libsim  Versione 7.2.6

◆ vol7d_compute_stat_proc()

subroutine vol7d_class_compute::vol7d_compute_stat_proc ( type(vol7d), intent(inout)  this,
type(vol7d), intent(out)  that,
integer, intent(in)  stat_proc_input,
integer, intent(in)  stat_proc,
type(timedelta), intent(in)  step,
type(datetime), intent(in), optional  start,
logical, intent(in), optional  full_steps,
real, intent(in), optional  frac_valid,
type(timedelta), intent(in), optional  max_step,
logical, intent(in), optional  weighted,
type(vol7d), intent(inout), optional  other 
)

General-purpose method for computing a statistical processing on data in a vol7d object already processed with the same statistical processing, on a different time interval specified by step and start.

This method tries to apply all the suitable specialized statistical processing methods according to the input and output statistical processing requested. The argument stat_proc_input determines which data will be processed, while the stat_proc argument determines the type of statistical process to be applied and which will be owned by output data.

The possible combinations are:

  • stat_proc_input = 254
    • stat_proc = 0 average instantaneous observations
    • stat_proc = 2 compute maximum of instantaneous observations
    • stat_proc = 3 compute minimum of instantaneous observations
    • stat_proc = 4 compute difference of instantaneous observations
    • stat_proc = 6 compute standard deviation of instantaneous observations
    • stat_proc = 201 compute the prevailing direction (mode) on specified sectors, suitable only for variables representing an angle in degrees, e.g. wind direction

processing is computed on longer intervals by aggregation, see the description of vol7d_compute_stat_proc_agg()

  • stat_proc_input = *
    • stat_proc = 254 consider statistically processed values as instantaneous without any extra processing

see the description of vol7d_decompute_stat_proc()

  • stat_proc_input = 0, 1, 2, 3, 4, 200

    • stat_proc = stat_proc_input recompute input data on different intervals

    the same statistical processing is applied to obtain data processed on a different interval, either longer, by aggregation, or shorter, by differences, see the description of vol7d_recompute_stat_proc_agg() and vol7d_recompute_stat_proc_diff() respectively; it is also possible to provide stat_proc_input /= stat_proc, but it has to be used with care.

  • stat_proc_input = 0

    • stat_proc = 1

    a time-averaged rate or flux is transformed into a time-integrated value (sometimes called accumulated) on the same interval by multiplying the values by the length of the time interval in seconds, keeping constant all the rest, including the variable; the unit of the variable implicitly changes accordingly, this is supported officially in grib2 standard, in the other cases it is a forcing of the standards.

  • stat_proc_input = 1

    • stat_proc = 0

    a time-integrated value (sometimes called accumulated) is transformed into a time-averaged rate or flux on the same interval by dividing the values by the length of the time interval in seconds, see also the previous description of the opposite computation.

If a particular statistical processing cannot be performed on the input data, the program continues with a warning and, if requested, the input data is passed over to the volume specified by the other argument, in order to allow continuation of processing. All the other parameters are passed over to the specifical statistical processing methods and are documented there.

Parametri
[in,out]thisvolume providing data to be recomputed, it is not modified by the method, apart from performing a vol7d_alloc_vol on it
[out]thatoutput volume which will contain the recomputed data
[in]stat_proc_inputtype of statistical processing of data that has to be processed (from grib2 table), only data having timerange of this type will be processed, the actual statistical processing performed and which will appear in the output volume, is however determined by stat_proc argument
[in]stat_proctype of statistical processing to be recomputed (from grib2 table), data in output volume that will have a timerange of this type
[in]steplength of the step over which the statistical processing is performed
[in]startstart of statistical processing interval
[in]full_stepsif .TRUE. apply processing only on intervals starting at a forecast time or a reference time modulo step
[in]frac_validminimum fraction of valid data required for considering acceptable a recomputed value, default=1.
[in]max_stepmaximum allowed distance in time between two contiguougs valid data within an interval, for the interval to be eligible for statistical processing
[in]weightedif provided and .TRUE., the statistical process is computed, if possible, by weighting every value with a weight proportional to its validity interval
[in,out]otheroptional volume that, on exit, is going to contain the data that did not contribute to the accumulation computation

Definizione alla linea 304 del file vol7d_class_compute.F90.

306 
307 ALLOCATE(ttr_mask(SIZE(this%time), SIZE(this%timerange)))
308 linshape = (/SIZE(ttr_mask)/)
309 ! finally perform computations
310 IF (ASSOCIATED(this%voldatir)) THEN
311  DO j = 1, SIZE(that%timerange)
312  DO i = 1, SIZE(that%time)
313 
314  DO i1 = 1, SIZE(this%ana)
315  DO i3 = 1, SIZE(this%level)
316  DO i6 = 1, SIZE(this%network)
317  DO i5 = 1, SIZE(this%dativar%r)
318 
319  frac_m = 0.
320  DO n1 = SIZE(dtratio), 1, -1 ! precedence to longer periods
321  IF (dtratio(n1) <= 0) cycle ! safety check
322  ttr_mask = .false.
323  DO n = 1, map_ttr(i,j)%arraysize
324  IF (map_ttr(i,j)%array(n)%extra_info == dtratio(n1)) THEN
325  IF (c_e(this%voldatir(i1,map_ttr(i,j)%array(n)%it,i3, &
326  map_ttr(i,j)%array(n)%itr,i5,i6))) THEN
327  ttr_mask(map_ttr(i,j)%array(n)%it, &
328  map_ttr(i,j)%array(n)%itr) = .true.
329  ENDIF
330  ENDIF
331  ENDDO
332 
333  ndtr = count(ttr_mask)
334  frac_c = real(ndtr)/real(dtratio(n1))
335 
336  IF (ndtr > 0 .AND. frac_c >= max(lfrac_valid, frac_m)) THEN
337  frac_m = frac_c
338  SELECT CASE(stat_proc)
339  CASE (0, 200) ! average, vectorial mean
340  that%voldatir(i1,i,i3,j,i5,i6) = &
341  sum(this%voldatir(i1,:,i3,:,i5,i6), &
342  mask=ttr_mask)/ndtr
343  CASE (1, 4) ! accumulation, difference
344  that%voldatir(i1,i,i3,j,i5,i6) = &
345  sum(this%voldatir(i1,:,i3,:,i5,i6), &
346  mask=ttr_mask)
347  CASE (2) ! maximum
348  that%voldatir(i1,i,i3,j,i5,i6) = &
349  maxval(this%voldatir(i1,:,i3,:,i5,i6), &
350  mask=ttr_mask)
351  CASE (3) ! minimum
352  that%voldatir(i1,i,i3,j,i5,i6) = &
353  minval(this%voldatir(i1,:,i3,:,i5,i6), &
354  mask=ttr_mask)
355  CASE (6) ! stddev
356  that%voldatir(i1,i,i3,j,i5,i6) = &
357  stat_stddev( &
358  reshape(this%voldatir(i1,:,i3,:,i5,i6), shape=linshape), &
359  mask=reshape(ttr_mask, shape=linshape))
360  END SELECT
361  ENDIF
362 
363  ENDDO ! dtratio
364  ENDDO ! var
365  ENDDO ! network
366  ENDDO ! level
367  ENDDO ! ana
368  CALL delete(map_ttr(i,j))
369  ENDDO ! otime
370  ENDDO ! otimerange
371 ENDIF
372 
373 IF (ASSOCIATED(this%voldatid)) THEN
374  DO j = 1, SIZE(that%timerange)
375  DO i = 1, SIZE(that%time)
376 
377  DO i1 = 1, SIZE(this%ana)
378  DO i3 = 1, SIZE(this%level)
379  DO i6 = 1, SIZE(this%network)
380  DO i5 = 1, SIZE(this%dativar%d)
381 
382  frac_m = 0.
383  DO n1 = SIZE(dtratio), 1, -1 ! precedence to longer periods
384  IF (dtratio(n1) <= 0) cycle ! safety check
385  ttr_mask = .false.
386  DO n = 1, map_ttr(i,j)%arraysize
387  IF (map_ttr(i,j)%array(n)%extra_info == dtratio(n1)) THEN
388  IF (c_e(this%voldatid(i1,map_ttr(i,j)%array(n)%it,i3, &
389  map_ttr(i,j)%array(n)%itr,i5,i6))) THEN
390  ttr_mask(map_ttr(i,j)%array(n)%it, &
391  map_ttr(i,j)%array(n)%itr) = .true.
392  ENDIF
393  ENDIF
394  ENDDO
395 

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