A data assimilation method of the Ensemble Kalman Filter for use in severe dust storm forecasts over China

Abstract

An Ensemble Kalman Filter (EnKF) data assimilation system was developed for a regional dust transport model. This paper applied the EnKF method to investigate modeling severe dust storm episodes occurred in March 2002 over China based on surface observations of dust concentrations to explore its impacts on forecast improvement. A series of sensitivity experiments using our system reveals that the EnKF is an advanced assimilation method to afford better initial conditions with surface observed PM10 in North China and lead to improved forecasts of dust storms, but forecast with large errors can be made by model errors. This result illustrates that it requires identifying and correcting model errors during the assimilation procedure in order to significantly improve forecasts. Results also show that the EnKF should use a large inflation parameter to obtain better model performance and forecast potential. Furthermore, the ensemble perturbations generated at the initial time should include enough ensemble spreads to represent the background error after several assimilation cycles.