Ensemble Kalman Filter and 4D-Var Intercomparison with the Japanese Operational Global Analysis and Prediction System

Abstract The local ensemble transform Kalman filter (LETKF) is implemented and assessed with the experimental operational system at the Japanese Meteorological Agency (JMA). This paper describes the details of the LETKF system and verification of deterministic forecast skill. JMA has been operating a four-dimensional variational data assimilation (4D-Var) system for global numerical weather prediction since 2005. The main purpose of this study is to make a reasonable comparison between the LETKF and the operational 4D-Var. Several forecast–analysis cycle experiments are performed to find sensitivity to the parameters of the LETKF. The difference between additive and multiplicative error covariance inflation schemes is investigated. Moreover, an adaptive bias correction method for satellite radiance observations is proposed and implemented, so that the LETKF is equipped with functionality similar to the variational bias correction used in the operational 4D-Var. Finally, the LETKF is compared with the operational 4D-Var. Although forecast verification scores of the two systems relative to each system’s own analyses and to radiosonde observations show some disagreement, the overall conclusion indicates that the LETKF and 4D-Var have essentially comparable performance. The LETKF shows larger temperature bias in the lower troposphere mainly over the ocean, which is related to a well-known JMA model bias that plays an important role in the significant degradation of the forecast scores in the SH. The LETKF suffers less of a performance degradation than 4D-Var in the absence of satellite radiance assimilation. This suggests that better treatment of satellite radiances would be important in future developments toward operational use of the LETKF. Developing both LETKF and 4D-Var at JMA has shown significant benefits by the synergistic effect and is the recommended strategy for the moment.