Changes between Version 24 and Version 25 of ImplementAnalysisseik

Timestamp:

May 17, 2011, 3:25:51 PM (14 years ago)

Author:

lnerger

Comment:

--

ImplementAnalysisseik

@@ -156 +156 @@
  * If a user considers to perform adjustments to the estimates (e.g. for balances), this routine is the right place for it.
  * Only for the SEEK filter the state vector (`state_p`) is initialized. For all other filters, the array is allocated, but it can be used freely during the execution of `U_prepoststep`.
- * The interface has a difference for ETKF and SEIK: For the ETKF, the array `Uinv` has size `dim_ens` x `dim_ens`. In contrast it has size `dim_ens-1` x `dim_ens-1` for the SEIK filter.
+ * The interface has a difference for ETKF and SEIK: For the ETKF, the array `Uinv` has size `dim_ens` x `dim_ens`. In contrast it has size `dim_ens-1` x `dim_ens-1` for the SEIK filter. (For most cases, this will be irrelevant, because most usually the ensemble array `ens_p` is used for computations, rather than `Uinv`. However, for the SEIK filter with fixed covariance matrix, `Uinv` is required to compute the estimate analysis error. The fixed covariance matrix mode is not available for the ETKF)
 
 
@@ -182 +182 @@
  * The routine does not require that the product is implemented as a real matrix-matrix product. Rather, the product can be implemented in its most efficient form. For example, if the observation error covariance matrix is diagonal, only the multiplication of the diagonal with matrix `A_p` has to be implemented.
  * The observation vector `obs_p` is provided through the interface for cases where the observation error variance is relative to the actual value of the observations.
- * The interface has a difference for SEIK and ETKF: For ETKF the third argument is the ensemble size (`dim_ens`), while for SEIK it is the rank (`rank`) of the covariance matrix (usually ensemble size minus one). In addition, the second dimension of `A_p` and `C_p` has size `dim_ens` for ETKF, while it is `rank` for the SEIK filter. (For most cases, this will be irrelevant, because most usually the ensemble array `ens_p` is used for computations, rather than `Uinv`. However, for the SEIK filter with fixed covariance matrix, `Uinv` is required to compute the estimate analysis error. The fixed covariance matrix mode is not available for the ETKF)
+ * The interface has a difference for SEIK and ETKF: For ETKF the third argument is the ensemble size (`dim_ens`), while for SEIK it is the rank (`rank`) of the covariance matrix (usually ensemble size minus one). In addition, the second dimension of `A_p` and `C_p` has size `dim_ens` for ETKF, while it is `rank` for the SEIK filter. 
 
 === `U_init_obsvar` (init_obsvar.F90) ===
@@ -216 +216 @@
 
 The analysis step is executed when the ensemble integration of the forecast is completed. During the analysis step the following routines are executed in the given order:
- 1. [#U_prepoststepprepoststep_seik.F90 U_prepoststep] (call to handle the forecast, called with negative value of the time step)
+ 1. [#U_prepoststepprepoststep_seik.F90 U_prepoststep] (Call to act on the forecast ensemble, called with negative value of the time step)
  1. [#U_init_dim_obsinit_dim_obs.F90 U_init_dim_obs]
  1. [#U_obs_opobs_op.F90 U_obs_op] (A single call to operate on the ensemble mean state)
  1. [#U_init_obsinit_obs.F90 U_init_obs]
- 1. [#U_obs_opobs_op.F90 U_obs_op] (`dim_ens` calls; one call for each ensemble member)
+ 1. [#U_obs_opobs_op.F90 U_obs_op] (`dim_ens` calls: one call for each ensemble member)
  1. [#U_init_obsvarinit_obsvar.F90 U_init_obsvar] (Only executed, if the adaptive forgetting factor is used (`type_forget=1` in the example implemention))
  1. [#U_prodRinvAprodrinva.F90 U_prodRinvA]
- 1. [#U_prepoststepprepoststep_seik.F90 U_prepoststep] (call to handle the analysis, called with (positive) value of the time step)
-
+ 1. [#U_prepoststepprepoststep_seik.F90 U_prepoststep] (Call to act on the analysis ensemble, called with (positive) value of the time step)
+