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粤港澳大湾区汛期降水的多模式集成预报方法的评估检验
朱鹏程1,2,3,4, 王东海1,2,3, 曾智琳1,2,3, 孙磊4, 李智丽4, 资桂4
1.中山大学大气科学学院/广东省气候变化与自然灾害研究重点实验室/热带海洋系统科学教育部重点实验室,广东珠海519082;2.南方海洋科学与工程广东省实验室(珠海),广东珠海519082;3.澳门海岸带生态环境国家野外科学观测研究站/澳门科技大学澳门环境研究院,澳门999078;4.珠海市公共气象服务中心,广东珠海519000
摘要:
基于欧洲中期天气预报中心(ECMWF)、中国气象局(CMA)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、英国气象局(UKMO)五个模式集成的交互式全球大集合预报系统(THORPEXInteractiveGrandGlobalEnsemble,简称TIGGE)资料集的确定性预报、集合预报以及地面降水观测数据,采用多模式集成平均(EMN)、消除偏差集成平均(BREM)、滑动训练期超级集合方法(R_SUP)对2018年华南汛期(4—9月)粤港澳大湾区的降水预报开展了评估检验。总体而言,多模式集成预报方法在大湾区前汛期降水预报的均方根误差平均比后汛期高2mm;多模式集成预报方法的预报能力在前汛期随着预报时效的延长而呈持续下降趋势,后汛期则表现为短期(24~72h)下降、中期(72~168h)持续平稳的变化特点。与预先的假设差异主要表现在:对前、后汛期的降水预报综合表现最好的均是数学原理相对简单的EMN,而BREM和R_SUP的空间平均评分指标则稍差,但其在降水落区预报中仍有较好的预报技巧。
关键词:  粤港澳大湾区  多模式集成预报  评估检验
DOI:10.16032/j.issn.1004-4965.2024.025
分类号:
基金项目:
Evaluation of Multi-model Integrated Forecast Method for Precipitation in Rainy Seasons of Guangdong-Hong Kong-Macao Greater Bay Area
ZHU Pengcheng1,2,3,4, WANG Donghai1,2,3, ZENG Zhilin1,2,3, SUN Lei4, LI Zhili4, ZI Guirong4
1.School of Atmospheric Sciences ,Sun Yat-sen University/Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies/Key Laboratory of Tropical Atmosphere-Ocean System ,Zhuhai ,Guangdong 519082 ,China;2.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai ),Zhuhai ,Guangdong 519082 ,China;3.National Observation and Research Station of Coastal Ecological Environments in Macao ,Macao Environmental Research Institute ,Macau University of Science and Technology ,Macao SAR 999078 ,China;4.Zhuhai Public Meteorological Service Center ,Zhuhai ,Guangdong 519000 ,China
Abstract:
This study evaluated the precipitation forecasts in the Guangdong-Hong Kong-Macao Greater Bay Area during the 2018 South China rainy seasons (April to September )using data from the THORPEX Interactive Grand Global Ensemble dataset .The dataset comprised deterministic forecasts ,ensemble forecasts ,and ground precipitation observations from models of five organizations :the European Centre for Medium-Range Weather Forecasts ,the China Meteorological Administration ,the Japan Meteorological Agency ,the National Centers for Environmental Prediction of the United States ,and the UK Meteorological Office .Three multi-model integrated forecast methods ,namely the multi-model ensemble average (EMN ),the bias-removed ensemble average (BREM ),and the sliding training period superensemble method (R_SUP ),were employed for the evaluation .The results showed that ,in general , the root mean square error of precipitation forecasts in the first rainy season of the Guangdong-Hong Kong- Macao Greater Bay Area was higher than that in the second rainy season ,with an average difference of 2 mm.The forecasting ability of the multi-mode integrated forecasting method showed a continuous and stable decline trend in the first rainy season as the forecast lead time increased .In contrast ,in the second rainy season ,it showed a stable decline in the short term (24~72hours )and remained stable in the medium term (72~168hours ).EMN ,which has a relatively simple mathematical principle ,showed the best comprehensive performance in the precipitation forecast of the two rainy seasons .BREM and R_SUP achieved slightly lower spatial average scores ,but they still demonstrated good forecasting skills in predicting precipitation areas .
Key words:  Guangdong-Hong Kong-Macao Greater Bay Area  multi-model integrated forecast  evaluation
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