A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation
✍ Scribed by R.E. Petrie; R.N. Bannister
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 188 KB
- Volume
- 46
- Category
- Article
- ISSN
- 0045-7930
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✦ Synopsis
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.