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A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

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Petrie, R. E. and Bannister, R. N. (2011) A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation. Computers & Fluids, 46 (1). pp. 387-391. ISSN 0045-7930

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To link to this article DOI: 10.1016/j.compfluid.2011.01.037

Abstract/Summary

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
Faculty of Science > School of Mathematical and Physical Sciences > National Centre for Earth Observation (NCEO)
ID Code:19981
Publisher:Elsevier

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