Changes between Initial Version and Version 1 of PDAF_diag_variance_nompi

Timestamp:

Mar 21, 2025, 7:22:16 PM (12 days ago)

Author:

lnerger

Comment:

--

PDAF_diag_variance_nompi

@@ +1 @@
+= PDAF_diag_variance_nompi = 
+
+This page documents the routine `PDAF_diag_variance_nompi` of PDAF, which was introduced with PDAF V3.0.
+
+This routine computes the unbiased standard deviation of the ensemble and returns it in the form of a state vector. In addition the mean standard deviation is computed. This variant only compute the standard deviation for the provided ensemble array `ens`. Thus, with a domain-decomposed parallel model, the value of of the standard deviation does not consider the full domain. The variant taking into account parallelization is [wiki:PDAF_diag_variance]. 
+
+The interface is the following: 
+{{{
+SUBROUTINE PDAF_diag_variance(dim_p, dim_ens, state_p, ens_p, variance_p, &
+     stddev_g, do_mean, do_stddev, status)
+}}}
+with the following arguments:
+{{{
+  INTEGER, INTENT(in) :: dim_p                 !< state dimension
+  INTEGER, INTENT(in) :: dim_ens               !< Ensemble size
+  REAL, INTENT(inout) :: state_p(dim_p)        !< State vector
+  REAL, INTENT(in)    :: ens_p(dim_p, dim_ens) !< State ensemble
+  REAL, INTENT(out)   :: variance_p(dim_p)     !< Variance state vector
+  REAL, INTENT(out)   :: stddev_g              !< Global standard deviation of ensemble
+  INTEGER, INTENT(in) :: do_mean               !< 1 to compute ensemble mean; 0 take values froem state_p as ensemble mean
+  INTEGER, INTENT(in) :: do_stddev             !< 1 to compute the ensemble mean standard deviation; 0 no computation of ensemble standard deviation
+  INTEGER, INTENT(out) :: status               !< Status flag (0=success)
+}}}
+
+**Note:**
+ * The ensemble standard deviation is a common measure of the estimate model root mean square error. It is typically computed in `prepoststep_pdaf` to monitor the assimilation process.
+ * The option `do_mean` exists mainly for performance reasons. If `state_p` contains is ensemble mean state it does not need to be computed again.
+ * The routine compute the unbiased variance, i.e. the normalization is 1/(dim_ens-1)