Localization in ensemble data assimilation

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Kirchgessner, P. , Nerger, L. and Bunse-Gerstner, A. (2012): Localization in ensemble data assimilation , International Symposium on data assimilation, Deutscher Wetterdienst Offenbach, 8 October 2012 - 11 October 2012 .
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Supplementary Information:


In data assimilation using ensemble Kalman filter methods, localization is an important technique to get good assimilation results. For the LETKF, the domain localization (DL) and observation localization (OL) are typically used. Depending on the localization method, one has to choose appropriate values for the localization parameters, such as the localization length, the inflation factor or the weight function. Although being frequently used, the properties of the localization techniques are not fully investigated. Thus, up to now an optimal choice for these parameters is a priori unknown and they are generally found by doing expensive numerical experiments. The relationship between the localization length and the ensemble size in DL and OL is studied using twin experiments with the Lorenz-96 model. It is found that for DL the optimal localization length depends linearly on the local observation dimension. This also holds for the localization length at which the filter diverges. A similar behavior was observed for OL by considering an effective local observation dimension.

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