIX_NI", &
2769 : var_description="NEGFIX_NI, Removal of negative ice number conc., -Nim", &
2770 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2771 0 : k = k + 1
2772 :
2773 : case ('NEGFIX_NS')
2774 0 : stats_metadata%iNEGFIX_NS= k
2775 :
2776 : call stat_assign( var_index=stats_metadata%iNEGFIX_NS, var_name="NEGFIX_NS", &
2777 : var_description="NEGFIX_NS, Removal of negative snow number conc, -Nsm", &
2778 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2779 0 : k = k + 1
2780 :
2781 : case ('NEGFIX_NG')
2782 0 : stats_metadata%iNEGFIX_NG= k
2783 :
2784 : call stat_assign( var_index=stats_metadata%iNEGFIX_NG, var_name="NEGFIX_NG", &
2785 : var_description="NEGFIX_NG, Removal of negative graupel number conc., -Ngm", &
2786 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2787 0 : k = k + 1
2788 :
2789 : case ('NIM_MORR_CL')
2790 0 : stats_metadata%iNIM_MORR_CL= k
2791 :
2792 : call stat_assign( var_index=stats_metadata%iNIM_MORR_CL, var_name="NIM_MORR_CL", &
2793 : var_description="NIM_MORR_CL, Clipping of large ice concentrations, -Nim", &
2794 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2795 0 : k = k + 1
2796 :
2797 : case ('QC_INST')
2798 0 : stats_metadata%iQC_INST= k
2799 :
2800 : call stat_assign( var_index=stats_metadata%iQC_INST, var_name="QC_INST", &
2801 : var_description="QC_INST, Change in mixing ratio due to instantaneous " &
2802 : // "processes +rcm", &
2803 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2804 0 : k = k + 1
2805 :
2806 : case ('QR_INST')
2807 0 : stats_metadata%iQR_INST= k
2808 :
2809 : call stat_assign( var_index=stats_metadata%iQR_INST, var_name="QR_INST", &
2810 : var_description="QR_INST, Change in mixing ratio from instantaneous processes, +rrm",&
2811 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2812 0 : k = k + 1
2813 :
2814 : case ('QI_INST')
2815 0 : stats_metadata%iQI_INST= k
2816 :
2817 : call stat_assign( var_index=stats_metadata%iQI_INST, var_name="QI_INST", &
2818 : var_description="QI_INST, Change in mixing ratio from instantaneous processes +rim",&
2819 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2820 0 : k = k + 1
2821 :
2822 : case ('QS_INST')
2823 0 : stats_metadata%iQS_INST= k
2824 :
2825 : call stat_assign( var_index=stats_metadata%iQS_INST, var_name="QS_INST", &
2826 : var_description="QS_INST, Change in mixing ratio from instantaneous processes +rsm",&
2827 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2828 0 : k = k + 1
2829 :
2830 : case ('QG_INST')
2831 0 : stats_metadata%iQG_INST= k
2832 :
2833 : call stat_assign( var_index=stats_metadata%iQG_INST, var_name="QG_INST", &
2834 : var_description="QG_INST, Change in mixing ratio from instantaneous processes +rgm",&
2835 0 : var_units="(kg/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2836 0 : k = k + 1
2837 :
2838 : case ('NC_INST')
2839 0 : stats_metadata%iNC_INST= k
2840 :
2841 : call stat_assign( var_index=stats_metadata%iNC_INST, var_name="NC_INST", &
2842 : var_description="NC_INST, Change in # conc. from instantaneous processes +Ncm", &
2843 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2844 0 : k = k + 1
2845 :
2846 : case ('NR_INST')
2847 0 : stats_metadata%iNR_INST= k
2848 :
2849 : call stat_assign( var_index=stats_metadata%iNR_INST, var_name="NR_INST", &
2850 : var_description="NR_INST, Change in # conc. from instantaneous processes +Nrm", &
2851 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2852 0 : k = k + 1
2853 :
2854 : case ('NI_INST')
2855 0 : stats_metadata%iNI_INST= k
2856 :
2857 : call stat_assign( var_index=stats_metadata%iNI_INST, var_name="NI_INST", &
2858 : var_description="NI_INST, Change in # conc. from instantaneous processes +Nim", &
2859 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2860 0 : k = k + 1
2861 :
2862 : case ('NS_INST')
2863 0 : stats_metadata%iNS_INST= k
2864 :
2865 : call stat_assign( var_index=stats_metadata%iNS_INST, var_name="NS_INST", &
2866 : var_description="NS_INST, Change in # conc. from instantaneous processes +Nsm", &
2867 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2868 0 : k = k + 1
2869 :
2870 : case ('NG_INST')
2871 0 : stats_metadata%iNG_INST= k
2872 :
2873 : call stat_assign( var_index=stats_metadata%iNG_INST, var_name="NG_INST", &
2874 : var_description="NG_INST, Change in # conc. from instantaneous processes +Ngm", &
2875 0 : var_units="(#/kg/s)", l_silhs=.true., grid_kind=stats_zt )
2876 0 : k = k + 1
2877 :
2878 :
2879 : case ('T_in_K_mc')
2880 0 : stats_metadata%iT_in_K_mc= k
2881 :
2882 : call stat_assign( var_index=stats_metadata%iT_in_K_mc, var_name="T_in_K_mc", &
2883 : var_description="T_in_K_mc, Temperature tendency from Morrison microphysics", &
2884 0 : var_units="(K/s)", l_silhs=.true., grid_kind=stats_zt )
2885 0 : k = k + 1
2886 :
2887 : case ('w_KK_evap_covar_zt')
2888 0 : stats_metadata%iw_KK_evap_covar_zt = k
2889 :
2890 : call stat_assign( var_index=stats_metadata%iw_KK_evap_covar_zt, var_name="w_KK_evap_covar_zt", &
2891 : var_description="w_KK_evap_covar_zt, Covariance of w and KK evaporation rate", &
2892 0 : var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
2893 0 : k = k + 1
2894 :
2895 : case ('rt_KK_evap_covar_zt')
2896 0 : stats_metadata%irt_KK_evap_covar_zt = k
2897 :
2898 : call stat_assign( var_index=stats_metadata%irt_KK_evap_covar_zt, var_name="rt_KK_evap_covar_zt", &
2899 : var_description="rt_KK_evap_covar_zt, Covariance of r_t and KK evaporation rate", &
2900 0 : var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
2901 0 : k = k + 1
2902 :
2903 : case ('thl_KK_evap_covar_zt')
2904 0 : stats_metadata%ithl_KK_evap_covar_zt = k
2905 :
2906 : call stat_assign( var_index=stats_metadata%ithl_KK_evap_covar_zt, var_name="thl_KK_evap_covar_zt", &
2907 : var_description="thl_KK_evap_covar_zt, Covariance of theta_l and KK " &
2908 : // "evaporation rate", &
2909 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2910 0 : k = k + 1
2911 :
2912 : case ('w_KK_auto_covar_zt')
2913 0 : stats_metadata%iw_KK_auto_covar_zt = k
2914 :
2915 : call stat_assign( var_index=stats_metadata%iw_KK_auto_covar_zt, var_name="w_KK_auto_covar_zt", &
2916 : var_description="w_KK_auto_covar_zt, Covariance of w and KK autoconversion rate", &
2917 0 : var_units="m*(kg/kg)/s^2", l_silhs=.false., grid_kind=stats_zt )
2918 0 : k = k + 1
2919 :
2920 : case ('rt_KK_auto_covar_zt')
2921 0 : stats_metadata%irt_KK_auto_covar_zt = k
2922 :
2923 : call stat_assign( var_index=stats_metadata%irt_KK_auto_covar_zt, var_name="rt_KK_auto_covar_zt", &
2924 : var_description="rt_KK_auto_covar_zt, Covariance of r_t and KK autoconversion rate",&
2925 0 : var_units="(kg/kg)^2/s", l_silhs=.false., grid_kind=stats_zt )
2926 0 : k = k + 1
2927 :
2928 : case ('thl_KK_auto_covar_zt')
2929 0 : stats_metadata%ithl_KK_auto_covar_zt = k
2930 :
2931 : call stat_assign( var_index=stats_metadata%ithl_KK_auto_covar_zt, var_name="thl_KK_auto_covar_zt", &
2932 : var_description="thl_KK_auto_covar_zt, Covariance of theta_l and " &
2933 : // "KK autoconversion rate", &
2934 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2935 0 : k = k + 1
2936 :
2937 : case ('w_KK_accr_covar_zt')
2938 0 : stats_metadata%iw_KK_accr_covar_zt = k
2939 :
2940 : call stat_assign( var_index=stats_metadata%iw_KK_accr_covar_zt, var_name="w_KK_accr_covar_zt", &
2941 : var_description="w_KK_accr_covar_zt, Covariance of w and KK accretion rate", &
2942 : var_units="m*(kg/kg)/s^2", &
2943 0 : l_silhs=.false., grid_kind=stats_zt )
2944 0 : k = k + 1
2945 :
2946 : case ('rt_KK_accr_covar_zt')
2947 0 : stats_metadata%irt_KK_accr_covar_zt = k
2948 :
2949 : call stat_assign( var_index=stats_metadata%irt_KK_accr_covar_zt, var_name="rt_KK_accr_covar_zt", &
2950 : var_description="rt_KK_accr_covar_zt, Covariance of r_t and KK accretion rate", &
2951 : var_units="(kg/kg)^2/s", &
2952 0 : l_silhs=.false., grid_kind=stats_zt )
2953 0 : k = k + 1
2954 :
2955 : case ('thl_KK_accr_covar_zt')
2956 0 : stats_metadata%ithl_KK_accr_covar_zt = k
2957 :
2958 : call stat_assign( var_index=stats_metadata%ithl_KK_accr_covar_zt, var_name="thl_KK_accr_covar_zt", &
2959 : var_description="thl_KK_accr_covar_zt, Covariance of theta_l and KK accretion rate",&
2960 0 : var_units="K*(kg/kg)/s", l_silhs=.false., grid_kind=stats_zt )
2961 0 : k = k + 1
2962 :
2963 : case ('rr_KK_mvr_covar_zt')
2964 0 : stats_metadata%irr_KK_mvr_covar_zt = k
2965 :
2966 : call stat_assign( var_index=stats_metadata%irr_KK_mvr_covar_zt, var_name="rr_KK_mvr_covar_zt", &
2967 : var_description="rr_KK_mvr_covar_zt, Covariance of r_r and KK rain drop mean " &
2968 : // "volume radius", &
2969 0 : var_units="(kg/kg)m", l_silhs=.false., grid_kind=stats_zt )
2970 0 : k = k + 1
2971 :
2972 : case ('Nr_KK_mvr_covar_zt')
2973 0 : stats_metadata%iNr_KK_mvr_covar_zt = k
2974 :
2975 : call stat_assign( var_index=stats_metadata%iNr_KK_mvr_covar_zt, var_name="Nr_KK_mvr_covar_zt", &
2976 : var_description="Nr_KK_mvr_covar_zt, Covariance of N_r and KK rain drop mean " &
2977 : // "volume radius", &
2978 0 : var_units="(num/kg)m", l_silhs=.false., grid_kind=stats_zt )
2979 0 : k = k + 1
2980 :
2981 : case ('KK_mvr_variance_zt')
2982 0 : stats_metadata%iKK_mvr_variance_zt = k
2983 :
2984 : call stat_assign( var_index=stats_metadata%iKK_mvr_variance_zt, var_name="KK_mvr_variance_zt", &
2985 : var_description="KK_mvr_variance_zt, Variance of KK rain drop mean volume radius", &
2986 0 : var_units="m^2", l_silhs=.false., grid_kind=stats_zt )
2987 0 : k = k + 1
2988 :
2989 : case ('vm_bt')
2990 0 : stats_metadata%ivm_bt = k
2991 :
2992 : call stat_assign( var_index=stats_metadata%ivm_bt, var_name="vm_bt", &
2993 : var_description="vm_bt, vm budget: vm time tendency", var_units="m s^{-2}", &
2994 0 : l_silhs=.false., grid_kind=stats_zt )
2995 0 : k = k + 1
2996 :
2997 : case ('vm_ma')
2998 0 : stats_metadata%ivm_ma = k
2999 : call stat_assign( var_index=stats_metadata%ivm_ma, var_name="vm_ma", &
3000 : var_description="vm_ma, vm budget: vm vertical mean advection", &
3001 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3002 0 : k = k + 1
3003 :
3004 : case ('vm_gf')
3005 0 : stats_metadata%ivm_gf = k
3006 :
3007 : call stat_assign( var_index=stats_metadata%ivm_gf, var_name="vm_gf", &
3008 : var_description="vm_gf, vm budget: vm geostrophic forcing", &
3009 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3010 0 : k = k + 1
3011 :
3012 : case ('vm_cf')
3013 0 : stats_metadata%ivm_cf = k
3014 :
3015 : call stat_assign( var_index=stats_metadata%ivm_cf, var_name="vm_cf", &
3016 : var_description="vm_cf, vm budget: vm coriolis forcing", var_units="m s^{-2}", &
3017 0 : l_silhs=.false., grid_kind=stats_zt )
3018 0 : k = k + 1
3019 :
3020 : case ('vm_ta')
3021 0 : stats_metadata%ivm_ta = k
3022 :
3023 : call stat_assign( var_index=stats_metadata%ivm_ta, var_name="vm_ta", &
3024 : var_description="vm_ta, vm budget: vm turbulent transport", &
3025 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3026 0 : k = k + 1
3027 :
3028 : case ('vm_f')
3029 0 : stats_metadata%ivm_f = k
3030 : call stat_assign( var_index=stats_metadata%ivm_f, var_name="vm_f", &
3031 : var_description="vm_f, vm budget: vm forcing", var_units="m s^{-2}", &
3032 0 : l_silhs=.false., grid_kind=stats_zt )
3033 0 : k = k + 1
3034 :
3035 : case ('vm_sdmp')
3036 0 : stats_metadata%ivm_sdmp = k
3037 : call stat_assign( var_index=stats_metadata%ivm_sdmp, var_name="vm_sdmp", &
3038 : var_description="vm_sdmp, vm budget: vm sponge damping", var_units="m s^{-2}", &
3039 0 : l_silhs=.false., grid_kind=stats_zt )
3040 0 : k = k + 1
3041 :
3042 : case ('vm_ndg')
3043 0 : stats_metadata%ivm_ndg = k
3044 : call stat_assign( var_index=stats_metadata%ivm_ndg, var_name="vm_ndg", &
3045 : var_description="vm_ndg, vm budget: vm nudging", var_units="m s^{-2}", &
3046 0 : l_silhs=.false., grid_kind=stats_zt )
3047 0 : k = k + 1
3048 :
3049 : case ('vm_mfl')
3050 0 : stats_metadata%ivm_mfl = k
3051 : call stat_assign( var_index=stats_metadata%ivm_mfl, var_name="vm_mfl", &
3052 : var_description="vm_mfl, vm budget: vm monotonic flux limiter", &
3053 : var_units="m s^{-2}", &
3054 0 : l_silhs=.false., grid_kind=stats_zt )
3055 0 : k = k + 1
3056 :
3057 : case ('um_bt')
3058 0 : stats_metadata%ium_bt = k
3059 :
3060 : call stat_assign( var_index=stats_metadata%ium_bt, var_name="um_bt", &
3061 : var_description="um_bt, um budget: um time tendency", var_units="m s^{-2}", &
3062 0 : l_silhs=.false., grid_kind=stats_zt )
3063 0 : k = k + 1
3064 :
3065 : case ('um_ma')
3066 0 : stats_metadata%ium_ma = k
3067 :
3068 : call stat_assign( var_index=stats_metadata%ium_ma, var_name="um_ma", &
3069 : var_description="um_ma, um budget: um vertical mean advection", &
3070 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3071 0 : k = k + 1
3072 :
3073 : case ('um_gf')
3074 0 : stats_metadata%ium_gf = k
3075 : call stat_assign( var_index=stats_metadata%ium_gf, var_name="um_gf", &
3076 : var_description="um_gf, um budget: um geostrophic forcing", &
3077 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3078 0 : k = k + 1
3079 :
3080 : case ('um_cf')
3081 0 : stats_metadata%ium_cf = k
3082 : call stat_assign( var_index=stats_metadata%ium_cf, var_name="um_cf", &
3083 : var_description="um_cf, um budget: um coriolis forcing", var_units="m s^{-2}", &
3084 0 : l_silhs=.false., grid_kind=stats_zt )
3085 0 : k = k + 1
3086 :
3087 : case ('um_ta')
3088 0 : stats_metadata%ium_ta = k
3089 : call stat_assign( var_index=stats_metadata%ium_ta, var_name="um_ta", &
3090 : var_description="um_ta, um budget: um turbulent advection", &
3091 0 : var_units="m s^{-2}", l_silhs=.false., grid_kind=stats_zt )
3092 0 : k = k + 1
3093 :
3094 : case ('um_f')
3095 0 : stats_metadata%ium_f = k
3096 : call stat_assign( var_index=stats_metadata%ium_f, var_name="um_f", &
3097 : var_description="um_f, um budget: um forcing", var_units="m s^{-2}", &
3098 0 : l_silhs=.false., grid_kind=stats_zt )
3099 0 : k = k + 1
3100 :
3101 : case ('um_sdmp')
3102 0 : stats_metadata%ium_sdmp = k
3103 : call stat_assign( var_index=stats_metadata%ium_sdmp, var_name="um_sdmp", &
3104 : var_description="um_sdmp, um budget: um sponge damping", var_units="m s^{-2}", &
3105 0 : l_silhs=.false., grid_kind=stats_zt )
3106 0 : k = k + 1
3107 :
3108 : case ('um_ndg')
3109 0 : stats_metadata%ium_ndg = k
3110 : call stat_assign( var_index=stats_metadata%ium_ndg, var_name="um_ndg", &
3111 : var_description="um_ndg, um budget: um nudging", var_units="m s^{-2}", &
3112 0 : l_silhs=.false., grid_kind=stats_zt )
3113 0 : k = k + 1
3114 :
3115 : case ('um_mfl')
3116 0 : stats_metadata%ium_mfl = k
3117 : call stat_assign( var_index=stats_metadata%ium_mfl, var_name="um_mfl", &
3118 : var_description="um_mfl, um budget: um monotonic flux limiter", &
3119 : var_units="m s^{-2}",&
3120 0 : l_silhs=.false., grid_kind=stats_zt )
3121 0 : k = k + 1
3122 :
3123 : case ('mixt_frac')
3124 0 : stats_metadata%imixt_frac = k
3125 : call stat_assign( var_index=stats_metadata%imixt_frac, var_name="mixt_frac", &
3126 : var_description="mixt_frac, pdf parameter: mixture fraction", var_units="count", &
3127 0 : l_silhs=.false., grid_kind=stats_zt )
3128 0 : k = k + 1
3129 :
3130 : case ('w_1')
3131 0 : stats_metadata%iw_1 = k
3132 : call stat_assign( var_index=stats_metadata%iw_1, var_name="w_1", &
3133 : var_description="w_1, pdf parameter: mean w of component 1", var_units="m/s", &
3134 0 : l_silhs=.false., grid_kind=stats_zt )
3135 :
3136 0 : k = k + 1
3137 :
3138 : case ('w_2')
3139 0 : stats_metadata%iw_2 = k
3140 :
3141 : call stat_assign( var_index=stats_metadata%iw_2, var_name="w_2", &
3142 : var_description="w_2, pdf paramete: mean w of component 2", var_units="m/s", &
3143 0 : l_silhs=.false., grid_kind=stats_zt )
3144 0 : k = k + 1
3145 :
3146 : case ('varnce_w_1')
3147 0 : stats_metadata%ivarnce_w_1 = k
3148 : call stat_assign( var_index=stats_metadata%ivarnce_w_1, var_name="varnce_w_1", &
3149 : var_description="varnce_w_1, pdf parameter: w variance of component 1", &
3150 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3151 :
3152 0 : k = k + 1
3153 :
3154 : case ('varnce_w_2')
3155 0 : stats_metadata%ivarnce_w_2 = k
3156 :
3157 : call stat_assign( var_index=stats_metadata%ivarnce_w_2, var_name="varnce_w_2", &
3158 : var_description="varnce_w_2, pdf parameter: w variance of component 2", &
3159 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3160 0 : k = k + 1
3161 :
3162 : case ('thl_1')
3163 0 : stats_metadata%ithl_1 = k
3164 :
3165 : call stat_assign( var_index=stats_metadata%ithl_1, var_name="thl_1", &
3166 : var_description="thl_1, pdf parameter: mean thl of component 1", var_units="K", &
3167 0 : l_silhs=.false., grid_kind=stats_zt )
3168 :
3169 0 : k = k + 1
3170 :
3171 : case ('thl_2')
3172 0 : stats_metadata%ithl_2 = k
3173 :
3174 : call stat_assign( var_index=stats_metadata%ithl_2, var_name="thl_2", &
3175 : var_description="thl_2, pdf parameter: mean thl of component 2", var_units="K", &
3176 0 : l_silhs=.false., grid_kind=stats_zt )
3177 0 : k = k + 1
3178 :
3179 : case ('varnce_thl_1')
3180 0 : stats_metadata%ivarnce_thl_1 = k
3181 :
3182 : call stat_assign( var_index=stats_metadata%ivarnce_thl_1, var_name="varnce_thl_1", &
3183 : var_description="varnce_thl_1, pdf parameter: thl variance of component 1", &
3184 : var_units="K^2", &
3185 0 : l_silhs=.false., grid_kind=stats_zt )
3186 :
3187 0 : k = k + 1
3188 :
3189 : case ('varnce_thl_2')
3190 0 : stats_metadata%ivarnce_thl_2 = k
3191 : call stat_assign( var_index=stats_metadata%ivarnce_thl_2, var_name="varnce_thl_2", &
3192 : var_description="varnce_thl_2, pdf parameter: thl variance of component 2", &
3193 : var_units="K^2", &
3194 0 : l_silhs=.false., grid_kind=stats_zt )
3195 :
3196 0 : k = k + 1
3197 :
3198 : case ('rt_1')
3199 0 : stats_metadata%irt_1 = k
3200 : call stat_assign( var_index=stats_metadata%irt_1, var_name="rt_1", &
3201 : var_description="rt_1, pdf parameter: mean rt of component 1", var_units="kg/kg", &
3202 0 : l_silhs=.false., grid_kind=stats_zt )
3203 :
3204 0 : k = k + 1
3205 :
3206 : case ('rt_2')
3207 0 : stats_metadata%irt_2 = k
3208 :
3209 : call stat_assign( var_index=stats_metadata%irt_2, var_name="rt_2", &
3210 : var_description="rt_2, pdf parameter: mean rt of component 2", var_units="kg/kg", &
3211 0 : l_silhs=.false., grid_kind=stats_zt )
3212 0 : k = k + 1
3213 :
3214 : case ('varnce_rt_1')
3215 0 : stats_metadata%ivarnce_rt_1 = k
3216 : call stat_assign( var_index=stats_metadata%ivarnce_rt_1, var_name="varnce_rt_1", &
3217 : var_description="varnce_rt_1, pdf parameter: rt variance of component 1", &
3218 0 : var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
3219 0 : k = k + 1
3220 :
3221 : case ('varnce_rt_2')
3222 0 : stats_metadata%ivarnce_rt_2 = k
3223 :
3224 : call stat_assign( var_index=stats_metadata%ivarnce_rt_2, var_name="varnce_rt_2", &
3225 : var_description="varnce_rt_2, pdf parameter: rt variance of component 2", &
3226 0 : var_units="(kg^2)/(kg^2)", l_silhs=.false., grid_kind=stats_zt )
3227 0 : k = k + 1
3228 :
3229 : case ('rc_1')
3230 0 : stats_metadata%irc_1 = k
3231 :
3232 : call stat_assign( var_index=stats_metadata%irc_1, var_name="rc_1", &
3233 : var_description="rc_1, pdf parameter: mean rc of component 1", var_units="kg/kg", &
3234 0 : l_silhs=.false., grid_kind=stats_zt )
3235 0 : k = k + 1
3236 :
3237 : case ('rc_2')
3238 0 : stats_metadata%irc_2 = k
3239 :
3240 : call stat_assign( var_index=stats_metadata%irc_2, var_name="rc_2", &
3241 : var_description="rc_2, pdf parameter: mean rc of component 2", var_units="kg/kg", &
3242 0 : l_silhs=.false., grid_kind=stats_zt )
3243 0 : k = k + 1
3244 :
3245 : case ('rsatl_1')
3246 0 : stats_metadata%irsatl_1 = k
3247 :
3248 : call stat_assign( var_index=stats_metadata%irsatl_1, var_name="rsatl_1", &
3249 : var_description="rsatl_1, pdf parameter: sat mix rat based on tl1", &
3250 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3251 0 : k = k + 1
3252 :
3253 : case ('rsatl_2')
3254 0 : stats_metadata%irsatl_2 = k
3255 :
3256 : call stat_assign( var_index=stats_metadata%irsatl_2, var_name="rsatl_2", &
3257 : var_description="rsatl_2, pdf parameter: sat mix rat based on tl2", &
3258 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3259 0 : k = k + 1
3260 :
3261 : case ('cloud_frac_1')
3262 0 : stats_metadata%icloud_frac_1 = k
3263 : call stat_assign( var_index=stats_metadata%icloud_frac_1, var_name="cloud_frac_1", &
3264 : var_description="cloud_frac_1, pdf parameter cloud_frac_1", var_units="-", &
3265 0 : l_silhs=.false., grid_kind=stats_zt )
3266 0 : k = k + 1
3267 :
3268 : case ('cloud_frac_2')
3269 0 : stats_metadata%icloud_frac_2 = k
3270 :
3271 : call stat_assign( var_index=stats_metadata%icloud_frac_2, var_name="cloud_frac_2", &
3272 : var_description="cloud_frac_2, pdf parameter cloud_frac_2", var_units="-", &
3273 0 : l_silhs=.false., grid_kind=stats_zt )
3274 0 : k = k + 1
3275 :
3276 : case ('chi_1')
3277 0 : stats_metadata%ichi_1 = k
3278 :
3279 : call stat_assign( var_index=stats_metadata%ichi_1, var_name="chi_1", &
3280 : var_description="chi_1, pdf parameter: Mellor's s (extended liq) for component 1", &
3281 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3282 0 : k = k + 1
3283 :
3284 : case ('chi_2')
3285 0 : stats_metadata%ichi_2 = k
3286 :
3287 : call stat_assign( var_index=stats_metadata%ichi_2, var_name="chi_2", &
3288 : var_description="chi_2, pdf parameter: Mellor's s (extended liq) for component 2", &
3289 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3290 0 : k = k + 1
3291 :
3292 : case ('stdev_chi_1')
3293 0 : stats_metadata%istdev_chi_1 = k
3294 :
3295 : call stat_assign( var_index=stats_metadata%istdev_chi_1, var_name="stdev_chi_1", &
3296 : var_description="stdev_chi_1, pdf parameter: Std dev of chi_1", var_units="kg/kg", &
3297 0 : l_silhs=.false., grid_kind=stats_zt )
3298 0 : k = k + 1
3299 :
3300 : case ('stdev_chi_2')
3301 0 : stats_metadata%istdev_chi_2 = k
3302 :
3303 : call stat_assign( var_index=stats_metadata%istdev_chi_2, var_name="stdev_chi_2", &
3304 : var_description="stdev_chi_2, pdf parameter: Std dev of chi_2", var_units="kg/kg", &
3305 0 : l_silhs=.false., grid_kind=stats_zt )
3306 0 : k = k + 1
3307 :
3308 : case ('chip2')
3309 0 : stats_metadata%ichip2 = k
3310 : call stat_assign( var_index=stats_metadata%ichip2, var_name="chip2", &
3311 : var_description="chip2, Variance of chi(s) (overall)", var_units="(kg/kg)^2", &
3312 0 : l_silhs=.false., grid_kind=stats_zt )
3313 0 : k = k + 1
3314 :
3315 : case ('stdev_eta_1')
3316 0 : stats_metadata%istdev_eta_1 = k
3317 :
3318 : call stat_assign( var_index=stats_metadata%istdev_eta_1, var_name="stdev_eta_1", &
3319 : var_description="stdev_eta_1, Standard dev. of eta(t) (1st PDF component)", &
3320 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3321 0 : k = k + 1
3322 :
3323 : case ('stdev_eta_2')
3324 0 : stats_metadata%istdev_eta_2 = k
3325 :
3326 : call stat_assign( var_index=stats_metadata%istdev_eta_2, var_name="stdev_eta_2", &
3327 : var_description="stdev_eta_2, Standard dev. of eta(t) (2nd PDF component)", &
3328 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3329 0 : k = k + 1
3330 :
3331 : case ('covar_chi_eta_1')
3332 0 : stats_metadata%icovar_chi_eta_1 = k
3333 :
3334 : call stat_assign( var_index=stats_metadata%icovar_chi_eta_1, var_name="covar_chi_eta_1", &
3335 : var_description="covar_chi_eta_1, Covariance of chi(s) and eta(t) " &
3336 : // "(1st PDF component)", &
3337 0 : var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
3338 0 : k = k + 1
3339 :
3340 : case ('covar_chi_eta_2')
3341 0 : stats_metadata%icovar_chi_eta_2 = k
3342 :
3343 : call stat_assign( var_index=stats_metadata%icovar_chi_eta_2, var_name="covar_chi_eta_2", &
3344 : var_description="covar_chi_eta_2, Covariance of chi(s) and eta(t) " &
3345 : // "(2nd PDF component)", &
3346 0 : var_units="kg^2/kg^2", l_silhs=.false., grid_kind=stats_zt )
3347 0 : k = k + 1
3348 :
3349 : case ('corr_w_chi_1')
3350 0 : stats_metadata%icorr_w_chi_1 = k
3351 :
3352 : call stat_assign( var_index=stats_metadata%icorr_w_chi_1, var_name="corr_w_chi_1", &
3353 : var_description="corr_w_chi_1, Correlation of w and chi (s)" &
3354 : // " (1st PDF component)", var_units="-", &
3355 0 : l_silhs=.false., grid_kind=stats_zt )
3356 0 : k = k + 1
3357 :
3358 : case ('corr_w_chi_2')
3359 0 : stats_metadata%icorr_w_chi_2 = k
3360 :
3361 : call stat_assign( var_index=stats_metadata%icorr_w_chi_2, var_name="corr_w_chi_2", &
3362 : var_description="corr_w_chi_2, Correlation of w and chi (s)" &
3363 : // " (2nd PDF component)", var_units="-", &
3364 0 : l_silhs=.false., grid_kind=stats_zt )
3365 0 : k = k + 1
3366 :
3367 : case ('corr_w_eta_1')
3368 0 : stats_metadata%icorr_w_eta_1 = k
3369 :
3370 : call stat_assign( var_index=stats_metadata%icorr_w_eta_1, var_name="corr_w_eta_1", &
3371 : var_description="corr_w_eta_1, Correlation of w and eta (t)" &
3372 : // " (1st PDF component)", var_units="-", &
3373 0 : l_silhs=.false., grid_kind=stats_zt )
3374 0 : k = k + 1
3375 :
3376 : case ('corr_w_eta_2')
3377 0 : stats_metadata%icorr_w_eta_2 = k
3378 :
3379 : call stat_assign( var_index=stats_metadata%icorr_w_eta_2, var_name="corr_w_eta_2", &
3380 : var_description="corr_w_eta_2, Correlation of w and eta (t)" &
3381 : // " (2nd PDF component)", var_units="-", &
3382 0 : l_silhs=.false., grid_kind=stats_zt )
3383 0 : k = k + 1
3384 :
3385 : case ('corr_chi_eta_1')
3386 0 : stats_metadata%icorr_chi_eta_1 = k
3387 :
3388 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_1, &
3389 : var_name="corr_chi_eta_1", &
3390 : var_description="corr_chi_eta_1, Correlation of chi (s) and" &
3391 : // " eta (t) (1st PDF component)", &
3392 : var_units="-", &
3393 0 : l_silhs=.false., grid_kind=stats_zt )
3394 0 : k = k + 1
3395 :
3396 : case ('corr_chi_eta_2')
3397 0 : stats_metadata%icorr_chi_eta_2 = k
3398 :
3399 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_2, &
3400 : var_name="corr_chi_eta_2", &
3401 : var_description="corr_chi_eta_2, Correlation of chi (s) and" &
3402 : // " eta (t) (2nd PDF component)", &
3403 : var_units="-", &
3404 0 : l_silhs=.false., grid_kind=stats_zt )
3405 0 : k = k + 1
3406 :
3407 : case ('corr_w_rt_1')
3408 0 : stats_metadata%icorr_w_rt_1 = k
3409 :
3410 : call stat_assign( var_index=stats_metadata%icorr_w_rt_1, var_name="corr_w_rt_1", &
3411 : var_description="corr_w_rt_1, Correlation of w and rt" &
3412 : // " (1st PDF component)", var_units="-", &
3413 0 : l_silhs=.false., grid_kind=stats_zt )
3414 0 : k = k + 1
3415 :
3416 : case ('corr_w_rt_2')
3417 0 : stats_metadata%icorr_w_rt_2 = k
3418 :
3419 : call stat_assign( var_index=stats_metadata%icorr_w_rt_2, var_name="corr_w_rt_2", &
3420 : var_description="corr_w_rt_2, Correlation of w and rt" &
3421 : // " (2nd PDF component)", var_units="-", &
3422 0 : l_silhs=.false., grid_kind=stats_zt )
3423 0 : k = k + 1
3424 :
3425 : case ('corr_w_thl_1')
3426 0 : stats_metadata%icorr_w_thl_1 = k
3427 :
3428 : call stat_assign( var_index=stats_metadata%icorr_w_thl_1, var_name="corr_w_thl_1", &
3429 : var_description="corr_w_thl_1, Correlation of w and thl" &
3430 : // " (1st PDF component)", var_units="-", &
3431 0 : l_silhs=.false., grid_kind=stats_zt )
3432 0 : k = k + 1
3433 :
3434 : case ('corr_w_thl_2')
3435 0 : stats_metadata%icorr_w_thl_2 = k
3436 :
3437 : call stat_assign( var_index=stats_metadata%icorr_w_thl_2, var_name="corr_w_thl_2", &
3438 : var_description="corr_w_thl_2, Correlation of w and thl" &
3439 : // " (2nd PDF component)", var_units="-", &
3440 0 : l_silhs=.false., grid_kind=stats_zt )
3441 0 : k = k + 1
3442 :
3443 : case ('corr_rt_thl_1')
3444 0 : stats_metadata%icorr_rt_thl_1 = k
3445 :
3446 : call stat_assign( var_index=stats_metadata%icorr_rt_thl_1, var_name="corr_rt_thl_1", &
3447 : var_description="corr_rt_thl_1, Correlation of rt and thl" &
3448 : // " (1st PDF component)", var_units="-", &
3449 0 : l_silhs=.false., grid_kind=stats_zt )
3450 0 : k = k + 1
3451 :
3452 : case ('corr_rt_thl_2')
3453 0 : stats_metadata%icorr_rt_thl_2 = k
3454 :
3455 : call stat_assign( var_index=stats_metadata%icorr_rt_thl_2, var_name="corr_rt_thl_2", &
3456 : var_description="corr_rt_thl_2, Correlation of rt and thl" &
3457 : // " (2nd PDF component)", var_units="-", &
3458 0 : l_silhs=.false., grid_kind=stats_zt )
3459 0 : k = k + 1
3460 :
3461 : case ('crt_1')
3462 0 : stats_metadata%icrt_1 = k
3463 :
3464 : call stat_assign( var_index=stats_metadata%icrt_1, var_name="crt_1", &
3465 : var_description="crt_1, Coefficient on rt in chi/eta" &
3466 : // " equations (1st PDF comp.)", &
3467 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3468 0 : k = k + 1
3469 :
3470 : case ('crt_2')
3471 0 : stats_metadata%icrt_2 = k
3472 :
3473 : call stat_assign( var_index=stats_metadata%icrt_2, var_name="crt_2", &
3474 : var_description="crt_2, Coefficient on rt in chi/eta" &
3475 : // " equations (2nd PDF comp.)", &
3476 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3477 0 : k = k + 1
3478 :
3479 : case ('cthl_1')
3480 0 : stats_metadata%icthl_1 = k
3481 :
3482 : call stat_assign( var_index=stats_metadata%icthl_1, var_name="cthl_1", &
3483 : var_description="cthl_1, Coefficient on theta-l in chi/eta" &
3484 : // " equations (1st PDF comp.)", &
3485 0 : var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
3486 0 : k = k + 1
3487 :
3488 : case ('cthl_2')
3489 0 : stats_metadata%icthl_2 = k
3490 :
3491 : call stat_assign( var_index=stats_metadata%icthl_2, var_name="cthl_2", &
3492 : var_description="cthl_2, Coefficient on theta-l in chi/eta" &
3493 : // " equations (2nd PDF comp.)", &
3494 0 : var_units="kg/kg/K", l_silhs=.false., grid_kind=stats_zt )
3495 0 : k = k + 1
3496 :
3497 : case('F_w')
3498 0 : stats_metadata%iF_w = k
3499 :
3500 : call stat_assign( var_index=stats_metadata%iF_w, var_name="F_w", &
3501 : var_description="F_w, Parameter for the spread of the" &
3502 : // " PDF component means of w (new PDF)", &
3503 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3504 0 : k = k + 1
3505 :
3506 : case('F_rt')
3507 0 : stats_metadata%iF_rt = k
3508 :
3509 : call stat_assign( var_index=stats_metadata%iF_rt, var_name="F_rt", &
3510 : var_description="F_rt, Parameter for the spread of the" &
3511 : // " PDF component means of rt (new PDF)", &
3512 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3513 0 : k = k + 1
3514 :
3515 : case('F_thl')
3516 0 : stats_metadata%iF_thl = k
3517 :
3518 : call stat_assign( var_index=stats_metadata%iF_thl, var_name="F_thl", &
3519 : var_description="F_thl, Parameter for the spread of the" &
3520 : // " PDF component means of thl (new PDF)", &
3521 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3522 0 : k = k + 1
3523 :
3524 : case('min_F_w')
3525 0 : stats_metadata%imin_F_w = k
3526 :
3527 : call stat_assign( var_index=stats_metadata%imin_F_w, var_name="min_F_w", &
3528 : var_description="min_F_w, Minimum allowable value of the" &
3529 : // " parameter F_w (new PDF)", &
3530 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3531 0 : k = k + 1
3532 :
3533 : case('max_F_w')
3534 0 : stats_metadata%imax_F_w = k
3535 :
3536 : call stat_assign( var_index=stats_metadata%imax_F_w, var_name="max_F_w", &
3537 : var_description="max_F_w, Maximum allowable value of the" &
3538 : // " parameter F_w (new PDF)", &
3539 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3540 0 : k = k + 1
3541 :
3542 : case('min_F_rt')
3543 0 : stats_metadata%imin_F_rt = k
3544 :
3545 : call stat_assign( var_index=stats_metadata%imin_F_rt, var_name="min_F_rt", &
3546 : var_description="min_F_rt, Minimum allowable value of the" &
3547 : // " parameter F_rt (new PDF)", &
3548 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3549 0 : k = k + 1
3550 :
3551 : case('max_F_rt')
3552 0 : stats_metadata%imax_F_rt = k
3553 :
3554 : call stat_assign( var_index=stats_metadata%imax_F_rt, var_name="max_F_rt", &
3555 : var_description="max_F_rt, Maximum allowable value of the" &
3556 : // " parameter F_rt (new PDF)", &
3557 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3558 0 : k = k + 1
3559 :
3560 : case('min_F_thl')
3561 0 : stats_metadata%imin_F_thl = k
3562 :
3563 : call stat_assign( var_index=stats_metadata%imin_F_thl, var_name="min_F_thl", &
3564 : var_description="min_F_thl, Minimum allowable value of the" &
3565 : // " parameter F_thl (new PDF)", &
3566 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3567 0 : k = k + 1
3568 :
3569 : case('max_F_thl')
3570 0 : stats_metadata%imax_F_thl = k
3571 :
3572 : call stat_assign( var_index=stats_metadata%imax_F_thl, var_name="max_F_thl", &
3573 : var_description="max_F_thl, Maximum allowable value of the" &
3574 : // " parameter F_thl (new PDF)", &
3575 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3576 0 : k = k + 1
3577 :
3578 : case ( 'coef_wprtp2_implicit' )
3579 0 : stats_metadata%icoef_wprtp2_implicit = k
3580 : call stat_assign( var_index=stats_metadata%icoef_wprtp2_implicit, &
3581 : var_name="coef_wprtp2_implicit", &
3582 : var_description="coef_wprtp2_implicit, wprtp2" &
3583 : // " = coef_wprtp2_implicit" &
3584 : // " * rtp2" &
3585 : // " + term_wprtp2_explicit", &
3586 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3587 0 : k = k + 1
3588 :
3589 : case ( 'term_wprtp2_explicit' )
3590 0 : stats_metadata%iterm_wprtp2_explicit = k
3591 : call stat_assign( var_index=stats_metadata%iterm_wprtp2_explicit, &
3592 : var_name="term_wprtp2_explicit", &
3593 : var_description="term_wprtp2_explicit, wprtp2" &
3594 : // " = coef_wprtp2_implicit" &
3595 : // " * rtp2" &
3596 : // " + term_wprtp2_explicit", &
3597 : var_units="m/s kg^2/kg^2", l_silhs=.false., &
3598 0 : grid_kind=stats_zt )
3599 0 : k = k + 1
3600 :
3601 : case ( 'coef_wpthlp2_implicit' )
3602 0 : stats_metadata%icoef_wpthlp2_implicit = k
3603 : call stat_assign( var_index=stats_metadata%icoef_wpthlp2_implicit, &
3604 : var_name="coef_wpthlp2_implicit", &
3605 : var_description="coef_wpthlp2_implicit, wpthlp2" &
3606 : // " = coef_wpthlp2_implicit" &
3607 : // " * thlp2" &
3608 : // " + term_wpthlp2_explicit", &
3609 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3610 0 : k = k + 1
3611 :
3612 : case ( 'term_wpthlp2_explicit' )
3613 0 : stats_metadata%iterm_wpthlp2_explicit = k
3614 : call stat_assign( var_index=stats_metadata%iterm_wpthlp2_explicit, &
3615 : var_name="term_wpthlp2_explicit", &
3616 : var_description="term_wpthlp2_explicit, wpthlp2" &
3617 : // " = coef_wpthlp2_implicit" &
3618 : // " * thlp2" &
3619 : // " + term_wpthlp2_explicit", &
3620 : var_units="m/s K^2", l_silhs=.false., &
3621 0 : grid_kind=stats_zt )
3622 0 : k = k + 1
3623 :
3624 : case ( 'coef_wprtpthlp_implicit' )
3625 0 : stats_metadata%icoef_wprtpthlp_implicit = k
3626 : call stat_assign( var_index=stats_metadata%icoef_wprtpthlp_implicit, &
3627 : var_name="coef_wprtpthlp_implicit", &
3628 : var_description="coef_wprtpthlp_implicit, wprtpthlp" &
3629 : // " = coef_wprtpthlp_implicit" &
3630 : // " * rtpthlp" &
3631 : // " + term_wprtpthlp_explicit", &
3632 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3633 0 : k = k + 1
3634 :
3635 : case ( 'term_wprtpthlp_explicit' )
3636 0 : stats_metadata%iterm_wprtpthlp_explicit = k
3637 : call stat_assign( var_index=stats_metadata%iterm_wprtpthlp_explicit, &
3638 : var_name="term_wprtpthlp_explicit", &
3639 : var_description="term_wprtpthlp_explicit, wprtpthlp" &
3640 : // " = coef_wprtpthlp_implicit" &
3641 : // " * rtpthlp" &
3642 : // " + term_wprtpthlp_explicit]", &
3643 : var_units="m/s (kg/kg) K", l_silhs=.false., &
3644 0 : grid_kind=stats_zt )
3645 0 : k = k + 1
3646 :
3647 : case ( 'coef_wp2rtp_implicit' )
3648 0 : stats_metadata%icoef_wp2rtp_implicit = k
3649 : call stat_assign( var_index=stats_metadata%icoef_wp2rtp_implicit, &
3650 : var_name="coef_wp2rtp_implicit", &
3651 : var_description="coef_wp2rtp_implicit, wp2rtp" &
3652 : // " = coef_wp2rtp_implicit" &
3653 : // " * wprtp" &
3654 : // " + term_wp2rtp_explicit", &
3655 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3656 0 : k = k + 1
3657 :
3658 : case ( 'term_wp2rtp_explicit' )
3659 0 : stats_metadata%iterm_wp2rtp_explicit = k
3660 : call stat_assign( var_index=stats_metadata%iterm_wp2rtp_explicit, &
3661 : var_name="term_wp2rtp_explicit", &
3662 : var_description="term_wp2rtp_explicit, wp2rtp" &
3663 : // " = coef_wp2rtp_implicit" &
3664 : // " * wprtp" &
3665 : // " + term_wp2rtp_explicit", &
3666 : var_units="m^2/s^2 kg/kg", l_silhs=.false., &
3667 0 : grid_kind=stats_zt )
3668 0 : k = k + 1
3669 :
3670 : case ( 'coef_wp2thlp_implicit' )
3671 0 : stats_metadata%icoef_wp2thlp_implicit = k
3672 : call stat_assign( var_index=stats_metadata%icoef_wp2thlp_implicit, &
3673 : var_name="coef_wp2thlp_implicit", &
3674 : var_description="coef_wp2thlp_implicit, wp2thlp" &
3675 : // " = coef_wp2thlp_implicit" &
3676 : // " * wpthlp" &
3677 : // " + term_wp2thlp_explicit", &
3678 0 : var_units="m/s", l_silhs=.false., grid_kind=stats_zt )
3679 0 : k = k + 1
3680 :
3681 : case ( 'term_wp2thlp_explicit' )
3682 0 : stats_metadata%iterm_wp2thlp_explicit = k
3683 : call stat_assign( var_index=stats_metadata%iterm_wp2thlp_explicit, &
3684 : var_name="term_wp2thlp_explicit", &
3685 : var_description="term_wp2thlp_explicit, wp2thlp" &
3686 : // " = coef_wp2thlp_implicit" &
3687 : // " * wpthlp" &
3688 : // " + term_wp2thlp_explicit", &
3689 : var_units="m^2/s^2 K", l_silhs=.false., &
3690 0 : grid_kind=stats_zt )
3691 0 : k = k + 1
3692 :
3693 : case('wp2_zt')
3694 0 : stats_metadata%iwp2_zt = k
3695 :
3696 : call stat_assign( var_index=stats_metadata%iwp2_zt, var_name="wp2_zt", &
3697 : var_description="wp2_zt, w'^2 interpolated to thermodynamic levels", &
3698 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3699 0 : k = k + 1
3700 :
3701 : case('thlp2_zt')
3702 0 : stats_metadata%ithlp2_zt = k
3703 :
3704 : call stat_assign( var_index=stats_metadata%ithlp2_zt, var_name="thlp2_zt", &
3705 : var_description="thlp2_zt, thl'^2 interpolated to thermodynamic levels", &
3706 0 : var_units="K^2", l_silhs=.false., grid_kind=stats_zt )
3707 0 : k = k + 1
3708 :
3709 : case('wpthlp_zt')
3710 0 : stats_metadata%iwpthlp_zt = k
3711 :
3712 : call stat_assign( var_index=stats_metadata%iwpthlp_zt, var_name="wpthlp_zt", &
3713 : var_description="wpthlp_zt, w'thl' interpolated to thermodynamic levels", &
3714 0 : var_units="(m K)/s", l_silhs=.false., grid_kind=stats_zt )
3715 0 : k = k + 1
3716 :
3717 : case('wprtp_zt')
3718 0 : stats_metadata%iwprtp_zt = k
3719 :
3720 : call stat_assign( var_index=stats_metadata%iwprtp_zt, var_name="wprtp_zt", &
3721 : var_description="wprtp_zt, w'rt' interpolated to thermodynamic levels", &
3722 0 : var_units="(m kg)/(s kg)", l_silhs=.false., grid_kind=stats_zt )
3723 0 : k = k + 1
3724 :
3725 : case('rtp2_zt')
3726 0 : stats_metadata%irtp2_zt = k
3727 :
3728 : call stat_assign( var_index=stats_metadata%irtp2_zt, var_name="rtp2_zt", &
3729 : var_description="rtp2_zt, rt'^2 interpolated to thermodynamic levels", &
3730 0 : var_units="(kg/kg)^2", l_silhs=.false., grid_kind=stats_zt )
3731 0 : k = k + 1
3732 :
3733 : case('rtpthlp_zt')
3734 0 : stats_metadata%irtpthlp_zt = k
3735 :
3736 : call stat_assign( var_index=stats_metadata%irtpthlp_zt, var_name="rtpthlp_zt", &
3737 : var_description="rtpthlp_zt, rt'thl' interpolated to thermodynamic levels", &
3738 0 : var_units="(kg K)/kg", l_silhs=.false., grid_kind=stats_zt )
3739 0 : k = k + 1
3740 :
3741 : case ('up2_zt')
3742 0 : stats_metadata%iup2_zt = k
3743 : call stat_assign( var_index=stats_metadata%iup2_zt, var_name="up2_zt", &
3744 : var_description="up2_zt, u'^2 interpolated to thermodynamic levels", &
3745 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3746 0 : k = k + 1
3747 :
3748 : case ('vp2_zt')
3749 0 : stats_metadata%ivp2_zt = k
3750 : call stat_assign( var_index=stats_metadata%ivp2_zt, var_name="vp2_zt", &
3751 : var_description="vp2_zt, v'^2 interpolated to thermodynamic levels", &
3752 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3753 0 : k = k + 1
3754 :
3755 : case ('upwp_zt')
3756 0 : stats_metadata%iupwp_zt = k
3757 : call stat_assign( var_index=stats_metadata%iupwp_zt, var_name="upwp_zt", &
3758 : var_description="upwp_zt, u'w' interpolated to thermodynamic levels", &
3759 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3760 0 : k = k + 1
3761 :
3762 : case ('vpwp_zt')
3763 0 : stats_metadata%ivpwp_zt = k
3764 : call stat_assign( var_index=stats_metadata%ivpwp_zt, var_name="vpwp_zt", &
3765 : var_description="vpwp_zt, v'w' interpolated to thermodynamic levels", &
3766 0 : var_units="m^2/s^2", l_silhs=.false., grid_kind=stats_zt )
3767 0 : k = k + 1
3768 :
3769 : case ('Skw_zt')
3770 0 : stats_metadata%iSkw_zt = k
3771 : call stat_assign( var_index=stats_metadata%iSkw_zt, var_name="Skw_zt", &
3772 : var_description="Skw_zt, Skewness of w on thermodynamic levels", &
3773 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3774 0 : k = k + 1
3775 :
3776 : case ('Skthl_zt')
3777 0 : stats_metadata%iSkthl_zt = k
3778 : call stat_assign( var_index=stats_metadata%iSkthl_zt, var_name="Skthl_zt", &
3779 : var_description="Skthl_zt, Skewness of thl on thermodynamic levels", &
3780 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3781 0 : k = k + 1
3782 :
3783 : case ('Skrt_zt')
3784 0 : stats_metadata%iSkrt_zt = k
3785 : call stat_assign( var_index=stats_metadata%iSkrt_zt, var_name="Skrt_zt", &
3786 : var_description="Skrt_zt, Skewness of rt on thermodynamic levels", &
3787 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3788 0 : k = k + 1
3789 :
3790 : case ('rcm_supersat_adj')
3791 0 : stats_metadata%ircm_supersat_adj = k
3792 : call stat_assign( var_index=stats_metadata%ircm_supersat_adj, var_name="rcm_supersat_adj", &
3793 : var_description="rcm_supersat_adj, rcm adjustment due to spurious supersaturation", &
3794 0 : var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt )
3795 0 : k = k + 1
3796 :
3797 : ! Hydrometeor overall variances for each hydrometeor type.
3798 : case('hmp2_zt')
3799 :
3800 0 : do hm_idx = 1, hydromet_dim, 1
3801 :
3802 0 : hm_type = hydromet_list(hm_idx)
3803 :
3804 : ! The overall variance of the hydrometeor.
3805 0 : stats_metadata%ihmp2_zt(hm_idx) = k
3806 :
3807 0 : if ( l_mix_rat_hm(hm_idx) ) then
3808 :
3809 0 : call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
3810 : var_name=trim( hm_type(1:2) )//"p2_zt", &
3811 : var_description="<" &
3812 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3813 : // "'^2> on thermodynamic levels (from " &
3814 : // "integration over PDF) [(kg/kg)^2]", &
3815 : var_units="(kg/kg)^2", &
3816 0 : l_silhs=.false., grid_kind=stats_zt )
3817 :
3818 : else ! Concentration
3819 :
3820 0 : call stat_assign( var_index=stats_metadata%ihmp2_zt(hm_idx), &
3821 : var_name=trim( hm_type(1:2) )//"p2_zt", &
3822 : var_description="<" &
3823 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3824 : // "'^2> on thermodynamic levels (from " &
3825 : // "integration over PDF) [(num/kg)^2]", &
3826 : var_units="(num/kg)^2", &
3827 0 : l_silhs=.false., grid_kind=stats_zt )
3828 :
3829 : endif ! l_mix_rat_hm(hm_idx)
3830 :
3831 0 : k = k + 1
3832 :
3833 : enddo ! hm_idx = 1, hydromet_dim, 1
3834 :
3835 : case ('C11_Skw_fnc')
3836 0 : stats_metadata%iC11_Skw_fnc = k
3837 :
3838 : call stat_assign( var_index=stats_metadata%iC11_Skw_fnc, var_name="C11_Skw_fnc", &
3839 : var_description="C11_Skw_fnc, C_11 parameter with Sk_w applied", var_units="count", &
3840 0 : l_silhs=.false., grid_kind=stats_zt )
3841 0 : k = k + 1
3842 :
3843 : case ('chi')
3844 0 : stats_metadata%ichi = k
3845 :
3846 : call stat_assign( var_index=stats_metadata%ichi, var_name="chi", &
3847 : var_description="chi, Mellor's s (extended liq)", var_units="kg/kg", &
3848 0 : l_silhs=.false., grid_kind=stats_zt )
3849 0 : k = k + 1
3850 :
3851 : case ( 'a3_coef_zt' )
3852 0 : stats_metadata%ia3_coef_zt = k
3853 : call stat_assign( var_index=stats_metadata%ia3_coef_zt, var_name="a3_coef_zt", &
3854 : var_description="a3_coef_zt, The a3 coefficient interpolated the the zt grid", &
3855 0 : var_units="count", l_silhs=.false., grid_kind=stats_zt )
3856 0 : k = k + 1
3857 :
3858 : case ( 'wp3_on_wp2_zt' )
3859 0 : stats_metadata%iwp3_on_wp2_zt = k
3860 : call stat_assign( var_index=stats_metadata%iwp3_on_wp2_zt, var_name="wp3_on_wp2_zt", &
3861 : var_description="wp3_on_wp2_zt, Smoothed version of wp3 / wp2", var_units="m/s", &
3862 0 : l_silhs=.false., grid_kind=stats_zt )
3863 0 : k = k + 1
3864 :
3865 : ! Hydrometeor component mean values for each PDF component and hydrometeor
3866 : ! type.
3867 : case ( "hm_i" )
3868 :
3869 0 : do hm_idx = 1, hydromet_dim, 1
3870 :
3871 0 : hm_type = hydromet_list(hm_idx)
3872 :
3873 : ! The mean of the hydrometeor in the 1st PDF component.
3874 0 : stats_metadata%ihm_1(hm_idx) = k
3875 :
3876 0 : if ( l_mix_rat_hm(hm_idx) ) then
3877 :
3878 0 : call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
3879 : var_name=trim( hm_type(1:2) )//"_1", &
3880 : var_description="Mean of " &
3881 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3882 : // " (1st PDF component) [kg/kg]", &
3883 : var_units="kg/kg", &
3884 0 : l_silhs=.false., grid_kind=stats_zt )
3885 :
3886 : else ! Concentration
3887 :
3888 0 : call stat_assign( var_index=stats_metadata%ihm_1(hm_idx), &
3889 : var_name=trim( hm_type(1:2) )//"_1", &
3890 : var_description="Mean of " &
3891 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3892 : // " (1st PDF component) [num/kg]", &
3893 : var_units="num/kg", &
3894 0 : l_silhs=.false., grid_kind=stats_zt )
3895 :
3896 : endif ! l_mix_rat_hm(hm_idx)
3897 :
3898 0 : k = k + 1
3899 :
3900 : ! The mean of the hydrometeor in the 2nd PDF component.
3901 0 : stats_metadata%ihm_2(hm_idx) = k
3902 :
3903 0 : if ( l_mix_rat_hm(hm_idx) ) then
3904 :
3905 0 : call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
3906 : var_name=trim( hm_type(1:2) )//"_2", &
3907 : var_description="Mean of " &
3908 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3909 : // " (2nd PDF component) [kg/kg]", &
3910 : var_units="kg/kg", &
3911 0 : l_silhs=.false., grid_kind=stats_zt )
3912 :
3913 : else ! Concentration
3914 :
3915 0 : call stat_assign( var_index=stats_metadata%ihm_2(hm_idx), &
3916 : var_name=trim( hm_type(1:2) )//"_2", &
3917 : var_description="Mean of " &
3918 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3919 : // " (2nd PDF component) [num/kg]", &
3920 : var_units="num/kg", &
3921 0 : l_silhs=.false., grid_kind=stats_zt )
3922 :
3923 : endif ! l_mix_rat_hm(hm_idx)
3924 :
3925 0 : k = k + 1
3926 :
3927 : enddo ! hm_idx = 1, hydromet_dim, 1
3928 :
3929 : case ( 'precip_frac' )
3930 0 : stats_metadata%iprecip_frac = k
3931 : call stat_assign( var_index=stats_metadata%iprecip_frac, var_name="precip_frac", &
3932 : var_description="precip_frac, Precipitation Fraction", var_units="-", &
3933 0 : l_silhs=.false., grid_kind=stats_zt )
3934 0 : k = k + 1
3935 :
3936 : case ( 'precip_frac_1' )
3937 0 : stats_metadata%iprecip_frac_1 = k
3938 : call stat_assign( var_index=stats_metadata%iprecip_frac_1, var_name="precip_frac_1", &
3939 : var_description="precip_frac_1, Precipitation Fraction (1st PDF component)", &
3940 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3941 0 : k = k + 1
3942 :
3943 : case ( 'precip_frac_2' )
3944 0 : stats_metadata%iprecip_frac_2 = k
3945 : call stat_assign( var_index=stats_metadata%iprecip_frac_2, var_name="precip_frac_2", &
3946 : var_description="precip_frac_2, Precipitation Fraction (2nd PDF component)", &
3947 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
3948 0 : k = k + 1
3949 :
3950 : case ( 'Ncnm' )
3951 0 : stats_metadata%iNcnm = k
3952 : call stat_assign( var_index=stats_metadata%iNcnm, var_name="Ncnm", &
3953 : var_description="Ncnm, Cloud nuclei concentration (PDF)", &
3954 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
3955 0 : k = k + 1
3956 :
3957 : ! Hydrometeor component mean values (in-precip) for each PDF component and
3958 : ! hydrometeor type.
3959 : case ( 'mu_hm_i' )
3960 :
3961 0 : do hm_idx = 1, hydromet_dim, 1
3962 :
3963 0 : hm_type = hydromet_list(hm_idx)
3964 :
3965 : ! The in-precip mean of the hydrometeor in the 1st PDF component.
3966 0 : stats_metadata%imu_hm_1(hm_idx) = k
3967 :
3968 0 : if ( l_mix_rat_hm(hm_idx) ) then
3969 :
3970 0 : call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
3971 : var_name="mu_"//trim( hm_type(1:2) )//"_1", &
3972 : var_description="Mean (in-precip) of " &
3973 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3974 : // " (1st PDF component) [kg/kg]", &
3975 : var_units="kg/kg", &
3976 0 : l_silhs=.false., grid_kind=stats_zt )
3977 :
3978 : else ! Concentration
3979 :
3980 0 : call stat_assign( var_index=stats_metadata%imu_hm_1(hm_idx), &
3981 : var_name="mu_"//trim( hm_type(1:2) )//"_1", &
3982 : var_description="Mean (in-precip) of " &
3983 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
3984 : // " (1st PDF component) [num/kg]", &
3985 : var_units="num/kg", &
3986 0 : l_silhs=.false., grid_kind=stats_zt )
3987 :
3988 : endif ! l_mix_rat_hm(hm_idx)
3989 :
3990 0 : k = k + 1
3991 :
3992 : ! The in-precip mean of the hydrometeor in the 2nd PDF component.
3993 0 : stats_metadata%imu_hm_2(hm_idx) = k
3994 :
3995 0 : if ( l_mix_rat_hm(hm_idx) ) then
3996 :
3997 0 : call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
3998 : var_name="mu_"//trim( hm_type(1:2) )//"_2", &
3999 : var_description="Mean (in-precip) of " &
4000 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4001 : // " (2nd PDF component) [kg/kg]", &
4002 : var_units="kg/kg", &
4003 0 : l_silhs=.false., grid_kind=stats_zt )
4004 :
4005 : else ! Concentration
4006 :
4007 0 : call stat_assign( var_index=stats_metadata%imu_hm_2(hm_idx), &
4008 : var_name="mu_"//trim( hm_type(1:2) )//"_2", &
4009 : var_description="Mean (in-precip) of " &
4010 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4011 : // " (2nd PDF component) [num/kg]", &
4012 : var_units="num/kg", &
4013 0 : l_silhs=.false., grid_kind=stats_zt )
4014 :
4015 : endif ! l_mix_rat_hm(hm_idx)
4016 :
4017 0 : k = k + 1
4018 :
4019 : enddo ! hm_idx = 1, hydromet_dim, 1
4020 :
4021 : case ( 'mu_Ncn_i' )
4022 0 : stats_metadata%imu_Ncn_1 = k
4023 :
4024 : call stat_assign( var_index=stats_metadata%imu_Ncn_1, &
4025 : var_name="mu_Ncn_1", &
4026 : var_description="mu_Ncn_1, Mean of N_cn (1st PDF component) " &
4027 : // "[num/kg]", var_units="num/kg", &
4028 0 : l_silhs=.false., grid_kind=stats_zt )
4029 :
4030 0 : k = k + 1
4031 :
4032 0 : stats_metadata%imu_Ncn_2 = k
4033 :
4034 : call stat_assign( var_index=stats_metadata%imu_Ncn_2, &
4035 : var_name="mu_Ncn_2", &
4036 : var_description="mu_Ncn_2, Mean of N_cn (2nd PDF component)", &
4037 : var_units="num/kg", &
4038 0 : l_silhs=.false., grid_kind=stats_zt )
4039 :
4040 0 : k = k + 1
4041 :
4042 : ! Hydrometeor component mean values (in-precip) for ln hm for each PDF
4043 : ! component and hydrometeor type.
4044 : case ( 'mu_hm_i_n' )
4045 :
4046 0 : do hm_idx = 1, hydromet_dim, 1
4047 :
4048 0 : hm_type = hydromet_list(hm_idx)
4049 :
4050 : ! The in-precip mean of ln hm in the 1st PDF component.
4051 0 : stats_metadata%imu_hm_1_n(hm_idx) = k
4052 :
4053 0 : if ( l_mix_rat_hm(hm_idx) ) then
4054 :
4055 0 : call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
4056 : var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
4057 : var_description="Mean (in-precip) of ln " &
4058 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4059 : // " (1st PDF component) [ln(kg/kg)]", &
4060 : var_units="ln(kg/kg)", &
4061 0 : l_silhs=.false., grid_kind=stats_zt )
4062 :
4063 : else ! Concentration
4064 :
4065 0 : call stat_assign( var_index=stats_metadata%imu_hm_1_n(hm_idx), &
4066 : var_name="mu_"//trim( hm_type(1:2) )//"_1_n", &
4067 : var_description="Mean (in-precip) of ln " &
4068 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4069 : // " (1st PDF component) [ln(num/kg)]", &
4070 : var_units="ln(num/kg)", &
4071 0 : l_silhs=.false., grid_kind=stats_zt )
4072 :
4073 : endif ! l_mix_rat_hm(hm_idx)
4074 :
4075 0 : k = k + 1
4076 :
4077 : ! The in-precip mean of ln hm in the 2nd PDF component.
4078 0 : stats_metadata%imu_hm_2_n(hm_idx) = k
4079 :
4080 0 : if ( l_mix_rat_hm(hm_idx) ) then
4081 :
4082 0 : call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
4083 : var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
4084 : var_description="Mean (in-precip) of ln " &
4085 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4086 : // " (2nd PDF component) [ln(kg/kg)]", &
4087 : var_units="ln(kg/kg)", &
4088 0 : l_silhs=.false., grid_kind=stats_zt )
4089 :
4090 : else ! Concentration
4091 :
4092 0 : call stat_assign( var_index=stats_metadata%imu_hm_2_n(hm_idx), &
4093 : var_name="mu_"//trim( hm_type(1:2) )//"_2_n", &
4094 : var_description="Mean (in-precip) of ln " &
4095 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4096 : // " (2nd PDF component) [ln(num/kg)]", &
4097 : var_units="ln(num/kg)", &
4098 0 : l_silhs=.false., grid_kind=stats_zt )
4099 :
4100 : endif ! l_mix_rat_hm(hm_idx)
4101 :
4102 0 : k = k + 1
4103 :
4104 : enddo ! hm_idx = 1, hydromet_dim, 1
4105 :
4106 : case ( 'mu_Ncn_i_n' )
4107 :
4108 0 : stats_metadata%imu_Ncn_1_n = k
4109 :
4110 : call stat_assign( var_index=stats_metadata%imu_Ncn_1_n, &
4111 : var_name="mu_Ncn_1_n", &
4112 : var_description="mu_Ncn_1_n, Mean of ln N_cn " &
4113 : // "(1st PDF component)", &
4114 : var_units="ln(num/kg)", &
4115 0 : l_silhs=.false., grid_kind=stats_zt )
4116 :
4117 0 : k = k + 1
4118 :
4119 0 : stats_metadata%imu_Ncn_2_n = k
4120 :
4121 : call stat_assign( var_index=stats_metadata%imu_Ncn_2_n, &
4122 : var_name="mu_Ncn_2_n", &
4123 : var_description="mu_Ncn_2_n, Mean of ln N_cn " &
4124 : // "(2nd PDF component)", &
4125 : var_units="ln(num/kg)", &
4126 0 : l_silhs=.false., grid_kind=stats_zt )
4127 :
4128 0 : k = k + 1
4129 :
4130 : ! Hydrometeor component standard deviations (in-precip) for each PDF
4131 : ! component and hydrometeor type.
4132 : case ( 'sigma_hm_i' )
4133 :
4134 0 : do hm_idx = 1, hydromet_dim, 1
4135 :
4136 0 : hm_type = hydromet_list(hm_idx)
4137 :
4138 : ! The in-precip standard deviation of the hydrometeor in the 1st PDF
4139 : ! component.
4140 0 : stats_metadata%isigma_hm_1(hm_idx) = k
4141 :
4142 0 : if ( l_mix_rat_hm(hm_idx) ) then
4143 :
4144 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
4145 : var_name="sigma_" &
4146 : // trim( hm_type(1:2) )//"_1", &
4147 : var_description="Standard deviation " &
4148 : // "(in-precip) of " &
4149 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4150 : // " (1st PDF component) [kg/kg]", &
4151 : var_units="kg/kg", &
4152 0 : l_silhs=.false., grid_kind=stats_zt )
4153 :
4154 : else ! Concentration
4155 :
4156 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1(hm_idx), &
4157 : var_name="sigma_" &
4158 : // trim( hm_type(1:2) )//"_1", &
4159 : var_description="Standard deviation " &
4160 : // "(in-precip) of " &
4161 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4162 : // " (1st PDF component) [num/kg]", &
4163 : var_units="num/kg", &
4164 0 : l_silhs=.false., grid_kind=stats_zt )
4165 :
4166 : endif ! l_mix_rat_hm(hm_idx)
4167 :
4168 0 : k = k + 1
4169 :
4170 : ! The in-precip standard deviation of the hydrometeor in the 2nd PDF
4171 : ! component.
4172 0 : stats_metadata%isigma_hm_2(hm_idx) = k
4173 :
4174 0 : if ( l_mix_rat_hm(hm_idx) ) then
4175 :
4176 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
4177 : var_name="sigma_" &
4178 : // trim( hm_type(1:2) )//"_2", &
4179 : var_description="Standard deviation " &
4180 : // "(in-precip) of " &
4181 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4182 : // " (2nd PDF component) [kg/kg]", &
4183 : var_units="kg/kg", &
4184 0 : l_silhs=.false., grid_kind=stats_zt )
4185 :
4186 : else ! Concentration
4187 :
4188 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2(hm_idx), &
4189 : var_name="sigma_" &
4190 : // trim( hm_type(1:2) )//"_2", &
4191 : var_description="Standard deviation " &
4192 : // "(in-precip) of " &
4193 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4194 : // " (2nd PDF component) [num/kg]", &
4195 : var_units="num/kg", &
4196 0 : l_silhs=.false., grid_kind=stats_zt )
4197 :
4198 : endif ! l_mix_rat_hm(hm_idx)
4199 :
4200 0 : k = k + 1
4201 :
4202 : enddo ! hm_idx = 1, hydromet_dim, 1
4203 :
4204 : case ( 'sigma_Ncn_i' )
4205 :
4206 0 : stats_metadata%isigma_Ncn_1 = k
4207 :
4208 : call stat_assign( var_index=stats_metadata%isigma_Ncn_1, &
4209 : var_name="sigma_Ncn_1", &
4210 : var_description="sigma_Ncn_1, Standard deviation of N_cn " &
4211 : // "(1st PDF component)", &
4212 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
4213 :
4214 0 : k = k + 1
4215 :
4216 0 : stats_metadata%isigma_Ncn_2 = k
4217 :
4218 : call stat_assign( var_index=stats_metadata%isigma_Ncn_2, &
4219 : var_name="sigma_Ncn_2", &
4220 : var_description="sigma_Ncn_2, Standard deviation of N_cn " &
4221 : // "(2nd PDF component)", &
4222 0 : var_units="num/kg", l_silhs=.false., grid_kind=stats_zt )
4223 :
4224 0 : k = k + 1
4225 :
4226 : ! Hydrometeor component standard deviations (in-precip) for ln hm for each
4227 : ! PDF component and hydrometeor type.
4228 : case ( 'sigma_hm_i_n' )
4229 :
4230 0 : do hm_idx = 1, hydromet_dim, 1
4231 :
4232 0 : hm_type = hydromet_list(hm_idx)
4233 :
4234 : ! The in-precip standard deviation of ln hm in the 1st PDF
4235 : ! component.
4236 0 : stats_metadata%isigma_hm_1_n(hm_idx) = k
4237 :
4238 0 : call stat_assign( var_index=stats_metadata%isigma_hm_1_n(hm_idx), &
4239 : var_name="sigma_" &
4240 : // trim( hm_type(1:2) )//"_1_n", &
4241 : var_description="Standard deviation " &
4242 : // "(in-precip) of ln " &
4243 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4244 : // " (1st PDF component) [-]", &
4245 : var_units="-", &
4246 0 : l_silhs=.false., grid_kind=stats_zt )
4247 :
4248 0 : k = k + 1
4249 :
4250 : ! The in-precip standard deviation of ln hm in the 2nd PDF
4251 : ! component.
4252 0 : stats_metadata%isigma_hm_2_n(hm_idx) = k
4253 :
4254 0 : call stat_assign( var_index=stats_metadata%isigma_hm_2_n(hm_idx), &
4255 : var_name="sigma_" &
4256 : // trim( hm_type(1:2) )//"_2_n", &
4257 : var_description="Standard deviation " &
4258 : // "(in-precip) of ln " &
4259 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4260 : // " (2nd PDF component) [-]", &
4261 : var_units="-", &
4262 0 : l_silhs=.false., grid_kind=stats_zt )
4263 :
4264 0 : k = k + 1
4265 :
4266 : enddo ! hm_idx = 1, hydromet_dim, 1
4267 :
4268 : case ( 'sigma_Ncn_i_n' )
4269 0 : stats_metadata%isigma_Ncn_1_n = k
4270 :
4271 : call stat_assign( var_index=stats_metadata%isigma_Ncn_1_n, &
4272 : var_name="sigma_Ncn_1_n", &
4273 : var_description="sigma_Ncn_1_n, Standard deviation of ln N_cn " &
4274 : // "(1st PDF component)", &
4275 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4276 :
4277 0 : k = k + 1
4278 :
4279 0 : stats_metadata%isigma_Ncn_2_n = k
4280 :
4281 : call stat_assign( var_index=stats_metadata%isigma_Ncn_2_n, &
4282 : var_name="sigma_Ncn_2_n", &
4283 : var_description="sigma_Ncn_2_n, Standard deviation of ln N_cn " &
4284 : // "(2nd PDF component)", &
4285 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4286 :
4287 0 : k = k + 1
4288 :
4289 : case ('corr_w_chi_1_ca')
4290 0 : stats_metadata%icorr_w_chi_1_ca = k
4291 :
4292 : call stat_assign( var_index=stats_metadata%icorr_w_chi_1_ca, &
4293 : var_name="corr_w_chi_1_ca", &
4294 : var_description="corr_w_chi_1_ca, Correlation of w and chi" &
4295 : // " (1st PDF component) found in the correlation" &
4296 : // " array", var_units="-", &
4297 0 : l_silhs=.false., grid_kind=stats_zt )
4298 0 : k = k + 1
4299 :
4300 : case ('corr_w_chi_2_ca')
4301 0 : stats_metadata%icorr_w_chi_2_ca = k
4302 :
4303 : call stat_assign( var_index=stats_metadata%icorr_w_chi_2_ca, &
4304 : var_name="corr_w_chi_2_ca", &
4305 : var_description="corr_w_chi_2_ca, Correlation of w and chi" &
4306 : // " (2nd PDF component) found in the correlation" &
4307 : // " array", var_units="-", &
4308 0 : l_silhs=.false., grid_kind=stats_zt )
4309 0 : k = k + 1
4310 :
4311 : case ('corr_w_eta_1_ca')
4312 0 : stats_metadata%icorr_w_eta_1_ca = k
4313 :
4314 : call stat_assign( var_index=stats_metadata%icorr_w_eta_1_ca, &
4315 : var_name="corr_w_eta_1_ca", &
4316 : var_description="corr_w_eta_1_ca, Correlation of w and eta" &
4317 : // " (1st PDF component) found in the correlation" &
4318 : // " array", var_units="-", &
4319 0 : l_silhs=.false., grid_kind=stats_zt )
4320 0 : k = k + 1
4321 :
4322 : case ('corr_w_eta_2_ca')
4323 0 : stats_metadata%icorr_w_eta_2_ca = k
4324 :
4325 : call stat_assign( var_index=stats_metadata%icorr_w_eta_2_ca, &
4326 : var_name="corr_w_eta_2_ca", &
4327 : var_description="corr_w_eta_2_ca, Correlation of w and eta" &
4328 : // " (2nd PDF component) found in the correlation" &
4329 : // " array", var_units="-", &
4330 0 : l_silhs=.false., grid_kind=stats_zt )
4331 0 : k = k + 1
4332 :
4333 : ! Correlation of w and a hydrometeor (in-precip) for each PDF
4334 : ! component and hydrometeor type.
4335 : case ( 'corr_w_hm_i' )
4336 :
4337 0 : do hm_idx = 1, hydromet_dim, 1
4338 :
4339 0 : hm_type = hydromet_list(hm_idx)
4340 :
4341 : ! The in-precip correlation of w and the hydrometeor in the
4342 : ! 1st PDF component.
4343 0 : stats_metadata%icorr_w_hm_1(hm_idx) = k
4344 :
4345 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_1(hm_idx), &
4346 : var_name="corr_w_"//trim( hm_type(1:2) )//"_1", &
4347 : var_description="Correlation (in-precip) " &
4348 : // "of w and " &
4349 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4350 : // " (1st PDF component) [-]", &
4351 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4352 :
4353 0 : k = k + 1
4354 :
4355 : ! The in-precip correlation of w and the hydrometeor in the
4356 : ! 2nd PDF component.
4357 0 : stats_metadata%icorr_w_hm_2(hm_idx) = k
4358 :
4359 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_2(hm_idx), &
4360 : var_name="corr_w_"//trim( hm_type(1:2) )//"_2", &
4361 : var_description="Correlation (in-precip) " &
4362 : // "of w and " &
4363 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4364 : // " (2nd PDF component) [-]", &
4365 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4366 :
4367 0 : k = k + 1
4368 :
4369 : enddo ! hm_idx = 1, hydromet_dim, 1
4370 :
4371 : case ( 'corr_w_Ncn_i' )
4372 0 : stats_metadata%icorr_w_Ncn_1 = k
4373 :
4374 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1, &
4375 : var_name="corr_w_Ncn_1", &
4376 : var_description="corr_w_Ncn_1, Correlation of w and N_cn " &
4377 : // "(1st PDF component)", &
4378 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4379 :
4380 0 : k = k + 1
4381 :
4382 0 : stats_metadata%icorr_w_Ncn_2 = k
4383 :
4384 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2, &
4385 : var_name="corr_w_Ncn_2", &
4386 : var_description="corr_w_Ncn_2, Correlation of w and N_cn " &
4387 : // "(2nd PDF component)", &
4388 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4389 :
4390 0 : k = k + 1
4391 :
4392 : case ('corr_chi_eta_1_ca')
4393 0 : stats_metadata%icorr_chi_eta_1_ca = k
4394 :
4395 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_1_ca, &
4396 : var_name="corr_chi_eta_1_ca", &
4397 : var_description="corr_chi_eta_1_ca, Correlation of chi (s) and" &
4398 : // " eta (t) (1st PDF component) found in the" &
4399 : // " correlation array", var_units="-", &
4400 0 : l_silhs=.false., grid_kind=stats_zt )
4401 0 : k = k + 1
4402 :
4403 : case ('corr_chi_eta_2_ca')
4404 0 : stats_metadata%icorr_chi_eta_2_ca = k
4405 :
4406 : call stat_assign( var_index=stats_metadata%icorr_chi_eta_2_ca, &
4407 : var_name="corr_chi_eta_2_ca", &
4408 : var_description="corr_chi_eta_2_ca, Correlation of chi (s) and" &
4409 : // " eta (t) (2nd PDF component) found in the" &
4410 : // " correlation array", var_units="-", &
4411 0 : l_silhs=.false., grid_kind=stats_zt )
4412 0 : k = k + 1
4413 :
4414 : ! Correlation of chi(s) and a hydrometeor (in-precip) for each PDF
4415 : ! component and hydrometeor type.
4416 : case ( 'corr_chi_hm_i' )
4417 :
4418 0 : do hm_idx = 1, hydromet_dim, 1
4419 :
4420 0 : hm_type = hydromet_list(hm_idx)
4421 :
4422 : ! The in-precip correlation of chi and the hydrometeor in the
4423 : ! 1st PDF component.
4424 0 : stats_metadata%icorr_chi_hm_1(hm_idx) = k
4425 :
4426 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_1(hm_idx), &
4427 : var_name="corr_chi_"//trim(hm_type(1:2))//"_1", &
4428 : var_description="Correlation (in-precip) " &
4429 : // "of chi (s) and " &
4430 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4431 : // " (1st PDF component) [-]", &
4432 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4433 :
4434 0 : k = k + 1
4435 :
4436 : ! The in-precip correlation of chi and the hydrometeor in the
4437 : ! 2nd PDF component.
4438 0 : stats_metadata%icorr_chi_hm_2(hm_idx) = k
4439 :
4440 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_2(hm_idx), &
4441 : var_name="corr_chi_"//trim(hm_type(1:2))//"_2", &
4442 : var_description="Correlation (in-precip) " &
4443 : // "of chi (s) and " &
4444 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4445 : // " (2nd PDF component) [-]", &
4446 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4447 :
4448 0 : k = k + 1
4449 :
4450 : enddo ! hm_idx = 1, hydromet_dim, 1
4451 :
4452 : case ( 'corr_chi_Ncn_i' )
4453 0 : stats_metadata%icorr_chi_Ncn_1 = k
4454 :
4455 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1, &
4456 : var_name="corr_chi_Ncn_1", &
4457 : var_description="corr_chi_Ncn_1, Correlation of chi and N_cn " &
4458 : // "(1st PDF component)", &
4459 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4460 :
4461 0 : k = k + 1
4462 :
4463 0 : stats_metadata%icorr_chi_Ncn_2 = k
4464 :
4465 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2, &
4466 : var_name="corr_chi_Ncn_2", &
4467 : var_description="corr_chi_Ncn_2, Correlation of chi and N_cn " &
4468 : // "(2nd PDF component)", &
4469 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4470 :
4471 0 : k = k + 1
4472 :
4473 : ! Correlation of eta(t) and a hydrometeor (in-precip) for each PDF
4474 : ! component and hydrometeor type.
4475 : case ( 'corr_eta_hm_i' )
4476 :
4477 0 : do hm_idx = 1, hydromet_dim, 1
4478 :
4479 0 : hm_type = hydromet_list(hm_idx)
4480 :
4481 : ! The in-precip correlation of eta and the hydrometeor in the
4482 : ! 1st PDF component.
4483 0 : stats_metadata%icorr_eta_hm_1(hm_idx) = k
4484 :
4485 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_1(hm_idx), &
4486 : var_name="corr_eta_"//trim(hm_type(1:2))//"_1", &
4487 : var_description="Correlation (in-precip) " &
4488 : // "of eta (t) and " &
4489 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4490 : // " (1st PDF component) [-]", &
4491 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4492 :
4493 0 : k = k + 1
4494 :
4495 : ! The in-precip correlation of eta and the hydrometeor in the
4496 : ! 2nd PDF component.
4497 0 : stats_metadata%icorr_eta_hm_2(hm_idx) = k
4498 :
4499 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_2(hm_idx), &
4500 : var_name="corr_eta_"//trim(hm_type(1:2))//"_2", &
4501 : var_description="Correlation (in-precip) " &
4502 : // "of eta (t) and " &
4503 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4504 : // " (2nd PDF component) [-]", &
4505 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4506 :
4507 0 : k = k + 1
4508 :
4509 : enddo ! hm_idx = 1, hydromet_dim, 1
4510 :
4511 : case ( 'corr_eta_Ncn_i' )
4512 :
4513 0 : stats_metadata%icorr_eta_Ncn_1 = k
4514 :
4515 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1, &
4516 : var_name="corr_eta_Ncn_1", &
4517 : var_description="corr_eta_Ncn_1, Correlation of eta and N_cn " &
4518 : // "(1st PDF component)", &
4519 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4520 :
4521 0 : k = k + 1
4522 :
4523 0 : stats_metadata%icorr_eta_Ncn_2 = k
4524 :
4525 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2, &
4526 : var_name="corr_eta_Ncn_2", &
4527 : var_description="corr_eta_Ncn_2, Correlation of eta and N_cn " &
4528 : // "(2nd PDF component)", &
4529 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4530 :
4531 0 : k = k + 1
4532 :
4533 : ! Correlation of Ncn and a hydrometeor (in-precip) for each PDF
4534 : ! component and hydrometeor type.
4535 : case ( 'corr_Ncn_hm_i' )
4536 :
4537 0 : do hm_idx = 1, hydromet_dim, 1
4538 :
4539 0 : hm_type = hydromet_list(hm_idx)
4540 :
4541 : ! The in-precip correlation of Ncn and the hydrometeor in the
4542 : ! 1st PDF component.
4543 0 : stats_metadata%icorr_Ncn_hm_1(hm_idx) = k
4544 :
4545 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1(hm_idx), &
4546 : var_name="corr_Ncn_"//trim(hm_type(1:2))//"_1", &
4547 : var_description="Correlation (in-precip) " &
4548 : // "of N_cn and " &
4549 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4550 : // " (1st PDF component) [-]", &
4551 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4552 :
4553 0 : k = k + 1
4554 :
4555 : ! The in-precip correlation of Ncn and the hydrometeor in the
4556 : ! 2nd PDF component.
4557 0 : stats_metadata%icorr_Ncn_hm_2(hm_idx) = k
4558 :
4559 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2(hm_idx), &
4560 : var_name="corr_Ncn_"//trim(hm_type(1:2))//"_2", &
4561 : var_description="Correlation (in-precip) " &
4562 : // "of N_cn and " &
4563 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4564 : // " (2nd PDF component) [-]", &
4565 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4566 :
4567 0 : k = k + 1
4568 :
4569 : enddo ! hm_idx = 1, hydromet_dim, 1
4570 :
4571 : ! Correlation (in-precip) of two different hydrometeors (hmx and hmy)
4572 : ! for each PDF component and hydrometeor type.
4573 : case ( 'corr_hmx_hmy_i' )
4574 :
4575 0 : do hmx_idx = 1, hydromet_dim, 1
4576 :
4577 0 : hmx_type = hydromet_list(hmx_idx)
4578 :
4579 0 : do hmy_idx = hmx_idx+1, hydromet_dim, 1
4580 :
4581 0 : hmy_type = hydromet_list(hmy_idx)
4582 :
4583 : ! The in-precip correlation of hmx and hmy in the 1st PDF
4584 : ! component.
4585 0 : stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx) = k
4586 :
4587 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1(hmy_idx,hmx_idx), &
4588 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4589 : // trim( hmy_type(1:2) )//"_1", &
4590 : var_description="Correlation (in-precip) " &
4591 : // "of " &
4592 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4593 : // " and " &
4594 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4595 : // " (1st PDF component) [-]", &
4596 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4597 :
4598 0 : k = k + 1
4599 :
4600 : ! The in-precip correlation of hmx and hmy in the 2nd PDF
4601 : ! component.
4602 0 : stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx) = k
4603 :
4604 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2(hmy_idx,hmx_idx), &
4605 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4606 : // trim( hmy_type(1:2) )//"_2", &
4607 : var_description="Correlation (in-precip) " &
4608 : // "of " &
4609 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4610 : // " and " &
4611 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4612 : // " (2nd PDF component) [-]", &
4613 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4614 :
4615 0 : k = k + 1
4616 :
4617 : enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
4618 :
4619 : enddo ! hmx_idx = 1, hydromet_dim, 1
4620 :
4621 : ! Correlation (in-precip) of w and ln hm for each PDF component and
4622 : ! hydrometeor type.
4623 : case ( 'corr_w_hm_i_n' )
4624 :
4625 0 : do hm_idx = 1, hydromet_dim, 1
4626 :
4627 0 : hm_type = hydromet_list(hm_idx)
4628 :
4629 : ! The in-precip correlation of w and ln hm in the 1st PDF
4630 : ! component.
4631 0 : stats_metadata%icorr_w_hm_1_n(hm_idx) = k
4632 :
4633 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_1_n(hm_idx), &
4634 : var_name="corr_w_"//trim(hm_type(1:2))//"_1_n", &
4635 : var_description="Correlation (in-precip) " &
4636 : // "of w and ln " &
4637 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4638 : // " (1st PDF component) [-]", &
4639 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4640 :
4641 0 : k = k + 1
4642 :
4643 : ! The in-precip correlation of w and ln hm in the 2nd PDF
4644 : ! component.
4645 0 : stats_metadata%icorr_w_hm_2_n(hm_idx) = k
4646 :
4647 0 : call stat_assign( var_index=stats_metadata%icorr_w_hm_2_n(hm_idx), &
4648 : var_name="corr_w_"//trim(hm_type(1:2))//"_2_n", &
4649 : var_description="Correlation (in-precip) " &
4650 : // "of w and ln " &
4651 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4652 : // " (2nd PDF component) [-]", &
4653 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4654 :
4655 0 : k = k + 1
4656 :
4657 : enddo ! hm_idx = 1, hydromet_dim, 1
4658 :
4659 : case ( 'corr_w_Ncn_i_n' )
4660 :
4661 0 : stats_metadata%icorr_w_Ncn_1_n = k
4662 :
4663 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_1_n, &
4664 : var_name="corr_w_Ncn_1_n", &
4665 : var_description="corr_w_Ncn_1_n, Correlation of w and " &
4666 : // "ln N_cn (1st PDF component)", &
4667 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4668 :
4669 0 : k = k + 1
4670 :
4671 0 : stats_metadata%icorr_w_Ncn_2_n = k
4672 :
4673 : call stat_assign( var_index=stats_metadata%icorr_w_Ncn_2_n, &
4674 : var_name="corr_w_Ncn_2_n", &
4675 : var_description="corr_w_Ncn_2_n, Correlation of w and " &
4676 : // "ln N_cn (2nd PDF component)", &
4677 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4678 :
4679 0 : k = k + 1
4680 :
4681 : ! Correlation (in-precip) of chi and ln hm for each PDF component and
4682 : ! hydrometeor type.
4683 : case ( 'corr_chi_hm_i_n' )
4684 :
4685 0 : do hm_idx = 1, hydromet_dim, 1
4686 :
4687 0 : hm_type = hydromet_list(hm_idx)
4688 :
4689 : ! The in-precip correlation of chi and ln hm in the 1st PDF
4690 : ! component.
4691 0 : stats_metadata%icorr_chi_hm_1_n(hm_idx) = k
4692 :
4693 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_1_n(hm_idx), &
4694 : var_name="corr_chi_"//trim(hm_type(1:2)) &
4695 : // "_1_n", &
4696 : var_description="Correlation (in-precip) " &
4697 : // "of chi (s) and ln " &
4698 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4699 : // " (1st PDF component) [-]", &
4700 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4701 :
4702 0 : k = k + 1
4703 :
4704 : ! The in-precip correlation of chi(s) and ln hm in the 2nd PDF
4705 : ! component.
4706 0 : stats_metadata%icorr_chi_hm_2_n(hm_idx) = k
4707 :
4708 0 : call stat_assign( var_index=stats_metadata%icorr_chi_hm_2_n(hm_idx), &
4709 : var_name="corr_chi_"//trim(hm_type(1:2)) &
4710 : // "_2_n", &
4711 : var_description="Correlation (in-precip) " &
4712 : // "of chi (s) and ln " &
4713 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4714 : // " (2nd PDF component) [-]", &
4715 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4716 :
4717 0 : k = k + 1
4718 :
4719 : enddo ! hm_idx = 1, hydromet_dim, 1
4720 :
4721 : case ( 'corr_chi_Ncn_i_n' )
4722 :
4723 0 : stats_metadata%icorr_chi_Ncn_1_n = k
4724 :
4725 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_1_n, &
4726 : var_name="corr_chi_Ncn_1_n", &
4727 : var_description="corr_chi_Ncn_1_n, Correlation of chi (s) and " &
4728 : // "ln N_cn (1st PDF component)", &
4729 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4730 :
4731 0 : k = k + 1
4732 :
4733 0 : stats_metadata%icorr_chi_Ncn_2_n = k
4734 :
4735 : call stat_assign( var_index=stats_metadata%icorr_chi_Ncn_2_n, &
4736 : var_name="corr_chi_Ncn_2_n", &
4737 : var_description="corr_chi_Ncn_2_n, Correlation of chi (s) and " &
4738 : // "ln N_cn (2nd PDF component)", &
4739 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4740 :
4741 0 : k = k + 1
4742 :
4743 : ! Correlation (in-precip) of eta and ln hm for each PDF component and
4744 : ! hydrometeor type.
4745 : case ( 'corr_eta_hm_i_n' )
4746 :
4747 0 : do hm_idx = 1, hydromet_dim, 1
4748 :
4749 0 : hm_type = hydromet_list(hm_idx)
4750 :
4751 : ! The in-precip correlation of eta and ln hm in the 1st PDF
4752 : ! component.
4753 0 : stats_metadata%icorr_eta_hm_1_n(hm_idx) = k
4754 :
4755 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_1_n(hm_idx), &
4756 : var_name="corr_eta_"//trim( hm_type(1:2) ) &
4757 : // "_1_n", &
4758 : var_description="Correlation (in-precip) " &
4759 : // "of eta (t) and ln " &
4760 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4761 : // " (1st PDF component) [-]", &
4762 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4763 :
4764 0 : k = k + 1
4765 :
4766 : ! The in-precip correlation of eta and ln hm in the 2nd PDF
4767 : ! component.
4768 0 : stats_metadata%icorr_eta_hm_2_n(hm_idx) = k
4769 :
4770 0 : call stat_assign( var_index=stats_metadata%icorr_eta_hm_2_n(hm_idx), &
4771 : var_name="corr_eta_"//trim( hm_type(1:2) ) &
4772 : // "_2_n", &
4773 : var_description="Correlation (in-precip) " &
4774 : // "of eta(t) and ln " &
4775 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4776 : // " (2nd PDF component) [-]", &
4777 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4778 :
4779 0 : k = k + 1
4780 :
4781 : enddo ! hm_idx = 1, hydromet_dim, 1
4782 :
4783 : case ( 'corr_eta_Ncn_i_n' )
4784 :
4785 0 : stats_metadata%icorr_eta_Ncn_1_n = k
4786 :
4787 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_1_n, &
4788 : var_name="corr_eta_Ncn_1_n", &
4789 : var_description="corr_eta_Ncn_1_n, Correlation of eta (t) and " &
4790 : // "ln N_cn (1st PDF component)", &
4791 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4792 :
4793 0 : k = k + 1
4794 :
4795 0 : stats_metadata%icorr_eta_Ncn_2_n = k
4796 :
4797 : call stat_assign( var_index=stats_metadata%icorr_eta_Ncn_2_n, &
4798 : var_name="corr_eta_Ncn_2_n", &
4799 : var_description="corr_eta_Ncn_2_n, Correlation of eta (t) and " &
4800 : // "ln N_cn (2nd PDF component)", &
4801 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4802 :
4803 0 : k = k + 1
4804 :
4805 : ! Correlation (in-precip) of ln Ncn and ln hm for each PDF component
4806 : ! and hydrometeor type.
4807 : case ( 'corr_Ncn_hm_i_n' )
4808 :
4809 0 : do hm_idx = 1, hydromet_dim, 1
4810 :
4811 0 : hm_type = hydromet_list(hm_idx)
4812 :
4813 : ! The in-precip correlation of ln Ncn and ln hm in the 1st PDF
4814 : ! component.
4815 0 : stats_metadata%icorr_Ncn_hm_1_n(hm_idx) = k
4816 :
4817 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_1_n(hm_idx), &
4818 : var_name="corr_Ncn_"//trim(hm_type(1:2)) &
4819 : // "_1_n", &
4820 : var_description="Correlation (in-precip) " &
4821 : // "of ln N_cn and ln " &
4822 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4823 : // " (1st PDF component) [-]", &
4824 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4825 :
4826 0 : k = k + 1
4827 :
4828 : ! The in-precip correlation of ln Ncn and ln hm in the 2nd PDF
4829 : ! component.
4830 0 : stats_metadata%icorr_Ncn_hm_2_n(hm_idx) = k
4831 :
4832 0 : call stat_assign( var_index=stats_metadata%icorr_Ncn_hm_2_n(hm_idx), &
4833 : var_name="corr_Ncn_"//trim(hm_type(1:2)) &
4834 : // "_2_n", &
4835 : var_description="Correlation (in-precip) " &
4836 : // "of ln N_cn and ln " &
4837 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4838 : // " (2nd PDF component) [-]", &
4839 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4840 :
4841 0 : k = k + 1
4842 :
4843 : enddo ! hm_idx = 1, hydromet_dim, 1
4844 :
4845 : ! Correlation (in-precip) of ln hmx and ln hmy (hmx and hmy are two
4846 : ! different hydrometeors) for each PDF component and hydrometeor type.
4847 : case ( 'corr_hmx_hmy_i_n' )
4848 :
4849 0 : do hmx_idx = 1, hydromet_dim, 1
4850 :
4851 0 : hmx_type = hydromet_list(hmx_idx)
4852 :
4853 0 : do hmy_idx = hmx_idx+1, hydromet_dim, 1
4854 :
4855 0 : hmy_type = hydromet_list(hmy_idx)
4856 :
4857 : ! The in-precip correlation of ln hmx and ln hmy in the 1st
4858 : ! PDF component.
4859 0 : stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx) = k
4860 :
4861 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_1_n(hmy_idx,hmx_idx), &
4862 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4863 : // trim( hmy_type(1:2) )//"_1_n", &
4864 : var_description="Correlation (in-precip) " &
4865 : // "of ln " &
4866 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4867 : // " and ln " &
4868 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4869 : // " (1st PDF component) [-]", &
4870 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4871 :
4872 0 : k = k + 1
4873 :
4874 : ! The in-precip correlation of ln hmx and ln hmy in the 2nd
4875 : ! PDF component.
4876 0 : stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx) = k
4877 :
4878 0 : call stat_assign( var_index=stats_metadata%icorr_hmx_hmy_2_n(hmy_idx,hmx_idx), &
4879 : var_name="corr_"//trim( hmx_type(1:2) )//"_" &
4880 : // trim( hmy_type(1:2) )//"_2_n", &
4881 : var_description="Correlation (in-precip) " &
4882 : // "of ln " &
4883 : // hmx_type(1:1)//"_"//trim( hmx_type(2:2) ) &
4884 : // " and ln " &
4885 : // hmy_type(1:1)//"_"//trim( hmy_type(2:2) ) &
4886 : // " (2nd PDF component) [-]", &
4887 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4888 :
4889 0 : k = k + 1
4890 :
4891 : enddo ! hmy_idx = hmx_idx+1, hydromet_dim, 1
4892 :
4893 : enddo ! hmx_idx = 1, hydromet_dim, 1
4894 :
4895 : ! Third-order mixed moment < w'^2 hm' >, where hm is a hydrometeor.
4896 : case ('wp2hmp')
4897 :
4898 0 : do hm_idx = 1, hydromet_dim, 1
4899 :
4900 0 : hm_type = hydromet_list(hm_idx)
4901 :
4902 0 : stats_metadata%iwp2hmp(hm_idx) = k
4903 :
4904 0 : if ( l_mix_rat_hm(hm_idx) ) then
4905 :
4906 0 : call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
4907 : var_name="wp2"//trim( hm_type(1:2) )//"p", &
4908 : var_description="Third-order moment < w'^2 " &
4909 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4910 : // "' > [(m/s)^2 kg/kg]", &
4911 : var_units="(m/s)^2 kg/kg", &
4912 0 : l_silhs=.false., grid_kind=stats_zt )
4913 :
4914 : else ! Concentration
4915 :
4916 0 : call stat_assign( var_index=stats_metadata%iwp2hmp(hm_idx), &
4917 : var_name="wp2"//trim( hm_type(1:2) )//"p", &
4918 : var_description="Third-order moment < w'^2 " &
4919 : // hm_type(1:1)//"_"//trim( hm_type(2:2) ) &
4920 : // "' > [(m/s)^2 num/kg]", &
4921 : var_units="(m/s)^2 num/kg", &
4922 0 : l_silhs=.false., grid_kind=stats_zt )
4923 :
4924 : endif ! l_mix_rat_hm(hm_idx)
4925 :
4926 0 : k = k + 1
4927 :
4928 : enddo ! hm_idx = 1, hydromet_dim, 1
4929 :
4930 : case ('cloud_frac_refined')
4931 0 : stats_metadata%icloud_frac_refined = k
4932 : call stat_assign( var_index=stats_metadata%icloud_frac_refined, var_name="cloud_frac_refined", &
4933 : var_description="cloud_frac_refined, Cloud fraction computed on " &
4934 : // "refined grid", &
4935 0 : var_units="-", l_silhs=.false., grid_kind=stats_zt )
4936 0 : k = k + 1
4937 :
4938 : case ('rcm_refined')
4939 0 : stats_metadata%ircm_refined = k
4940 : call stat_assign( var_index=stats_metadata%ircm_refined, var_name="rcm_refined", &
4941 : var_description="rcm_refined, Cloud water mixing ratio computed on " &
4942 : // "refined grid &
4943 0 : &[kg/kg]", var_units="kg/kg", l_silhs=.false., grid_kind=stats_zt)
4944 0 : k = k + 1
4945 :
4946 : case ('hl_on_Cp_residual')
4947 0 : stats_metadata%ihl_on_Cp_residual = k
4948 : call stat_assign( var_index=stats_metadata%ihl_on_Cp_residual, var_name="hl_on_Cp_residual", &
4949 : var_description="hl_on_Cp_residual, Residual change in HL/Cp from " &
4950 : // "Morrison microphysics &
4951 : ¬ due to sedimentation", &
4952 0 : var_units="K", l_silhs=.true., grid_kind=stats_zt)
4953 0 : k = k + 1
4954 :
4955 : case ('qto_residual')
4956 0 : stats_metadata%iqto_residual = k
4957 : call stat_assign( var_index=stats_metadata%iqto_residual, var_name="qto_residual", &
4958 : var_description="qto_residual, Residual change in total water " &
4959 : // "from Morrison &
4960 : µphysics not due to sedimentation", &
4961 0 : var_units="kg/kg", l_silhs=.true., grid_kind=stats_zt)
4962 0 : k = k + 1
4963 :
4964 : case ( 'sclrm' )
4965 0 : do j = 1, sclr_dim, 1
4966 0 : write(sclr_idx, * ) j
4967 0 : sclr_idx = adjustl(sclr_idx)
4968 0 : stats_metadata%isclrm(j) = k
4969 0 : call stat_assign( var_index=stats_metadata%isclrm(j), var_name="sclr"//trim(sclr_idx)//"m", &
4970 : var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
4971 0 : l_silhs=.false., grid_kind=stats_zt )
4972 0 : k = k + 1
4973 : end do
4974 :
4975 : case ( 'sclrm_f' )
4976 0 : do j = 1, sclr_dim, 1
4977 0 : write(sclr_idx, * ) j
4978 0 : sclr_idx = adjustl(sclr_idx)
4979 0 : stats_metadata%isclrm_f(j) = k
4980 0 : call stat_assign( var_index=stats_metadata%isclrm_f(j), var_name="sclr"//trim(sclr_idx)//"m_f", &
4981 : var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
4982 0 : l_silhs=.false., grid_kind=stats_zt )
4983 0 : k = k + 1
4984 : end do
4985 :
4986 : case ( 'edsclrm' )
4987 0 : do j = 1, edsclr_dim, 1
4988 0 : write(sclr_idx, * ) j
4989 0 : sclr_idx = adjustl(sclr_idx)
4990 0 : stats_metadata%iedsclrm(j) = k
4991 0 : call stat_assign( var_index=stats_metadata%iedsclrm(j), var_name="edsclr"//trim(sclr_idx)//"m", &
4992 : var_description="passive scalar "//trim(sclr_idx), var_units="unknown", &
4993 0 : l_silhs=.false., grid_kind=stats_zt )
4994 0 : k = k + 1
4995 : end do
4996 :
4997 : case ( 'edsclrm_f' )
4998 0 : do j = 1, edsclr_dim, 1
4999 0 : write(sclr_idx, * ) j
5000 0 : sclr_idx = adjustl(sclr_idx)
5001 0 : stats_metadata%iedsclrm_f(j) = k
5002 0 : call stat_assign( var_index=stats_metadata%iedsclrm_f(j), var_name="edsclr"//trim(sclr_idx)//"m_f", &
5003 : var_description="passive scalar forcing "//trim(sclr_idx), var_units="unknown", &
5004 0 : l_silhs=.false., grid_kind=stats_zt )
5005 0 : k = k + 1
5006 : end do
5007 :
5008 : case default
5009 :
5010 0 : write(fstderr,*) 'Error: unrecognized variable in vars_zt: ', trim( vars_zt(i) )
5011 0 : l_error = .true. ! This will stop the run.
5012 :
5013 : end select ! trim( vars_zt )
5014 :
5015 :
5016 : end do ! i=1,stats_zt%num_output_fields
5017 :
5018 :
5019 0 : return
5020 :
5021 : end subroutine stats_init_zt
5022 :
5023 : !===============================================================================
5024 :
5025 : end module stats_zt_module
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