Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/85606
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Type: Journal article
Title: Controlling overestimation of error covariance in ensemble Kalman filters with sparse observations: a variance-limiting Kalman filter
Author: Gottwald, G.
Mitchell, L.
Reich, S.
Citation: Monthly Weather Review, 2011; 139(8):2650-2667
Publisher: American Meteorological Society
Issue Date: 2011
ISSN: 0027-0644
1520-0493
Statement of
Responsibility: 
Georg A. Gottwald and Lewis Mitchell, Sebastian Reich
Abstract: The problem of an ensemble Kalman filter when only partial observations are available is considered. In particular, the situation is investigated where the observational space consists of variables that are directly observable with known observational error, and of variables of which only their climatic variance and mean are given. To limit the variance of the latter poorly resolved variables a variance-limiting Kalman filter (VLKF) is derived in a variational setting. The VLKF for a simple linear toy model is analyzed and its range of optimal performance is determined. The VLKF is explored in an ensemble transform setting for the Lorenz-96 system, and it is shown that incorporating the information of the variance of some unobservable variables can improve the skill and also increase the stability of the data assimilation procedure.
Rights: © 2011 American Meteorological Society
DOI: 10.1175/2011MWR3557.1
Grant ID: ARC
Published version: http://dx.doi.org/10.1175/2011MWR3557.1
Appears in Collections:Aurora harvest 7
Mathematical Sciences publications

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