基于深度学习的FY-4A/AGRI海表温度反演方法  

FY-4A/AGRI Sea Surface Temperature Retrieval Method Based on Deep Learning

作　　者：周丽娜 崔鹏 孙安来 梁永楼 张迺强 ZHOU Lina;CUI Peng;SUN Anlai;LIANG Yonglou;ZHANG Naiqiang(Beijing Huayun Shinetek Science and Technology Co.,Ltd,Beijing 100081;National Satellite Meteorological Center/National Center for Space Weather,Beijing 100081;Innovation Center for Fengyun Meteorological Satellite,Beijing 100081;Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,China Meteorological Administration,Beijing 100081)

机构地区：[1]北京华云星地通科技有限公司,北京100081 [2]国家卫星气象中心/国家空间天气监测预警中心,北京100081 [3]许健民气象卫星创新中心,北京100081 [4]中国气象局中国遥感卫星辐射测量和定标重点开放实验室,北京100081

出　　处：《气象科技》2025年第2期153-166,共14页Meteorological Science and Technology

摘　　要：海表温度是气候和天气研究中的一个重要变量。本文提出了一种基于深度学习的FY-4A/AGRI(Advanced Geostationary Radiation Imager)海表温度反演方法,旨在提高海表温度的反演精度,并为气象研究提供更为精确的数据支持。该方法利用FY-4A/AGRI卫星数据、背景场海表温度和现场实测海表温度构建反演数据集;通过非线性海表温度算法进行特征选择,并采用所选特征数据建立一个基于深度神经网络的海温反演模型;最后,利用该模型将卫星数据反演生成海表温度产品。本文以现场实测海表温度为基准,从产品精度和长时间序列稳定性两个维度对海温产品进行评价。结果表明:本研究反演的海温产品的平均偏差为-0.19℃,均方根误差为0.67℃,相关系数达到0.992,精度比FY-4A/AGRI业务海温产品有所提高。Sea surface temperature(SST)is an important parameter for ocean and atmospheric forecasting systems and climate change research.The National Satellite Meteorological Centre(NSMC)develops the Fengyun-4A(FY-4A)/AGRI(advanced geostationary radiation imager)SST products using the split-window nonlinear SST(NLSST)algorithm.However,the traditional regression algorithm is difficult to meet the needs of higher accuracy SST retrieval.To solve this problem,this paper proposes a FY-4A/AGRI sea surface temperature retrieval method based on deep learning,aiming to improve the retrieval accuracy of SST and provide more accurate data support for meteorological research.FY-4A/AGRI satellite data,SST climatology data,and in situ SST observations are used to construct the retrieval dataset according to quality control standards and spatio-temporal matching rules.The NLSST algorithm is used to select features,including 10.7μm band brightness temperature,12μm band brightness temperature,satellite zenith angle,and SST climatology data.According to the ratio of 8∶2,the feature data are divided into a training dataset and a validation dataset,which are used for training and validation respectively.A SST retrieval model based on a deep neural network is obtained through experiments.Finally,the FY-4A/AGRI satellite data are retrieved by the DNN model to generate SST products.The model-retrieved SST products are evaluated from two dimensions of accuracy and long-term series stability based on in situ SST,and also compared with the FY-4A/AGRI official SST products.By applying the quality levels of FY-4A/AGRI official SST products to the model-retrieved SST products,the performance of model-retrieved SST products under different quality levels(excellent,good,and bad)in three periods of day,night,and dawn is analysed.The statistical results show that when the quality level is excellent,the mean bias of the model-retrieved SST products is-0.19℃,the root mean square error(RMSE)is 0.67℃,and the correlation coefficient reaches 0.992.Howe

关 键 词：海表温度反演 深度学习 FY-4A/AGRI 深度神经网络 误差评估 

分 类 号：P405[天文地球—大气科学及气象学] P423.7