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首页> 外文期刊>Advances in Atmospheric Sciences >Rainfall assimilation using a new four-dimensional variational method: A single-point observation experiment
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Rainfall assimilation using a new four-dimensional variational method: A single-point observation experiment

机译:使用新的四维变分方法进行降雨同化:单点观测实验

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摘要

Accurate forecast of rainstorms associated with the mei-yu front has been an important issue for the Chinese economy and society. In July 1998 a heavy rainstorm hit the Yangzi River valley and received widespread attention from the public because it caused catastrophic damage in China. Several numerical studies have shown that many forecast models, including Pennsylvania State University National Center for Atmospheric Research’s fifth-generation mesoscale model (MM5), failed to simulate the heavy precipitation over the Yangzi River valley. This study demonstrates that with the optimal initial conditions from the dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) system, MM5 can successfully reproduce these observed rainfall amounts and can capture many important mesoscale features, including the southwestward shear line and the low-level jet stream. The study also indicates that the failure of previous forecasts can be mainly attributed to the lack of mesoscale details in the initial conditions of the models.
机译:准确预测与梅雨锋有关的暴雨一直是中国经济和社会的重要问题。 1998年7月,一场大暴雨袭击了扬子江流域,受到了公众的广泛关注,因为这在中国造成了灾难性的破坏。多项数值研究表明,许多预报模型,包括宾夕法尼亚州立大学国家大气研究中心的第五代中尺度模型(MM5),都未能模拟长江流域的强降水。这项研究表明,利用降维投影的四维变分数据同化(DRP-4DVar)系统的最佳初始条件,MM5可以成功地再现这些观测到的降雨量,并且可以捕获许多重要的中尺度特征,包括西南切变线和低空急流。该研究还表明,先前预测的失败主要归因于模型初始条件中缺乏中尺度细节。

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