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A spatio-temporal evaluation of the WRF physical parameterisations for numerical rainfall simulation in semi-humid and semi-arid catchments of Northern China

机译:中国北方半湿润半干旱流域数值降雨模拟的WRF物理参数时空评估

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摘要

Mesoscale Numerical Weather Prediction systems can provide rainfall products at high resolutions in space and time, playing an increasingly more important role in water management and flood forecasting. The Weather'Research and Forecasting (WRF) model is one of the most popular mesoscale systems and has been extensively used in research and practice. However, for hydrologists, an unsolved question must be addressed before each model application in a different target area. That is, how are the most appropriate combinations of physical parameterisations from the vast WRF library selected to provide the best downscaled rainfall? In this study, the WRF model was applied with 12 designed parameterisation schemes with different combinations of physical parameterisations, including microphysics, radiation, planetary boundary layer (PBL), land-surface model (LSM) and cumulus.parameterisations. The selected study areas are two semi-humid and semi-arid catchments located in the Daqinghe River basin, Northern China. The performance of WRF with different parameterisation schemes is tested for simulating eight typical 24-h storm events with different evenness in space and time. In addition to the cumulative rainfall amount, the spatial and temporal patterns of the simulated rainfall are evaluated based on a two-dimensional composed verification statistic. Among the 12 parameterisation schemes, Scheme 4 outperforms the other schemes with the best average performance in simulating rainfall totals and temporal patterns; in contrast, Scheme 6 is generally a good choice for simulations of spatial rainfall distributions. Regarding the individual parameterisations, Single-Moment 6 (WSM6), Yonsei University (YSU), Kain-Fritsch (KF) and GrellDevenyi (GD) are better choices for microphysics, planetary boundary layers (PBL) and cumulus parameterisations, respectively, in the study area. These findings provide helpful information for WRF rainfall downscaling in semi-humid and semi-arid areas. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for generating ensembles in numerical rainfall predictions using the WRF model. (C) 2017 Elsevier B.V. All rights reserved.
机译:中尺度数值天气预报系统可以在时空上提供高分辨率的降雨产品,在水管理和洪水预报中发挥着越来越重要的作用。天气研究与预报(WRF)模型是最流行的中尺度系统之一,已广泛用于研究和实践中。但是,对于水文学家来说,在将每个模型应用到不同目标区域之前,必须解决一个尚未解决的问题。也就是说,如何从庞大的WRF库中选择最合适的物理参数组合,以提供最佳的降尺度降雨?在这项研究中,WRF模型与12种设计的参数化方案一起应用,这些方案具有不同的物理参数化组合,包括微观物理学,辐射,行星边界层(PBL),陆面模型(LSM)和积云参数化。选定的研究区域是位于中国北方大庆河流域的两个半湿润半干旱流域。测试了具有不同参数化方案的WRF的性能,以模拟八种典型的24小时暴风雨事件,这些暴风雨事件在时空上具有不同的均匀性。除了累积降雨量外,还基于二维组合验证统计量来评估模拟降雨的时空格局。在12个参数化方案中,方案4在模拟降雨总量和时间模式方面的性能优于其他方案。相反,方案6通常是模拟空间降雨分布的理想选择。对于单个参数设置,单矩6(WSM6),延世大学(YSU),凯恩·弗里奇(KF)和GrellDevenyi(GD)分别是微物理学,行星边界层(PBL)和积云参数设置的更好选择。学习区。这些发现为半湿润和半干旱地区的WRF降雨缩减提供了有用的信息。设计和测试参数化组合方案的方法学也可以视为使用WRF模型在数值降雨预测中生成集合的参考。 (C)2017 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Atmospheric research》 |2017年第7期|141-155|共15页
  • 作者单位

    China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, 1 Fuxing Rd, Beijing 100038, Peoples R China;

    China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, 1 Fuxing Rd, Beijing 100038, Peoples R China|Hohai Univ, State Key Lab Hydrol Water Resource & Hydraul Eng, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, 1 Fuxing Rd, Beijing 100038, Peoples R China;

    China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, 1 Fuxing Rd, Beijing 100038, Peoples R China;

    Nanjing Univ Informat Sci & Technol, Minist Educ, Key Lab Meteorol Disaster, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Spatio-temporal evaluation; Numerical rainfall simulation; WRF model; Physical parameterisations; Medium-sized catchments;

    机译:时空评估;降雨模拟;WRF模型;物理参数设置;中型流域;

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