A New Estimation Method of Ensemble Forecast Error in ETKF Assimilation with Nonlinear Observation Operator

作者：Huang, Chengcheng; Wu, Guocan^*; Zheng, Xiaogu

来源：SOLA, 2017, 13(0): 63-68.

DOI：10.2151/sola.2017-012

• TRANSFORM KALMAN FILTER
• ADAPTIVE COVARIANCE INFLATION
• MODEL
• ALGORITHM
• MATRICES

The estimation accuracy of ensemble forecast errors has a key influence on the assimilation results of all ensemble-based schemes. The ensemble transform Kalman filter (ETKF) assimilation scheme can assimilate nonlinear observations without using the adjoint of a dynamical model; however, the initially estimated ensemble forecast errors must be further adjusted. In this paper, the estimation of forecast error is improved using a self-generated analysis and a corresponding iterative procedure is then established for the ETKF with nonlinear observation operators. The improved assimilation scheme is validated using the Lorenz-96 model with a nonlinear and spatially correlated observation system as a test bed. The experiment results demonstrate that the improved ETKF assimilation scheme can effectively reduce the analysis error.

• 出版日期2017
• 单位中国科学院大气物理研究所; 北京师范大学