Journal
COMPUTERS & FLUIDS
Volume 46, Issue 1, Pages 387-391Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compfluid.2011.01.037
Keywords
Data assimilation; Background error covariance; Flow-dependence; Hybrid methods
Funding
- NERC [earth010005] Funding Source: UKRI
- Natural Environment Research Council [earth010005] Funding Source: researchfish
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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, P(f). 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. (C) 2011 Elsevier Ltd. All rights reserved.
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