Data assimilation method based on the constraints of confidence region

Yong Li, Siming Li, Yao Sheng, Luheng Wang

Research output: Contribution to journalArticle

Abstract

The ensemble Kalman filter (EnKF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence. Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called EnCR, to estimate the inflation parameter of the forecast error variance of the EnKF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods.

Original languageEnglish (US)
Pages (from-to)334-345
Number of pages12
JournalAdvances in Atmospheric Sciences
Volume35
Issue number3
DOIs
Publication statusPublished - Mar 1 2018

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All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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