文章摘要
杨明祥,雷晓辉,蒋云钟,王浩,何素明.青狮潭水库降水集合预报模式构建及应用[J].水利学报,2018,49(2):263-270
青狮潭水库降水集合预报模式构建及应用
Construction and application of precipitation ensemble forecast model for Qingshitan Reservoir
投稿时间:2017-07-11  
DOI:10.13243/j.cnki.slxb.20170683
中文关键词: 青狮潭水库  集合预报  WRF模式  参数化方案扰动
英文关键词: Qingshitan reservoir  ensemble forecast  WRF model  parameterization scheme disturbance
基金项目:桂林市防洪及漓江补水水库群生态调度技术研究(GXZC2016-G3-2344-JHZJ,桂科AB16380313);国家自然科学基金青年基金项目(51709271);中国水利水电科学研究院基本科研业务费专项项目(WR0145B212017)
作者单位
杨明祥 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038 
雷晓辉 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038 
蒋云钟 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038 
王浩 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038 
何素明 广西壮族自治区水利电力勘测设计研究院, 广西 南宁 530023 
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中文摘要:
      数值降水集合预报能够降低因单值预报带来的不确定性,是国内外关注的热点。但当前研究多将集合样本预报技巧等同对待,采用集合算数平均得到最终预报结论,这种集合预报方式较为粗放,难以有效区分不同集合样本的差异。针对该问题,本文选择位于广西暴雨中心的青狮潭水库为研究区,构建了基于ETS评分的综合定量评价模型,通过WRF模式21组参数化方案敏感性分析,以及样本数量与集合预报技巧的定量分析,依据ETS评价结果实现了青狮潭水库降水集合预报方案的构建。经验证,本文构建的集合预报方案较集合算数平均预报技巧更高,表现更加稳定。
英文摘要:
      The numerical ensemble forecast of precipitation can reduce the uncertainty caused by single value forecasting, and it is a hot topic both at home and abroad. But most of the researches at present treat the forecast skills of set members equally, and obtain the final forecast conclusion with the average value of the collective arithmetic extensively, so this kind of method can hardly effectively show the differences among the forecast skills of different parameterization schemes. Aimed at this problem,the Qingshitan Reservoir located at the rainstorm center of Guangxi Zhuang Autonomous Region was selected in this paper as the research area and a comprehensive quantitative evaluation model based on the ETS grading was built, through sensitivity analysis on the WRF model and the 21 groups of parameterization schemes and the quantitative analysis for the number of samples and ensemble forecast skills,the establishment of ensemble forecast scheme of rainfall in Qingshitan Reservoir was realized based on the ETS evaluation results. Through verification, compared with the collective arithmetic average forecast, the ensemble forecast scheme constructed in this paper has higher skills and a more stable performance.
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