海气通量（潜热通量）和SST 数据产品（1980-2020年）

本数据集由中科院海洋大科学研究中心共建单位中科院南海海洋研究所王鑫研究员和张荣望助理研究员牵头研制，主要基于卫星遥感和再分析数据，以中国近海浮标、海上平台和通量塔等观测数据为训练数据，采用卷积神经网络深度学习和改进后的COARE 3.5算法等技术研发，有效克服了中等大气比湿条件下的潜热通量低估和海表冷皮效应造成的潜热通量误差等突出问题。与国际主流海气通量产品对比，该产品在中国近海具有显著的区域优势和性能指标优势。

This dataset was developed under the leadership of Researcher Wang Xin and Assistant Researcher Zhang Rongwang from the South China Sea Institute of Oceanology (SCSIO), Chinese Academy of Sciences (CAS), a co-constructing unit of the Center for Ocean Mega-Science, CAS. It is primarily based on satellite remote sensing and reanalysis data, with observation data from buoys, offshore platforms, and flux towers in China's coastal waters as training data. Developed using technologies such as convolutional neural network deep learning and the improved COARE 3.5 algorithm, it effectively overcomes prominent issues including the underestimation of latent heat flux under moderate atmospheric specific humidity conditions and latent heat flux errors caused by the sea surface cool skin effect. Compared with international mainstream air-sea flux products, this product has significant regional advantages and performance indicator advantages in China's coastal waters.