多元信息耦合的致灾山洪降雨预报方法
雷达预估信息(0~2 h)、数值天气预报产品(0~72 h)及基于高空和地面大气探测资料的综合预报信息(0~24 h)在不同预见期的降雨预报各有差异,预报效果上各有优劣,而针对山洪的预报存在准确率低、预警有效时间短等问题,研究面向山洪灾害防治区的数值预报模式的选取、模式最优物理参数化组合方案、同化方案、降水偏差订正技术以及基于大数据的自分型雷达估测降水最优化算法。以湖南临湘市为试验区进行验证,结果表明:WRF中尺度数值模式适用于山洪灾害降雨预报,其WSM6云微物理过程、Grell--Devenyi ensemble对流参数化方案和YSU边界层参数方案对致洪山洪暴雨过程模拟较好,基于频率(或面积...
Saved in:
Published in | 水资源研究 Vol. 6; no. 2; pp. 91 - 102 |
---|---|
Main Author | |
Format | Journal Article |
Language | Chinese |
Published |
2017
|
Subjects | |
Online Access | Get full text |
ISSN | 2166-6024 2166-5982 |
Cover
Summary: | 雷达预估信息(0~2 h)、数值天气预报产品(0~72 h)及基于高空和地面大气探测资料的综合预报信息(0~24 h)在不同预见期的降雨预报各有差异,预报效果上各有优劣,而针对山洪的预报存在准确率低、预警有效时间短等问题,研究面向山洪灾害防治区的数值预报模式的选取、模式最优物理参数化组合方案、同化方案、降水偏差订正技术以及基于大数据的自分型雷达估测降水最优化算法。以湖南临湘市为试验区进行验证,结果表明:WRF中尺度数值模式适用于山洪灾害降雨预报,其WSM6云微物理过程、Grell--Devenyi ensemble对流参数化方案和YSU边界层参数方案对致洪山洪暴雨过程模拟较好,基于频率(或面积)匹配的降水偏差订正方法能显著改善模式降水预报中雨量和雨区范围的系统性偏差,大数据分析方法应用于雷达估测降雨能显著提高准确度。在此研究基础上,提出了基于高空和地面大气探测、数值预报模式的0~72 h短期定量降雨预报和基于雷达的0~2h临近定量降雨预报的多元信息耦合预报方法,将对致灾山洪的预报时间由2 h延长至72 h,并可显著提高了山洪预报的精度。 |
---|---|
Bibliography: | Disastrous Flash Flooding, Radar, Numerical Weather Forecasting, Coupling Ming Xiong1, Wenfa Yang1,Jun Li2, Beiping Zhou3, Li Zi1( 1Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan Hubei ;2Wuhan Institute of Stormrain Research, China Meteorological Administration, Wuhan Hube; 3Nanjing University of Information Engineering, Nanjing Jiangsu) The precipitation forecasts from radar and satellite cloud pictures (with a lead time of 0 - 2 h), from upper air and ground surface atmospheric sounding (with a lead time of 0 - 24 h) and from numerical forecasting mode (with a lead time of 0 - 72 h) have different forecast effect in different forecast pe-riods. Considering the short lead time and low accuracy forecast for early warning of mountain tor-rents, study on the numerical forecasting model, the model’s optimized combination of parameters, data assimilation, correcting the errors on forecasted rainfall in flash-flood affected region and an op-timized calculating method based on big data for radar mo |
ISSN: | 2166-6024 2166-5982 |