基于小波分析的客观预报方法在智能网格高低温预报中的应用  

Application of objective prediction method based on wavelet analysis in intelligent grid high and low temperature prediction

作　　者：刘新伟[1] 段伯隆 黄武斌[1] 段明铿[2] 李蓉 狄潇泓[1] 魏素娟 LIU Xinwei;DUAN Bolong;HUANG Wubin;DUAN Mingkeng;LI Rong;DI Xiaohong;WEI Sujuan(Lanzhou Central Meteorological Observatory,Lanzhou 730020,China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC),Nanjing University of Information Science&Technology,Nanjing 210044,China;Gansu Academy of Eco-environmental Sciences,Lanzhou 730020,China)

机构地区：[1]兰州中心气象台,甘肃兰州730020 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室,江苏南京210044 [3]甘肃省生态环境科学设计研究院,甘肃兰州730020

出　　处：《大气科学学报》2020年第3期577-584,共8页Transactions of Atmospheric Sciences

基　　金：国家重点研发计划项目(2017YFC1502002);中国气象局预报员专项项目(CMAYBY2019-122);甘肃对流性暴雨预报预警关键技术创新团队(GSQXCXTD-2020-01);甘肃省气象局十人计划(GSMArc2019-04)。

摘　　要：基于2017—2018年中国气象局高分辨率数值预报产品、甘肃实时城镇预报产品和国家级地面观测站数据,利用小波分析、滑动训练、最优融合等技术,研发出甘肃省智能网格高低温客观订正产品。检验分析表明:城镇预报产品、滑动训练订正产品、最优融合产品3种订正产品对CMA预报均有订正能力,3种客观订正产品的最高气温订正能力强于最低气温订正能力;滑动训练法与最优融合法产生的高低温订正产品,在系统误差明显地区(甘南、陇南等)的预报结果要好于模式客观预报,而高低温城镇预报产品在气温局地性强或者模式客观预报能力差的区域有优势;最优融合预报方法生成的高低温产品预报能力略高于滑动训练订正产品且与现有预报员制作城镇预报产品基本持平,初步具备了替代主观预报的能力。Based on the 2017—2018 high-resolution numerical forecast products of China Meteorological Administration (CMA),real-time urban forecast products of Gansu Province and data of national ground-based observation stations,the intelligent grid high and low temperature objective correction products of Gansu Province are developed by using wavelet analysis,sliding training,optimal fusion and other technologies.The test results show that the three correction products (urban forecast products,sliding training correction products and optimal fusion products) have the ability to correct CMA forecast,and the maximum temperature correction ability of the three objective correction products is stronger than the minimum temperature correction ability.The prediction results of the high and low temperature correction products produced by the sliding training method and the optimal fusion method are better than those of the model objective prediction in the areas with obvious systematic errors (Gannan,Longnan,etc.),while the high and low temperature urban prediction products have advantages in the areas with strong temperature localization or poor model objective prediction ability.The prediction ability of the high and low temperature products generated by the optimal fusion prediction method is slightly higher than that of the sliding training correction products,and is basically the same as that of the urban prediction products produced by the existing forecasters,which initially has the ability to replace the subjective prediction.

关 键 词：智能网格 小波分析 温度订正 

分 类 号：P457.3[天文地球—大气科学及气象学]