融合多源观测资料的微波辐射计温度廓线订正试验  

Experiments on Microwave Radiometer Temperature Profile Correction by Integrating Multi-Source Observational Data

作　　者：单乃超 周后福[2,3] 郦敏杰 王琛 严文莲 SHAN Naichao;ZHOU Houfu;LI Minjie;WANG Chen;YAN Wenlian(Anhui Branch,Air Traffic Management Bureau of Civil Aviation Administration of China,Hefei 230051,China;Anhui Institute of Meteorological Sciences,Hefei 230031,China;Anhui Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing,Hefei 230031,China;Hangzhou Meteorological Bureau,Hangzhou 310051,China;Jiangsu Meteorological Observatory,Nanjing 210008,China)

机构地区：[1]民航安徽空管分局,安徽合肥230051 [2]安徽省气象科学研究所,安徽合肥230031 [3]大气科学与卫星遥感安徽省重点实验室,安徽合肥230031 [4]杭州市气象局,浙江杭州310051 [5]江苏省气象台,江苏南京210008

出　　处：《热带气象学报》2024年第4期586-598,共13页Journal of Tropical Meteorology

基　　金：华东区域气象创新基金项目(QYHZ20210);安徽省重点研发计划(2022m07020003)共同资助。

摘　　要：为增加大气探空站点,提高微波辐射计大气温度探测精度,利用FY-4A气象卫星温度产品和BP神经网络、遗传算法,分别实施杭州站、南京站微波辐射计的温度订正仿真试验,并获得准确的连续性大气温度垂直廓线;结合探空资料和民航AMDAR气温资料,评估模型订正效果。研究结果表明:(1)微波辐射计温度产品存在一定误差,两站均在高度2 km处平均偏差最大,同站有雨时的偏差均大于无雨时的偏差;(2)经过BP神经网络模拟订正后的微波辐射计测温精度较原温度产品提升幅度较大;杭州站MAE、MSE、RMSE的降低幅度分别为45%~55%、65%~78%、41%~53%,南京站的降低幅度分别为58%~66%、83%~88%、55%~59%;(3)经过遗传算法优化初始权值和阈值后的神经网络订正模型模拟效果有进一步的提升,其中有雨模型提升效果明显,RMSE降低幅度11%~15%。微波辐射计的上述订正方法,可以推广到各地微波辐射计站点应用,具有实际使用价值。In the present study,a correction method was developed to improve the accuracy of groundbased microwave radiometers in measuring temperatures.Using temperature products from the FY-4A meteorological satellite,a back propagation(BP)neural network,and a genetic algorithm,we conducted temperature correction simulation experiments to correct the temperature profiles measured by two MP-3000 ground-based microwave radiometers located at the meteorological stations in Hangzhou and Nanjing,respectively,and obtained accurate and continuous vertical profiles of atmospheric temperature.The corrected temperature profiles were then compared with temperature data from radiosonde measurements and the Aircraft Meteorological Data Relay(AMDAR)data from the Civil Aviation Administration of China.The results show that:(1)Microwave radiometer temperature products exhibited inherent inaccuracies,with larger discrepancies during rainy conditions and the greatest average deviation observed at the altitude of 2 km for both stations.(2)The temperature measured by the microwave radiometer,after being corrected through BP neural network simulation,was a significant enhancement compared to the original temperature.At Hangzhou station,the reductions in mean absolute error,mean squared error,and root mean square error(RMSE)were observed in the ranges of 45%~55%,65%~78%,and 41%~53%,respectively,while at Nanjing station,these metrics decreased by 58%~66%,83%~88%,and 55%~59%respectively.(3)The simulation model of the neural network,after its initial weights and thresholds were optimized using a genetic algorithm,demonstrated further improvements.There was a significant enhancement in the rain model,with RMSE reductions of 11%-15%.The proposed correction method for microwave radiometers seems to be suitable for broader applications across microwave radiometer stations.

关 键 词：微波辐射计 FY-4A卫星 AMDAR BP神经网络 遗传算法 廓线订正 

分 类 号：P413[天文地球—大气科学及气象学]