摘要
风云三号D星(FY-3D)微波成像仪(MWRI)L1级亮温数据可用于全球海面风速反演,本文讨论了在晴空区和云区使用多元线性统计回归模型和机器学习模型反演海面风速的情况,在晴空区将4 d测试集分别放入多元线性统计回归模型,采用随机森林(RF),支持向量回归(SVR),卷积神经网络(CNN)和Stacking融合(SF)模型对海面风速进行反演,最优的均方根误差(RMSE)分别为1.56、1.31、1.24、1.29和1.27 m/s;在云区2 d测试集上的最优RMSE分别为2.12、1.98、1.87、1.89和1.89 m/s。为了进一步验证晴空区海面风速反演的可靠性,选取美国国家浮标数据中心(NDBC)实测的浮标风速对海面反演风速进行验证,CNN反演风速与NDBC实测风速的RMSE为0.74 m/s,决定系数(R^(2))为0.80;SF反演风速与NDBC实测风速的RMSE为0.85 m/s,R^(2)为0.74。结果证实了通过机器学习模型能够很好地完成FY-3D MWRI亮温反演全球海面风速的任务。
The L1-level bright temperature data of the microwave radiation imager(MWRI)in the Fengyun-3D(FY-3D)satellite can be used to retrieve the global sea surface wind speed.This paper discussed the use of multiple linear statistical regression model and machine learning models to retrieve the sea surface wind speed in clear sky and cloud areas.Four-day test sets are put into multiple linear statistical regression model,Random Forest(RF)model,Support Vector Regression(SVR)model,Convolutional Neural Network(CNN)model,and the Stacking Fusion(SF)model for the sea surface wind speed retrieval in the clear sky area,and the obtained optimal root mean square errors(RMSEs)are 1.56,1.31,1.24,1.29,and 1.27 m/s,respectively.Meanwhile,two-day test sets are put into multiple linear statistical regression model,RF model,SVR model,CNN model,and SF model for the sea surface wind speed retrieval in the cloud area and the obtained optimal RMSEs are 2.12,1.98,1.87,1.89 and 1.89 m/s,respectively.To further verify the reliability of the sea surface wind speed retrieval in the clear sky area,the buoy wind speed measured by the National Data Buoy Center(NDBC)in the United States is selected.The results show that the RMSE of the wind speed retrieved by the CNN model and the wind speed measured by the NDBC is 0.74 m/s,and the coefficient of determination(R-square,R^(2))is 0.80;the RMSE of the wind speed retrieved by the SF model and the wind speed measured by the NDBC is 0.85 m/s,and the R^(2) is 0.74.These results confirm that the machine learning models can effectively complete the brightness temperature retrieval tasks for global sea surface wind speed with the FY-3D MWRI.
作者
张云
韩天辉
孟婉婷
杨树瑚
周绍辉
韩彦岭
ZHANG Yun;HAN Tianhui;MENG Wanting;YANG Shuhu;ZHOU Shaohui;HAN Yanling(College of Information Technology,Shanghai Ocean University,Shanghai 201306,China;Shanghai Engineering Research Center of Marine Intelligent Information and Navigation Remote Sensing,Shanghai 201306,China;Shanghai Spaceflight Institute of TT&C and Telecommunication,Shanghai 201109,China;Shanghai Aerospace Space Technology Co.,Ltd.,Shanghai 201109,China)
出处
《上海航天（中英文）》
CSCD
2024年第4期120-132,172,共14页
Aerospace Shanghai（Chinese&English）
基金
国家自然科学基金资助项目(42271335,42176175)
国家重点研发计划资助项目(2019YFD0900805)。
