Distributed Quantitative Precipitation Forecasting Using Information from Radar and Numerical Weather Prediction Models
Distributed Quantitative Precipitation Forecasting Using Information from Radar and Numerical Weather Prediction Models
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使用雷达和数值天气预报模型信息进行分布式定量降水预报
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
R. Bras
中科院分区：
文献类型：
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作者：
A. Ganguly;R. Bras
短期(1-6小时)分布式定量降水预报(DQPF)的好处是众所周知的。然而，这一领域被公认为是水文气象学中最具挑战性的领域之一。以前的研究表明，可以通过利用雷达和数值天气预报(NWP)模式的相关信息，在各自最适合的情况下使用过程物理和数据指示工具，来增强“最先进”的方法。测试表明，通过将整个问题分解成组件过程，可以获得更好的结果，并且每个过程可能需要从简单内插到统计时间序列模型和人工神经网络(ANN)的替代工具。提出了一种新的混合模式策略，它利用雷达[天气监视雷达-1998多普勒(WSR-88D)网络：4公里，1小时]的测量和数值预报模式(48公里ETA模式：48公里，6小时)的输出。与现有的雷达外推、基于数值预报的QPF以及雷达外推和基于数值预报的QPF相结合的方法相比，该策略改进了分布式QPF。
The benefits of short-term (1‐6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the ‘‘state of the art’’ methods can be enhanced by exploiting relevant information from radar and numerical weather prediction (NWP) models, using process physics and data-dictated tools where each fits best. Tests indicate that improved results are obtained by decomposing the overall problem into component processes, and that each process may require alternative tools ranging from simple interpolation to statistical time series models and artificial neural networks (ANNs). A new hybrid modeling strategy is proposed for DQPF that utilizes measurements from radar [Weather Surveillance Radar-1998 Doppler (WSR-88D) network: 4 km, 1 h] and outputs from NWP models (48-km Eta Model: 48 km, 6 h). The proposed strategy improves distributed QPF over existing methods like radar extrapolation or NWP-based QPF by themselves, as well as combinations of radar extrapolation and NWP-based QPF.