Changes between Version 10 and Version 11 of WhichFiltertouse


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Timestamp:
May 28, 2020, 7:52:17 AM (4 months ago)
Author:
lnerger
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  • WhichFiltertouse

    v10 v11  
    1515The 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 only 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 the choice. If only a very small number of observations is available, it might be better to use the global filter as the observational information can be spear out over a larger region. In contrast, spatially resolved observations, like surface temperature fields of the ocean detected by satellites, call for the local filter.
    1616
    17 Special cases like data assimilation 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 ESTKF, SEEK, 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.
     17Special cases like data assimilation 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 provided for all filters, except EnKF, LEnKF, and the weight-based filters NETF, LNETF, and PF. They can be selected by specifying `subtype=2` for the static covariance matrix and `subtype=3` for the fixed covariance matrix.
    1818
    1919== Examples of parameter settings ==