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Impact of Data Assimilation on Short-Term Precipitation Forecasts Using WRF-ARW Model

机译：使用WRF-ARW模型的数据同化对短期降水预报的影响

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In spite of efforts made by the scientific community during the last decades on the weather forecast improving, prediction of precipitation systems and fogs is still considered to be a difficult challenge. The main reason for the difficulties in prediction of these phenomena is the complexity of their formation, such as orography dependence, spatio-temporal inhomogeneity of land use and large scale synoptic conditions. Remote sensing and in-situ data assimilation have been applied to a number of studies in recent years, demonstrating significant improvements of the model results. The objective of this study is to evaluate the performance of Weather Research and Forecasting (WRF) model, and assess the improvement in the short-term precipitation forecast, using high-resolution data assimilation of satellite and in-situ measurements. The study case is specific weather phenomenon for the Eastern parts of Balkan Peninsula - passing winter Mediterranean cyclone causing excessive amounts of rainfall in Bulgaria. A three-dimensional variational (3D-Var) data assimilation system is used in this study. The model results obtained using or not data assimilation procedure, are compared to demonstrate the impact of this method on the start time of precipitation, rainfall spacial distribution and amount.
机译：尽管科学界在过去几十年中为改善天气预报做出了努力，但仍然认为对降水系统和雾的预测仍然是一项艰巨的挑战。难以预测这些现象的主要原因是其形成的复杂性，例如地形依赖性，土地利用的时空不均匀性和大规模天气条件。近年来，遥感和原位数据同化已应用于许多研究中，证明了模型结果的显着改进。这项研究的目的是使用高分辨率的卫星和原位测量数据同化方法，评估天气研究和预报（WRF）模型的性能，并评估短期降水预报的改进。该研究案例是巴尔干半岛东部地区的特定天气现象-越过冬季地中海气旋，导致保加利亚大量降雨。本研究中使用了三维变分（3D-Var）数据同化系统。比较使用或不使用数据同化程序获得的模型结果，以证明该方法对降水开始时间，降雨空间分布和降水量的影响。

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