Accurate estimation of satellite-derived ocean latent heat flux (LHF) at high spatial resolution remains a major challenge. Here, we estimate monthly ocean LHF at 4 km spatial resolution over 5 years using bulk algorithm COARE 3.0, driven by satellite data and meteorological variables from reanalysis. We validated the estimated ocean LHF by multiyear observations and by comparison with seven ocean LHF products. Validation results from monthly observations at 96 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA, and RAMA) indicated a bias of less than 7 W/m² with R² of more than 0.80 () and with a King–Gupta efficiency (KGE) of over 0.84. Our estimated ocean LHF also performs well in simulating annual variability and predicting between-site variability, as indicated by a bias of lower than 6 W/m² and an R² of more than 0.84 (). Overall, the average KGE for estimated ocean LHF increased by 18%–23% compared to other LHF products, indicating robust LHF estimation performance. Importantly, our estimated annual ocean LHF has similar global spatial distribution compared to other LHF products, although there are general differences in LHF values due to the difference in the models and the spatial resolution.

中文翻译：

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根据MODIS SST产品和AMSR-E数据估算高分辨率的全球每月海洋潜热通量

在高空间分辨率下准确估算源自卫星的海洋潜热通量（LHF）仍然是一项重大挑战。在这里，我们使用批量算法COARE 3.0，根据卫星数据和重新分析的气象变量，估计5年内4 km空间分辨率下的每月海洋LHF。我们通过多年观察并与七个海洋LHF产品进行比较，验证了估计的海洋LHF。对来自三个浮标位阵列（TAO，PIRATA和RAMA）的96个分布广泛的浮标位进行月度观测的验证结果表明，偏差小于7 W / m ²，R ²大于0.80（），King-Gupta效率（KGE）超过0.84。我们估计的海洋LHF在模拟年度变化和预测站点间变化方面也表现良好，如偏差小于6 W / m ²且R ²大于0.84（）。总体而言，与其他LHF产品相比，估计的海洋LHF的平均KGE增加了18％–23％，表明LHF估计性能强劲。重要的是，尽管由于模型和空间分辨率的差异，LHF值存在一般差异，但与其他LHF产品相比，我们估计的年度海洋LHF具有相似的全球空间分布。