||Ensemble Kalman filter (EnKF) is a method for data assimilation. In this study, we apply the Observation System Simulation Experiments (OSSE) type of experimental designs to explore the performance of assimilating Doppler radar data using EnKF. A general purpose non-hydrostatic compressible model, the ARPS (Advanced Regional Prediction System), with complex multi-class microphysics, is employed for conducting all the experiments. Artificial data sets are from a simulated classic storm case that occurred on 20 May 1977 in Del City, Oklahoma. With and without terrain, we investigate the impact of several factors on the model forecasts, with the emphasis on the issue of quantitative precipitation forecast (QPF). These factors consider the number of ensembles, the time interval and frequency of data injection, the area of data availability, and so on. The major results show that using 40 members, and assimilating the radar data once every 5 minutes, can effectively produce the forecasts with sufficient accuracy. Assimilating as many data sets as possible can help to reduce the errors, and prevent the errors from growing to an uncontrollable scale. When the terrain is present and becomes a potential blockage to the radar beams, and if one can assimilate into the model the information of the initial storm development before the storm reaches the lee side of the mountain, then it is still possible to catch the location and pattern of the storm. Such a measure makes the following model forecast maintain its accuracy, even after the storm passes the mountain, and arrives at a region where the radar beams are completely blocked. Finally, to obtain an accurate one-hour QPF also requires an one-hour of radar data assimilation. Overall speaking, the assimilation of Doppler radar data does reveal significant improvements on reducing the forecast errors.|
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