Changes between Version 5 and Version 6 of WhichFiltertouse

Timestamp:

Mar 1, 2012, 1:36:14 PM (13 years ago)

Author:

lnerger

Comment:

--

WhichFiltertouse

@@ -8 +8 @@
 
 The choice whether a global filter like ESTKF or a local formulation as LESTKF is used depends on the problem that is simulated. If the model represents large scale features, the global filter should be a good choice. If the model fields represent many small scale features, the local filter is required. Also the availability of observations influences that choice. If only a very small number of observations is available, it might be better to use the global filter. In contrast, spatially resolved observations, like surface temperature fields of the ocean detected by satellites, call for the local filter.
+
+Special cases like a filtering with a fixed covariance matrix or a static covariance matrix are also supported in PDAF. In case of a fixed covariance matrix one obtains an ensemble optimal interpolation (ensemble OI) algorithm in which the covariance matrix from the initialization is used for all filter analysis steps. With a static covariance matrix, the ensemble members representing the covariance matrix are updated during the analysis step. However, in the forecast phase only the ensemble mean state is integrated by the model. These special cases are currently only provided with the SEIK and LSEIK filters. They can be selected by specifying `subtype=2` for the static covariance matrix and `subtype=3` for the fixed covariance matrix. As the ensemble members are not integrated in these cases, the improved ensemble transformation of the ESTKF would not change the results.
+
+== Examples of parameter settings ==
 
 To use the global ESTKF with minimum transformation one has to specify (following the naming of variables used in the example in `/testsuite/src/dummymodel_1D/`):
@@ -21 +25 @@
 || `type_trans` || 1 || This specifies the random transformation ||
 
+
+For the local LESTKF with minimum transformation a localization radius of 10 grid points and observation localization by a 5th order polynomial one specifies:
+|| '''parameter''' || '''value''' || '''comment''' ||
+|| `filtertype` || 7 || Selecting LESTKF ||
+|| `type_sqrt` || 0 || This specifies the SVD-based symmetric square root ||
+|| `type_trans` || 0 || This specifies the deterministic ensemble transformation ||
+|| `locweight` || 4 || Observation localization by 5th order polynomial function ||
+|| `local_range` || 10 || Set influence radius of observations to 10 grid points ||
+
+For the global SEIK with fixed covariance matrix one specifies:
+|| '''parameter''' || '''value''' || '''comment''' ||
+|| `filtertype` || 1 || Selecting SEIK ||
+|| `subtype` || 3 || Select fixed covariance matrix ||
+
+The available configuration options of the filters are provided on the [wiki:AvailableOptionsforInitPDAF overview page on filter-specific options]. Some parameters are only specified outside of PDAF and only used by the user-supplied call back routines. Such options are, for example, `locweight` and `local_range` to specify the localization. These options can be found in the example implementation in `testsuite/src/dummymodel_1D`.