LAUSR

This paper presents a new multiple linear regression (MLR) approach to updating the hourly, extrapolated precipitation forecasts generated by the INCA (Integrated Nowcasting through Comprehensive Analysis) system for the Eastern Alps. The generalized form of the model approximates the updated precipitation forecast as a linear response to combinations of predictors selected through a backward elimination algorithm from a pool of predictors. The predictors comprise the raw output of the extrapolated precipitation forecast, the latest radar observations, the convective analysis, and the precipitation analysis. For every MLR model, bias and distribution correction procedures are designed to further correct the systematic regression errors. Applications of the MLR models to a verification dataset containing two months of qualified samples, and to one-month gridded data, are performed and evaluated. Generally, MLR yields slight, but definite, improvements in the intensity accuracy of forecasts during the late evening to morning period, and significantly improves the forecasts for large thresholds. The structure–amplitude–location scores, used to evaluate the performance of the MLR approach, based on its simulation of morphological features, indicate that MLR typically reduces the overestimation of amplitudes and generates similar horizontal structures in precipitation patterns and slightly degraded location forecasts, when compared with the extrapolated nowcasting.摘要本文基于实时雷达观测资料, 对流参数以及实时降水分析等多源数据,采用多元线性回归建立了降水外推预报的后验统计外推方法,并应用于综合分析集成临近预报系统(INCA)在阿尔卑斯山东部夏季逐小时降水外推预报,本文设计了包括偏差及分布误差在内的两步订正方法,以修正系统性回归误差,并使线性回归后的预报值概率密度分布更加接近实际的观测分布. 另外,本文对多元线性回归模型进行了交叉验证并对各项主要因子的重要性进行了讨论.采用结构-强度-位置(SAL)检验方法对2014年7月的统计外推降水预报效果进行评估后看出,统计外推预报有效地修正了传统外推易于报出过量降水的缺陷,但也易于形成较多分散小尺度降水而导致位置评分略有下降;通过个例分析可以发现,统计外推方法在局地热力对流的初始阶段能够更好地捕捉到对流单体的发展;而对线状组织对流系统,统计外推方法较原始外推预报有效地提升了降水强度预报性能.