MPAS-JEDI
predefined_configs.py
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1 #!/usr/bin/env python3
2 
3 import binning_utils as bu
4 from collections import defaultdict
5 from copy import deepcopy
6 from jediApplicationArgs import jediAppName, nOuterIter
7 import numpy as np
8 import os
9 import var_utils as vu
10 
11 #======================================================
12 # outer iteration settings for Variational applications
13 #======================================================
14 
15 outerIter = str(nOuterIter)
16 anIter = str(nOuterIter)
17 appName = jediAppName
18 
19 
20 #=============================================================
21 # groups of diagnostics for which statistics can be calculated
22 #=============================================================
23 
24 ## model-space diagnostics
25 modelDiags = ['mmgfsan']
26 
27 ## difference diagnostics
28 diffDiagnostics_ = defaultdict(list)
29 diffDiagnostics_['variational'] += ['omb']
30 diffDiagnostics_['hofx'] += ['omf']
31 
32 ## relative difference diagnostics
33 relativeDiagnostics_ = defaultdict(list)
34 relativeDiagnostics_['variational'] += ['rltv_omb']
35 relativeDiagnostics_['hofx'] += ['rltv_omf']
36 
37 ## absolute diagnostics
38 absoluteDiagnostics_ = defaultdict(list)
39 absoluteDiagnostics_['variational'] += ['obs', 'bak']
40 absoluteDiagnostics_['hofx'] += ['obs', 'h(x)']
41 
42 ## cloudy radiance diagnostics
43 cloudyRadDiagnostics = ['SCI']
44 
45 ## STD diagnostics
46 sigmaDiagnostics_ = defaultdict(list)
47 sigmaDiagnostics_['variational'] = ['omb','sigmaob', 'sigmab', 'CRyb']
48 sigmaDiagnostics_['hofx'] = ['omf','sigmaof', 'sigmaf', 'CRyf']
49 
50 # variational analysis diagnostics
51 if nOuterIter > 0:
52  diffDiagnostics_['variational'] += ['oma']
53  relativeDiagnostics_['variational'] += ['rltv_oma']
54  absoluteDiagnostics_['variational'] += ['ana']
55  sigmaDiagnostics_['variational'] += ['oma','sigmaoa','sigmaa', 'CRya']
56 
57 diffDiagnostics = diffDiagnostics_[jediAppName]
58 relativeDiagnostics = relativeDiagnostics_[jediAppName]
59 absoluteDiagnostics = absoluteDiagnostics_[jediAppName]
60 sigmaDiagnostics = sigmaDiagnostics_[jediAppName]
61 
62 defaultDiagnostics = diffDiagnostics
63 
64 ## diagnostics for which QC is irrelevant
65 nonQCedDiagnostics = ['obs']
66 
67 #==========================
68 # names and values of bins
69 #==========================
70 
71 ## heterogeneous/named bins
72 # note: the order of subplots will follow what is specified here
73 
74 # latbandsMethod bins, north to south
75 allNamedLatBands = {}
76 allNamedLatBands['values'] = ['NPol','NXTro','Tro','SXTro','SPol']
77 allNamedLatBands['minBounds'] = [60.0, 30.0, -30.0, -90.0, -90.0]
78 allNamedLatBands['maxBounds'] = [90.0, 90.0, 30.0, -30.0, -60.0]
79 
80 namedLatBands = {}
81 namedLatBands['values'] = ['NXTro','Tro','SXTro']
82 namedLatBands['minBounds'] = []
83 namedLatBands['maxBounds'] = []
84 
85 for latBand in namedLatBands['values']:
86  iband = allNamedLatBands['values'].index(latBand)
87  namedLatBands['minBounds'].append(allNamedLatBands['minBounds'][iband])
88  namedLatBands['maxBounds'].append(allNamedLatBands['maxBounds'][iband])
89 
90 
91 # cloudbandsMethod bins
92 clrskyThresh = 0.05
93 cldskyThresh = 1.0 - clrskyThresh
94 
95 namedCldFracBands = {}
96 namedCldFracBands['values'] = [bu.clrskyMethod, bu.mixskyMethod, bu.cldskyMethod, bu.allskyMethod]
97 namedCldFracBands['minBounds'] = [0.0, clrskyThresh, cldskyThresh, 0.0]
98 namedCldFracBands['maxBounds'] = [clrskyThresh, cldskyThresh, 1.0, 1.0]
99 
100 
101 # surfbandsMethod bins
102 seasurfThresh = 0.05
103 landsurfThresh = 1.0 - seasurfThresh
104 
105 namedLandFracBands = {}
106 namedLandFracBands['values'] = [bu.seasurfMethod, bu.mixsurfMethod, bu.landsurfMethod, bu.allsurfMethod]
107 namedLandFracBands['minBounds'] = [0.0, seasurfThresh, landsurfThresh, 0.0]
108 namedLandFracBands['maxBounds'] = [seasurfThresh, landsurfThresh, 1.0, 1.0]
109 
110 
111 # geoirlatlonboxMethod bins
112 geoirLonBands = deepcopy(bu.geoirlatlonBoxParams)
113 geoirLatBands = deepcopy(bu.geoirlatlonBoxParams)
114 
115 # store minBounds/maxBounds
116 for centerLon in geoirLonBands['centerLon']:
117  geoirLonBands['minBounds'] += \
118  [centerLon - bu.geoirMaxZenith]
119  geoirLonBands['maxBounds'] += \
120  [centerLon + bu.geoirMaxZenith]
121  geoirLatBands['minBounds'] += \
122  [-bu.geoirMaxZenith]
123  geoirLatBands['maxBounds'] += \
124  [bu.geoirMaxZenith]
125 
126 # ensure positive-definite longitude
127 for bound in ['minBounds', 'maxBounds']:
128  for ii, lon in enumerate(geoirLonBands[bound]):
129  while geoirLonBands[bound][ii] > 360.:
130  geoirLonBands[bound][ii] -= 360.
131  while geoirLonBands[bound][ii] < 0.:
132  geoirLonBands[bound][ii] += 360.
133 
134 ## homogeneous bins
135 # note: the ordering described here does not make any difference
136 # figure axes will be monotonically increasing except for pressure
137 
138 # binLims is used to auto-generate a large number of binning
139 # configurations in the binVarConfigs dictionary
140 
141 binLims = {}
142 
143 binLims[vu.obsVarPrs] = {}
144 binLims[vu.obsVarPrs]['start'] = 0.0
145 binLims[vu.obsVarPrs]['finish'] = 1000.0
146 binLims[vu.obsVarPrs]['step'] = 100.0
147 binLims[vu.obsVarPrs]['format'] = '{:.0f}'
148 
149 binLims[vu.obsVarAlt] = {}
150 binLims[vu.obsVarAlt]['start'] = 1000.0
151 binLims[vu.obsVarAlt]['finish'] = 50000.0
152 binLims[vu.obsVarAlt]['step'] = 2000.0
153 binLims[vu.obsVarAlt]['format'] = '{:.0f}'
154 
155 binLims[vu.obsVarLat] = {}
156 binLims[vu.obsVarLat]['start'] = -90.0
157 binLims[vu.obsVarLat]['finish'] = 90.0
158 binLims[vu.obsVarLat]['step'] = 10.0
159 binLims[vu.obsVarLat]['format'] = '{:.0f}'
160 
161 binLims[vu.obsVarLT] = {}
162 binLims[vu.obsVarLT]['start'] = bu.LH0
163 binLims[vu.obsVarLT]['finish'] = bu.LH1
164 binLims[vu.obsVarLT]['step'] = bu.LHDT
165 binLims[vu.obsVarLT]['format'] = '{:.0f}'
166 
167 binLims[vu.obsVarSenZen] = {}
168 binLims[vu.obsVarSenZen]['start'] = 0.0
169 binLims[vu.obsVarSenZen]['finish'] = 70.0
170 binLims[vu.obsVarSenZen]['step'] = 5.0
171 binLims[vu.obsVarSenZen]['format'] = '{:.0f}'
172 
173 binLims[vu.obsVarGlint] = {}
174 binLims[vu.obsVarGlint]['start'] = 0.0
175 binLims[vu.obsVarGlint]['finish'] = bu.maxGlint
176 binLims[vu.obsVarGlint]['step'] = 10.0
177 binLims[vu.obsVarGlint]['format'] = '{:.0f}'
178 
179 binLims[vu.obsVarLandFrac] = {}
180 binLims[vu.obsVarLandFrac]['start'] = 0.0
181 binLims[vu.obsVarLandFrac]['finish'] = 1.0
182 binLims[vu.obsVarLandFrac]['step'] = seasurfThresh / 2.0
183 binLims[vu.obsVarLandFrac]['format'] = '{:.3f}'
184 
185 binLims[vu.obsVarCldFrac] = {}
186 binLims[vu.obsVarCldFrac]['start'] = 0.0
187 binLims[vu.obsVarCldFrac]['finish'] = 1.0
188 binLims[vu.obsVarCldFrac]['step'] = clrskyThresh / 2.0
189 binLims[vu.obsVarCldFrac]['format'] = '{:.3f}'
190 
191 binLims[vu.obsVarSCI] = {}
192 binLims[vu.obsVarSCI]['start'] = 0.0
193 binLims[vu.obsVarSCI]['finish'] = 60.0
194 binLims[vu.obsVarSCI]['step'] = 1.0
195 binLims[vu.obsVarSCI]['format'] = '{:.0f}'
196 
197 binLims[vu.obsVarACI] = {}
198 binLims[vu.obsVarACI]['start'] = -20.0
199 binLims[vu.obsVarACI]['finish'] = 20.0
200 binLims[vu.obsVarACI]['step'] = 2.0
201 binLims[vu.obsVarACI]['format'] = '{:.0f}'
202 
203 binLims[vu.obsVarNormErr] = {}
204 binLims[vu.obsVarNormErr]['start'] = -7.0
205 binLims[vu.obsVarNormErr]['finish'] = 7.0
206 binLims[vu.obsVarNormErr]['step'] = 0.25
207 binLims[vu.obsVarNormErr]['format'] = '{:.2f}'
208 
209 binLims[vu.modVarLat] = {}
210 binLims[vu.modVarLat]['start'] = -90.0
211 binLims[vu.modVarLat]['finish'] = 90.0
212 binLims[vu.modVarLat]['step'] = 5.0
213 binLims[vu.modVarLat]['format'] = '{:.0f}'
214 
215 #binLims[vu.modVarAlt] = {}
216 #binLims[vu.modVarAlt]['start'] = 0.0
217 #binLims[vu.modVarAlt]['finish'] = 50000.0
218 #binLims[vu.modVarAlt]['step'] = 5000.0
219 #binLims[vu.modVarAlt]['format'] = '{:.0f}'
220 
221 for binType, param in binLims.items():
222  binBounds = list(np.arange(
223  param['start']-0.5*np.abs(param['step']),
224  param['finish']+1.5*param['step'],
225  param['step']))
226  binLims[binType]['minBounds'] = []
227  binLims[binType]['maxBounds'] = []
228  binLims[binType]['values'] = []
229  for ibin in list(range(len(binBounds)-1)):
230  binLims[binType]['minBounds'].append(binBounds[ibin])
231  binLims[binType]['maxBounds'].append(binBounds[ibin+1])
232 
233  binVal = 0.5 * (binBounds[ibin+1] + binBounds[ibin])
234  binLims[binType]['values'].append(param['format'].format(binVal))
235 
236 
237 #@EffectiveQC* values:
238 # pass = 0; // we like that one!
239 # passive = 1; // H(x) is computed (for monitoring, BC...) but obs not assimilated
240 # missing = 10; // missing values prevent use of observation
241 # preQC = 11; // observation rejected by pre-processing
242 # bounds = 12; // observation value out of bounds
243 # domain = 13; // observation not within domain of use
244 # exclude = 14; // observation excluded
245 # Hfailed = 15; // H(x) computation failed
246 # thinned = 16; // observation removed due to thinning
247 # diffref = 17; // metadata too far from reference
248 # clw = 18; // observation removed due to cloud field
249 # fguess = 19; // observation too far from guess
250 # seaice = 20; // observation based sea ice detection, also flags land points
251 # track = 21; // observation removed as inconsistent with the rest of track
252 # buddy = 22; // observation rejected by the buddy check
253 # derivative = 23; // observation removed due to metadata derivative value
254 # profile = 24; // observation rejected by at least one profile QC check
255 # onedvar = 25; // observation failed to converge in 1dvar check
256 # bayesianQC = 26; // observation failed due to Bayesian background check
257 # modelobthresh = 27; // observation failed modelob threshold check
258 # Static list above copied on 22 Apr 2021
259 # see ufo/src/ufo/filters/QCflags.h for up-to-date list
260 
261 goodFlags = [0, 1]
262 goodFlagNames = ['pass', 'passive']
263 
264 badFlags = [10, 11, 12, 13,
265  14, 15, 16, 17,
266  18, 19, 20, 21,
267  22, 23, 24, 25, 26, 27,
268 ]
269 badFlagNames = ['missing', 'preQC', 'bounds', 'domain',
270  'exclude', 'Hfailed', 'thinned', 'diffref',
271  'clw', 'fguess', 'seaice', 'track',
272  'buddy', 'derivative', 'profile', 'onedvar', 'bayesianQC', 'modelobthresh',
273 ]
274 
275 #=================================================
276 # binning configurations used for all bin methods
277 #=================================================
278 
279 # Each binVarConfig member has the following properties
280 # key: binVar string describes the variable to bin over
281 # binMethod: used to distinguish between multiple methods with the same binVar
282 # (e.g., bu.identityBinMethod, bu.badQC, bu.latbandsMethod, etc.)
283 # filters: list of filters that will exclude locations for each method
284 # for each filter:
285 # where: logical function that determines locations that are excluded
286 # variable: string or class that is used to initialize the IdObsFunction or ObsFunction class;
287 # this may refer to a predefined key of vu.ObsGroups and vu.ObsVars
288 # bounds: numerical value(s) used in the where test (scalar or Iterable same length as values). At least one filter must have len(bounds)==len(values).
289 # except_diags (optional): list of diagnostics for which a particular filter does not apply
290 # values (string): list of values associated with each bin
291 #
292 #TODO: classify each binmethod as 1D, 2D, etc. to indicate which types of figures apply to it
293 
294 nullBinMethod = { 'filters': [], 'values': [] }
295 nullBinVarConfig = { bu.identityBinMethod:nullBinMethod }
296 
297 # filter locations that do not have goodFlags
298 AnyBadQC = {
299  'where': bu.notEqualAnyBound,
300  'variable': vu.selfQCValue,
301  'bounds': [goodFlags],
302  'except_diags': nonQCedDiagnostics,
303 }
304 
305 binVarConfigs = {
306  vu.obsVarQC: {
307  bu.goodQCMethod: {
308  'filters': [
309  {'where': bu.notEqualBound,
310  'variable': vu.selfQCValue,
311  'bounds': goodFlags,
312  'except_diags': nonQCedDiagnostics}
313  ],
314  'values': goodFlagNames,
315  },
316  bu.badQCMethod: {
317  'filters': [
318  {'where': bu.notEqualBound,
319  'variable': vu.selfQCValue,
320  'bounds': badFlags,
321  'except_diags': nonQCedDiagnostics},
322  {'where': bu.equalBound,
323  'variable': vu.selfQCValue,
324  'bounds': badFlags,
325  'except_diags': nonQCedDiagnostics,
326  'mask_value': 0.0},
327  ],
328  'values': badFlagNames,
329  },
330  bu.allQCMethod: {
331  'filters': [
332  {'where': bu.notEqualBound,
333  'variable': vu.selfQCValue,
334  'bounds': goodFlags+badFlags,
335  'except_diags': nonQCedDiagnostics},
336  {'where': bu.equalBound,
337  'variable': vu.selfQCValue,
338  'bounds': goodFlags+badFlags,
339  'except_diags': nonQCedDiagnostics,
340  'mask_value': 0.0},
341  ],
342  'values': goodFlagNames+badFlagNames,
343  },
344  },
345  vu.obsVarPrs: {
346  bu.PjetMethod: {
347  'filters': [
348 # eliminate locations outside bu.P_jet_min to bu.P_jet_max
349  {'where': bu.lessBound,
350  'variable': vu.prsMeta,
351  'bounds': bu.P_jet_min},
352  {'where': bu.greatEqualBound,
353  'variable': vu.prsMeta,
354  'bounds': bu.P_jet_max},
355  AnyBadQC,
356  ],
357  'values': bu.P_jet_val,
358  },
359  },
360  vu.obsVarAlt: {
361  bu.altjetMethod: {
362  'filters': [
363 # eliminate locations outside bu.alt_jet_min to bu.alt_jet_max
364  {'where': bu.lessBound,
365  'variable': vu.altMeta,
366  'bounds': bu.alt_jet_min},
367  {'where': bu.greatEqualBound,
368  'variable': vu.altMeta,
369  'bounds': bu.alt_jet_max},
370  AnyBadQC,
371  ],
372  'values': bu.alt_jet_val,
373  },
374  },
375  vu.obsVarLat: {
376  bu.latbandsMethod: {
377  'filters': [
378  {'where': bu.lessBound,
379  'variable': vu.latMeta,
380  'bounds': namedLatBands['minBounds']},
381  {'where': bu.greatEqualBound,
382  'variable': vu.latMeta,
383  'bounds': namedLatBands['maxBounds']},
384  AnyBadQC,
385  ],
386  'values': namedLatBands['values'],
387  },
388  bu.PjetMethod: {
389  'filters': [
390  {'where': bu.lessBound,
391  'variable': vu.latMeta,
392  'bounds': binLims[vu.obsVarLat]['minBounds']},
393  {'where': bu.greatEqualBound,
394  'variable': vu.latMeta,
395  'bounds': binLims[vu.obsVarLat]['maxBounds']},
396  {'where': bu.lessBound,
397  'variable': vu.prsMeta,
398  'bounds': bu.P_jet_min},
399  {'where': bu.greatEqualBound,
400  'variable': vu.prsMeta,
401  'bounds': bu.P_jet_max},
402  AnyBadQC,
403  ],
404  'values': binLims[vu.obsVarLat]['values'],
405  },
406  bu.altjetMethod: {
407  'filters': [
408  {'where': bu.lessBound,
409  'variable': vu.latMeta,
410  'bounds': binLims[vu.obsVarLat]['minBounds']},
411  {'where': bu.greatEqualBound,
412  'variable': vu.latMeta,
413  'bounds': binLims[vu.obsVarLat]['maxBounds']},
414  {'where': bu.lessBound,
415  'variable': vu.altMeta,
416  'bounds': bu.alt_jet_min},
417  {'where': bu.greatEqualBound,
418  'variable': vu.altMeta,
419  'bounds': bu.alt_jet_max},
420  AnyBadQC,
421  ],
422  'values': binLims[vu.obsVarLat]['values'],
423  },
424  },
425  vu.obsVarLandFrac: {
426  bu.surfbandsMethod: {
427  'filters': [
428  {'where': bu.lessBound,
429  'variable': vu.landfracGeo,
430  'bounds': namedLandFracBands['minBounds']},
431  {'where': bu.greatBound,
432  'variable': vu.landfracGeo,
433  'bounds': namedLandFracBands['maxBounds']},
434  AnyBadQC,
435  ],
436  'values': namedLandFracBands['values'],
437  },
438  },
439  vu.obsVarCldFrac: {
440  bu.cloudbandsMethod: {
441  'filters': [
442  {'where': bu.lessBound,
443  'variable': vu.cldfracMeta,
444  'bounds': namedCldFracBands['minBounds']},
445  {'where': bu.greatBound,
446  'variable': vu.cldfracMeta,
447  'bounds': namedCldFracBands['maxBounds']},
448  AnyBadQC,
449  ],
450  'values': namedCldFracBands['values'],
451  },
452  },
453  vu.obsVarSCI: {
454  bu.OkamotoMethod: {
455  'filters': [
456  {'where': bu.lessBound,
457  'variable': bu.SCIOkamoto,
458  'bounds': binLims[vu.obsVarSCI]['minBounds']},
459  {'where': bu.greatEqualBound,
460  'variable': bu.SCIOkamoto,
461  'bounds': binLims[vu.obsVarSCI]['maxBounds']},
462  ],
463  'values': binLims[vu.obsVarSCI]['values'],
464  },
465  bu.ScaleOkamotoMethod: {
466  'filters': [
467  {'where': bu.lessBound,
468  'variable': bu.ScaledSCIOkamoto,
469  'bounds': binLims[vu.obsVarSCI]['minBounds']},
470  {'where': bu.greatEqualBound,
471  'variable': bu.ScaledSCIOkamoto,
472  'bounds': binLims[vu.obsVarSCI]['maxBounds']},
473  ],
474  'values': binLims[vu.obsVarSCI]['values'],
475  },
476  bu.ModHarnischMethod: {
477  'filters': [
478  {'where': bu.lessBound,
479  'variable': bu.SCIModHarnisch,
480  'bounds': binLims[vu.obsVarSCI]['minBounds']},
481  {'where': bu.greatEqualBound,
482  'variable': bu.SCIModHarnisch,
483  'bounds': binLims[vu.obsVarSCI]['maxBounds']},
484  ],
485  'values': binLims[vu.obsVarSCI]['values'],
486  },
487  bu.ScaleModHarnischMethod: {
488  'filters': [
489  {'where': bu.lessBound,
490  'variable': bu.ScaledSCIModHarnisch,
491  'bounds': binLims[vu.obsVarSCI]['minBounds']},
492  {'where': bu.greatEqualBound,
493  'variable': bu.ScaledSCIModHarnisch,
494  'bounds': binLims[vu.obsVarSCI]['maxBounds']},
495  ],
496  'values': binLims[vu.obsVarSCI]['values'],
497  },
498  },
499  vu.obsVarNormErr: {
500  bu.OkamotoMethod: {
501  'filters': [
502  {'where': bu.lessBound,
503  'variable': bu.OkamotoNormalizedError,
504  'bounds': binLims[vu.obsVarNormErr]['minBounds']},
505  {'where': bu.greatEqualBound,
506  'variable': bu.OkamotoNormalizedError,
507  'bounds': binLims[vu.obsVarNormErr]['maxBounds']},
508  ],
509  'values': binLims[vu.obsVarNormErr]['values'],
510  },
511  bu.ScaleOkamotoMethod: {
512  'filters': [
513  {'where': bu.lessBound,
514  'variable': bu.ScaledOkamotoNormalizedError,
515  'bounds': binLims[vu.obsVarNormErr]['minBounds']},
516  {'where': bu.greatEqualBound,
517  'variable': bu.ScaledOkamotoNormalizedError,
518  'bounds': binLims[vu.obsVarNormErr]['maxBounds']},
519  ],
520  'values': binLims[vu.obsVarNormErr]['values'],
521  },
522  bu.ModHarnischMethod: {
523  'filters': [
524  {'where': bu.lessBound,
525  'variable': bu.ModHarnischNormalizedError,
526  'bounds': binLims[vu.obsVarNormErr]['minBounds']},
527  {'where': bu.greatEqualBound,
528  'variable': bu.ModHarnischNormalizedError,
529  'bounds': binLims[vu.obsVarNormErr]['maxBounds']},
530  ],
531  'values': binLims[vu.obsVarNormErr]['values'],
532  },
533  bu.ScaleModHarnischMethod: {
534  'filters': [
535  {'where': bu.lessBound,
536  'variable': bu.ScaledModHarnischNormalizedError,
537  'bounds': binLims[vu.obsVarNormErr]['minBounds']},
538  {'where': bu.greatEqualBound,
539  'variable': bu.ScaledModHarnischNormalizedError,
540  'bounds': binLims[vu.obsVarNormErr]['maxBounds']},
541  ],
542  'values': binLims[vu.obsVarNormErr]['values'],
543  },
544  },
545  vu.obsRegionBinVar: {
546  'AFRICA': nullBinMethod,
547  'ATLANTIC': nullBinMethod,
548  'AUSTRALIA': nullBinMethod,
549  'CONUS': {
550  'filters': [
551  {'where': bu.lessBound,
552  'variable': vu.lonMeta,
553  'bounds': 234.0},
554  {'where': bu.greatBound,
555  'variable': vu.lonMeta,
556  'bounds': 294.0},
557  {'where': bu.lessBound,
558  'variable': vu.latMeta,
559  'bounds': 25.0},
560  {'where': bu.greatBound,
561  'variable': vu.latMeta,
562  'bounds': 50.0},
563  AnyBadQC,
564  ],
565  'values': ['CONUS'],
566  },
567  'EUROPE': nullBinMethod,
568  'E_EUROPE': nullBinMethod,
569  'NAMERICA': nullBinMethod,
570  'PACIFIC': nullBinMethod,
571  'SAMERICA': nullBinMethod,
572  'SE_ASIA': nullBinMethod,
573  'S_ASIA': nullBinMethod,
574  bu.geoirlatlonboxMethod: {
575  'filters': [
576  {'where': bu.lessBound,
577  'variable': vu.lonMeta,
578  'bounds': geoirLonBands['minBounds']},
579  {'where': bu.greatBound,
580  'variable': vu.lonMeta,
581  'bounds': geoirLonBands['maxBounds']},
582  {'where': bu.lessBound,
583  'variable': vu.latMeta,
584  'bounds': geoirLatBands['minBounds']},
585  {'where': bu.greatBound,
586  'variable': vu.latMeta,
587  'bounds': geoirLatBands['maxBounds']},
588  ],
589  'values': geoirLonBands['values'],
590  },
591 #TODO: use shapefiles/polygons to describe geographic regions instead of lat/lon boxes, e.g.,
592 # 'CONUS_POLYGON': {
593 # 'filters': [
594 # {'where': bu.notInBound,
595 # 'variable': bu.Regions,
596 # 'variable': [vu.lonMeta, vu.latMeta],
597 # 'bounds': ['CONUS']},
598 # ],
599 # 'values': ['CONUS'],
600 # },
601 # 'EUROPE_POLYGON': {
602 # 'filters': [
603 # {'where': bu.notInBound,
604 # 'variable': bu.Regions,
605 # 'variable': [vu.lonMeta, vu.latMeta],
606 # 'bounds': ['EUROPE']},
607 # ],
608 # 'values': ['EUROPE'],
609 # },
610  },
611 #TODO: enable binning in MPAS Model space
612 # vu.modVarPrs: {
613 # bu.identityBinMethod: {
614 # 'filters': [
615 # {'where': bu.lessBound,
616 # 'variable': vu.modVarPrs,
617 # 'bounds': binLims[vu.modVarPrs]['minBounds']},
618 # {'where': bu.greatEqualBound,
619 # 'variable': vu.modVarPrs,
620 # 'bounds': binLims[vu.modVarPrs]['maxBounds']},
621 # ],
622 # 'values': binLims[vu.modVarPrs]['values'],
623 # },
624 # },
625 # vu.modVarAlt: {
626 # bu.identityBinMethod: {
627 # 'filters': [
628 # {'where': bu.lessBound,
629 # 'variable': vu.modVarAlt,
630 # 'bounds': binLims[vu.modVarAlt]['minBounds']},
631 # {'where': bu.greatEqualBound,
632 # 'variable': vu.modVarAlt,
633 # 'bounds': binLims[vu.modVarAlt]['maxBounds']},
634 # ],
635 # 'values': binLims[vu.modVarAlt]['values'],
636 # },
637 # },
638 # vu.modVarLat: {
639 # bu.identityBinMethod: {
640 # 'filters': [
641 # {'where': bu.lessBound,
642 # 'variable': vu.modVarLat,
643 # 'bounds': binLims[vu.modVarLat]['minBounds']},
644 # {'where': bu.greatEqualBound,
645 # 'variable': vu.modVarLat,
646 # 'bounds': binLims[vu.modVarLat]['maxBounds']},
647 # ],
648 # 'values: binLims[vu.modVarLat]['values'],
649 # },
650 # bu.latbandsMethod: {
651 # 'filters': [
652 # {'where': bu.lessBound,
653 # 'variable': vu.modVarLat,
654 # 'bounds': namedLatBands['minBounds']},
655 # {'where': bu.greatEqualBound,
656 # 'variable': vu.modVarLat,
657 # 'bounds': namedLatBands['maxBounds']},
658 # ],
659 # 'values': namedLatBands['values'],
660 # },
661 # },
662 # vu.modelRegionBinVar: {
663 # 'AFRICA': nullBinMethod,
664 # 'ATLANTIC': nullBinMethod,
665 # 'AUSTRALIA': nullBinMethod,
666 # 'CONUS': {
667 # 'filters': [
668 # {'where': bu.lessBound,
669 # 'variable': vu.modVarLon,
670 # 'bounds': 234.0},
671 # {'where': bu.greatBound,
672 # 'variable': vu.modVarLon,
673 # 'bounds': 294.0},
674 # {'where': bu.lessBound,
675 # 'variable': vu.modVarLat,
676 # 'bounds': 25.0},
677 # {'where': bu.greatBound,
678 # 'variable': vu.modVarLat,
679 # 'bounds': 50.0},
680 # ],
681 # 'values': ['CONUS'],
682 # },
683 # 'EUROPE': nullBinMethod,
684 # 'E_EUROPE': nullBinMethod,
685 # 'NAMERICA': nullBinMethod,
686 # 'PACIFIC': nullBinMethod,
687 # 'SAMERICA': nullBinMethod,
688 # 'SE_ASIA': nullBinMethod,
689 # 'S_ASIA': nullBinMethod,
690 # bu.geoirlatlonboxMethod: {
691 # 'filters': [
692 # {'where': bu.lessBound,
693 # 'variable': vu.modelVarLon,
694 # 'bounds': geoirLonBands['minBounds']},
695 # {'where': bu.greatBound,
696 # 'variable': vu.modelVarLon,
697 # 'bounds': geoirLonBands['maxBounds']},
698 # {'where': bu.lessBound,
699 # 'variable': vu.modelVarLat,
700 # 'bounds': geoirLatBands['minBounds']},
701 # {'where': bu.greatBound,
702 # 'variable': vu.modelVarLat,
703 # 'bounds': geoirLatBands['maxBounds']},
704 # ],
705 # 'values': geoirLonBands['values'],
706 # },
707 # },
708 }
709 
710 
711 #=============================
712 # Parameterized binVarConfigs
713 #=============================
714 
715 # Add bu.identityBinMethod for identity ranged binning variables
716 identityRangeBinVars = {
717  vu.obsVarAlt: [vu.altMeta, []],
718  vu.obsVarACI: [bu.AsymmetricCloudImpact, ['obs','bak','ana','SCI']],
719  vu.obsVarCldFrac: [vu.cldfracMeta, ['obs','bak','ana','SCI']],
720  vu.obsVarGlint: [bu.GlintAngle, ['obs','bak','ana','SCI']],
721  vu.obsVarLandFrac: [vu.landfracGeo, ['obs','bak','ana','SCI']],
722  vu.obsVarLat: [vu.latMeta, ['obs','bak','ana','SCI']],
723  vu.obsVarLT: [bu.LocalHour, ['obs','bak','ana','SCI']],
724  vu.obsVarNormErr: [bu.NormalizedError, []],
725  vu.obsVarPrs: [vu.prsMeta, []],
726  vu.obsVarSenZen: [vu.senzenMeta, ['obs','bak','ana','SCI']],
727 }
728 for binVar, rangeVar in identityRangeBinVars.items():
729  if binVar not in binVarConfigs: binVarConfigs[binVar] = {}
730  binVarConfigs[binVar][bu.identityBinMethod] = {
731  'filters': [
732  {'where': bu.lessBound,
733  'variable': rangeVar[0],
734  'bounds': binLims[binVar]['minBounds']},
735  {'where': bu.greatEqualBound,
736  'variable': rangeVar[0],
737  'bounds': binLims[binVar]['maxBounds']},
738  AnyBadQC,
739  ],
740  'values': binLims[binVar]['values'],
741  'override_exclusiveDiags': rangeVar[1],
742  }
743 
744 # Add named latitude-band-specific bins for applicable ranged variables
745 latBinVars = {
746  vu.obsVarAlt: [vu.altMeta, []],
747  vu.obsVarPrs: [vu.prsMeta, []],
748 }
749 for binVar, rangeVar in latBinVars.items():
750  if binVar not in binVarConfigs: binVarConfigs[binVar] = {}
751  for iband, latBand in enumerate(namedLatBands['values']):
752  binVarConfigs[binVar][latBand] = {
753  'filters': [
754  {'where': bu.lessBound,
755  'variable': rangeVar[0],
756  'bounds': binLims[binVar]['minBounds']},
757  {'where': bu.greatEqualBound,
758  'variable': rangeVar[0],
759  'bounds': binLims[binVar]['maxBounds']},
760  {'where': bu.lessBound,
761  'variable': vu.latMeta,
762  'bounds': namedLatBands['minBounds'][iband]},
763  {'where': bu.greatEqualBound,
764  'variable': vu.latMeta,
765  'bounds': namedLatBands['maxBounds'][iband]},
766  AnyBadQC,
767  ],
768  'values': binLims[binVar]['values'],
769  'override_exclusiveDiags': rangeVar[1],
770  }
771 
772 # Add named cloud fraction-band-specific bins for applicable ranged variables
773 cldfracBinVars = {
774 # vu.obsVarACI: [bu.AsymmetricCloudImpact],
775 # vu.obsVarGlint: [bu.GlintAngle],
776 # vu.obsVarLandFrac: [vu.landfracGeo],
777  vu.obsVarLat: [vu.latMeta],
778 # vu.obsVarLT: [bu.LocalHour],
779 # vu.obsVarSenZen: [vu.senzenMeta],
780 }
781 for binVar, rangeVar in cldfracBinVars.items():
782  if binVar not in binVarConfigs: binVarConfigs[binVar] = {}
783  for iband, cldBand in enumerate(namedCldFracBands['values']):
784  binVarConfigs[binVar][cldBand] = {
785  'filters': [
786  {'where': bu.lessBound,
787  'variable': rangeVar[0],
788  'bounds': binLims[binVar]['minBounds']},
789  {'where': bu.greatEqualBound,
790  'variable': rangeVar[0],
791  'bounds': binLims[binVar]['maxBounds']},
792  {'where': bu.lessBound,
793  'variable': vu.cldfracMeta,
794  'bounds': namedCldFracBands['minBounds'][iband]},
795  {'where': bu.greatBound,
796  'variable': vu.cldfracMeta,
797  'bounds': namedCldFracBands['maxBounds'][iband]},
798  AnyBadQC,
799  ],
800  'values': binLims[binVar]['values'],
801  'override_exclusiveDiags': ['obs','bak','ana','SCI'],
802  }
803 
804 # Add named land fraction-band-specific bins for applicable ranged variables
805 landfracBinVars = {
806  vu.obsVarSCI: [bu.SCIOkamoto],
807 }
808 for binVar, rangeVar in landfracBinVars.items():
809  if binVar not in binVarConfigs: binVarConfigs[binVar] = {}
810  for iband, surfBand in enumerate(namedLandFracBands['values']):
811  binVarConfigs[binVar][surfBand] = {
812  'filters': [
813  {'where': bu.lessBound,
814  'variable': rangeVar[0],
815  'bounds': binLims[binVar]['minBounds']},
816  {'where': bu.greatEqualBound,
817  'variable': rangeVar[0],
818  'bounds': binLims[binVar]['maxBounds']},
819  {'where': bu.lessBound,
820  'variable': vu.landfracGeo,
821  'bounds': namedLandFracBands['minBounds'][iband]},
822  {'where': bu.greatBound,
823  'variable': vu.landfracGeo,
824  'bounds': namedLandFracBands['maxBounds'][iband]},
825  AnyBadQC,
826  ],
827  'values': binLims[binVar]['values'],
828  'override_exclusiveDiags': ['obs','bak','ana','SCI'],
829  }