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Can Lagrangian Extrapolation of Radar Fields Be Used for Precipitation Nowcasting over Complex Alpine Orography?

机译:可以将雷达场的拉格朗日外推法用于复杂高山地形上的临近降水预报吗?

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In this study, a Lagrangian radar echo extrapolation scheme (MAPLE) was tested for use in very short-term forecasting of precipitation over a complex orographic region. The high-resolution forecasts from MAPLE for lead times of 5 min-5 h are evaluated against the radar observations for 20 summer rainfall events by employing a series of categorical, continuous, and neighborhood evaluation techniques. The verification results are then compared with those from Eulerian persistence and high-resolution numerical weather prediction model [the Consortium for Small-scale Modeling model (COSMO2)] forecasts. The forecasts from the MAPLE model clearly outperformed Eulerian persistence forecasts for all the lead times, and had better skill compared to COSMO2 up to lead time of 3 h on average. The results also showed that the predictability achieved from the MAPLE model depends on the spatial structure of the precipitation patterns. This study is a first implementation of the MAPLE model over a complex Alpine region. In addition to comprehensive evaluation of precipitation forecast products, some open questions related to the nowcasting of rainfall over a complex terrain are discussed.
机译:在这项研究中,对拉格朗日雷达回波外推方案(MAPLE)进行了测试,以用于非常短期的复杂地形区域降水预测。通过采用一系列分类,连续和邻域评估技术,针对雷达观测到的20个夏季降雨事件,评估了MAPLE的5分钟至5小时提前期的高分辨率预报。然后,将验证结果与欧拉持续性和高分辨率数值天气预报模型[小型模型联盟(COSMO2)]预测的结果进行比较。 MAPLE模型的预测在所有交货时间上均明显优于欧拉持续性预报,并且与COSMO2相比,平均交货时间为3 h时具有更好的技能。结果还表明,从MAPLE模型获得的可预测性取决于降水模式的空间结构。这项研究是MAPLE模型在复杂的高山地区的首次实施。除了对降水预报产品进行综合评估外,还讨论了一些与复杂地形上的降雨临近预报有关的开放性问题。

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