Abstract:  A huge computational cost in the ensemble forecast is primary challenge for the ensemble Kalman filter application into high-resolution mesoscale models under an operational environment. The dual-resolution ensemble Kalman filter algorithm could significantly save the time of computation because its covariance matrix of errors is provided by a group of low resolution samples. Using the simulated data, the dual-resolution ensemble Kalman filter algorithm was tested and it was compared with the high-resolution ensemble Kalman filter method. The results show that in the first assimilation cycle, the center locations of high and low values of simulated increments of horizontal wind field and disturbed potential temperature field at 500 hPa and their observed increment fields are same for both methods, and the structures are close to the observational increment field. The increment value from the approach of the high resolution ensemble Kalman filter is closer to the observational value than that from the dual-resolution method. In the forecast-assimilation cycle test, the root mean square errors from the two methods decrease generally with the increase of assimilation number and it suggests that both methods have compatible assimilation abilities. The result from the dual-resolution is poorer than that of the high resolution method, but the running time of the former is only 1/6 of that of the latter.

Key words: Ensemble Kalman Filter, Resolution, Forecast error covariance matrix, Computational time cost