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《Quarterly Journal of Applied Meteorology》 2003-01
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Wang Chenxi Duan Yihong (Shanghai Typhoon Institute, Shanghai 200030)  
Numerical weather prediction errors come from the initial conditions and model errors. Ensemble forecasting technique is an effective way to diminish the errors. Short range ensemble forecasting experiments are made for three precipitation cases during the 1999 Meiyu period in the East China area. The MM5 model is used as the experimental model configuration. Eight ensemble members are created by choosing four kinds of cumulus parameterization schemes and two kinds of PBL parameterization schemes. The four kinds of cumulus parameterization schemes are Anthes Kuo, Grell, Kain Fritsch and Betts Miller schemes. The two kinds of PBL parameterization schemes are MRF and Eta schemes. The results indicate that different ensemble members have different forecasting results. For the precipitation forecasting results, the influence of cumulus parameterization scheme is larger than the influence of the PBL parameterization scheme. For the bias score, most ensemble members have a "wet" bias. The bias score is larger for large precipitation than that for small precipitation. The effects of ensemble averaging increase the bias score for small precipitation and reduce the bias score for large precipitation. For different cases, the member who has the best precipitation forecasting results is not the same one. After ensemble averaging, stable precipitation forecasting results can be gotten. Also the objective and quantitative precipitation probability forecasts can be obtained from the ensemble forecasting.
【CateGory Index】: P456
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