libsim  Versione6.3.0

◆ 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

    • 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. cumulate only on intervals starting at a forecast time modulo step, default is to cumulate on all possible combinations of intervals
[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 321 del file vol7d_class_compute.F90.

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

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