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A mesoscale ensemble Kalman filter (EnKF) for a regional model is often initialized from global analysis products and with initial ensemble perturbations constructed based on the background error covariance used in the three-dimensional variational data assimilation (3DVar) system. Because of the lack of proper mesoscale information, a long spinup period of typically a few days is required for the regional EnKF to reach its asymptotic level of accuracy, and thus, the impact of observations is limited during the EnKF spinup. For the case of typhoon assimilation, such spinup usually corresponds to the stages of generation and development of tropical cyclones, when observations are important but limited over open waters. To improve the analysis quality during the spinup, the “running in place” (RIP) method is implemented within the framework of the local ensemble transform Kalman filter (LETKF) coupled with the Weather Research and Forecasting model (WRF). Results from observing system simulation exp...

Accelerating the EnKF Spinup for Typhoon Assimilation and Prediction