An Estimate of Inflation Factor and Analysis Sensitivity in Ensemble Kalman Filter

Other literature type English OPEN
Wu, Guocan (2016)
  • Journal: (issn: 1607-7946, eissn: 1607-7946)
  • Related identifiers: doi: 10.5194/npg-2016-44
  • Subject:
    arxiv: Physics::Atmospheric and Oceanic Physics

The estimation accuracy of forecast error matrix is crucial to the assimilation result. Ensemble Kalman filter (EnKF) is a widely used ensemble based assimilation method, which initially estimate the forecast error matrix using a Monte Carlo method with the short-term ensemble forecast states. However, this estimate needs to be further improved using inflation technique. In this study, the forecast error inflation factor is estimated based on cross validation and the analysis sensitivity is also investigated. The improved EnKF assimilation scheme is validated by assimilating spatially correlated observations to the atmosphere-like Lorenz-96 model. The experiment results show that, the analysis error is reduced and the analysis sensitivity to observations is improved.
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