Changes between Version 8 and Version 9 of WhichFiltertouse


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Timestamp:
Dec 22, 2014, 3:36:47 PM (9 years ago)
Author:
lnerger
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  • WhichFiltertouse

    v8 v9  
    22
    33PDAF provides several filter algorithms. Here, we provide some guidance about which filter algorithm one should use.
     4
     5== The short story ==
     6
     7In short, we recommend the LESTKF filter, if a local filter is needed. For a global filter, we recommend the ESKTF. For smoothing, we recommend the smoother extensions of these two filters. In our studies by now, we obtained the best results (i.e. smallest assimilation errors) with these filters and smoothers. Further, these filters are very efficient also with very high-dimensional models.
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     9== The full story ==
    410
    511The filter algorithms that are currently implemented in PDAF have been used in comparison studies giving insight in the performance of different filter formulations. In particular, the Ensemble Kalman Filter (EnKF, Evensen, 1994) was compared with the SEEK and SEIK filters (Pham et al., 1998) in [PublicationsandPresentations Nerger et al. (2005)] (the links refer to the page listing the full references of the publications). The SEIK filter was then related to the ETKF (Bishop, 2002) in [PublicationsandPresentations Nerger et al. (2012)]. This study also introduced the ESTKF. [PublicationsandPresentations Nerger et al. (2006)] introduced the localized SEIK filter (LSEIK) and [PublicationsandPresentations Nerger and Gregg (2007)] added observation localization to the LSEIK filter. The local filters LETKF and LESTKF use the same localization method as the LSEIK filter.