短期集合预报技术在梅雨降水预报中的试验研究被引量：59

EXPERIMENT AND RESEARCH OF SHORT-RANGE ENSEMBLE FORECASTING TECHNIQUES IN FORECASTING MEIYU PRECIPITATION

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摘要数值预报的误差来源于初始场和模式的误差 ,集合预报技术是减小这些误差的有效方法。该文以MM5模式作为试验模式框架 ,模式的积云参数化方案分别取Anthes Kuo、Grell、Kain Fritsch和Betts Miller方案 ,边界层参数化方案分别取MRF和Eta方案 ,通过组合 4种积云参数化方案和两种边界层参数化方案产生 8个集合成员 ,对 1 999年华东地区梅雨期间 3个降水个例进行 48h集合预报试验。结果显示不同集合成员的预报结果各不相同 ,积云参数化方案对降水的影响比边界层参数化方案对降水的影响大 ;不同集合成员预报降水的偏差也各不相同 ,大多存在湿偏差 ,量级小的降水的湿偏差程度比量级大的降水的湿偏差程度小 ;对于不同个例 ,各成员中预报效果相对较好的成员是不同的 ,集合平均后可以得到一个比较稳定的预报结果 ;从集合预报结果中还能得到客观化和定量化的降水概率预报 ,它能对可能发生的天气现象发出信号。 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.

作者王晨稀端义宏

机构地区上海台风研究所

出处《应用气象学报》 CSCD 北大核心 2003年第1期69-78,共10页 Journal of Applied Meteorological Science

关键词数值预报积云短期集合预报短期集合预报技术梅雨降水预报 Ensemble forecasting Meiyu Precipitation

分类号 P456 [天文地球—大气科学及气象学]

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