沙漠与绿洲气象2025，Vol.19Issue(2)：55-63,9.DOI:10.12057/j.issn.1002-0799.2307.24001
葵花八号红外辐射率资料同化在一次东北暴雨中的应用研究
Application of Assimilating Infrared Radiance Data from Himawari-8 Satellite in a Heavy Rainfall in Northeast China
摘要
Abstract
Based on the assimilation module for Himawari-8 AHI radiance data in the weather research and forecasting model data assimilation(WRFDA)system with WRF model,this paper investigated the impact of direct assimilation of the radiance data of the three water vapor channels from Himawari-8 AHI on the forecast of a heavy rainfall in Northeast China.The results show that the quality control and bias correction are effective in eliminating anomalous data,making the mean of observation minus background(OMB)closer to 0,while the standard deviation and root mean square error of observation minus analysis(OMA)are further reduced.Compared with the control experiment,after assimilating AHI radiance data,the root mean square errors are reduced by about 0.2 m/s and 0.1 K in the middle and lower layers for the wind and temperature respectively,and the root mean square error of specific humidity decreases by about 0.03 g/kg in the lower layers.Besides,the assimilation of satellite data contributes positively to the increment of relative humidity in the central part of Jilin Province.The final forecast of 3 h precipitation is improved.The experiments involving satellite data assimilation show an improvement in the ETS and bias scores exceeding 0.1 of all thresholds.For the 50 mm threshold,the improvement rates for ETS and bias scores reach 64%and 58%,respectively.In addition,the assimilation of AHI radiation data enhances the alignment of the hourly precipitation forecast trends at a single observation station with the actual observations.关键词
东北暴雨/WRF模式/AHI辐射率/资料同化Key words
heavy rainfall in Northeast China/WRF model/AHI radiance/data assimilation分类
大气科学引用本文复制引用
王国羽,许冬梅,晋明红,黄帅,郭新春..葵花八号红外辐射率资料同化在一次东北暴雨中的应用研究[J].沙漠与绿洲气象,2025,19(2):55-63,9.基金项目
中国电力工程顾问集团西南电力设计院有限公司科研项目(KQ0807) （KQ0807）
福建省灾害天气重点实验室/中国气象局海峡灾害天气重点开放实验室开放课题(2023KFKT03) （2023KFKT03）
