Upper‐ocean stratification strongly impacts vertical mixing and the heat flux between the ocean and atmosphere, especially under extreme conditions of tropical cyclones (TCs). Knowledge of prestorm stratification is important for accurate TC intensity prediction. In situ observations of the tropical ocean have significantly increased in the past decade. However, they are still too sparse to resolve ocean stratification variability in near‐real time and on small spatial scales. In this study, based on long‐term observations and an ocean reanalysis data set from 2004–2017, we investigate the possibility of retrieving upper‐ocean stratification from sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) using a simple regression method. It is found that more than 90% of the mean seasonal cycle and about 30% to 80% of temperature and salinity stratification anomalies can be reconstructed using surface data from either observations or an ocean reanalysis. Simple regression can be used with satellite observations to create a high‐resolution, near‐real‐time‐gridded ocean stratification data set that successfully reproduces both the large and mesoscale variability of ocean stratification. When used in a simple expression for TC‐induced SST cooling, the satellite‐derived stratification shows improvements over an ocean analysis in terms of variance explained of SST cooling, offering promise as a near‐real‐time indicator of the ocean's impact on TC intensification.

中文翻译：

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卫星得出的北大西洋热带上层分层数据集及其在飓风强度预测中的潜在应用

上层海洋分层强烈影响垂直混合和海洋与大气之间的热通量，特别是在热带气旋（TC）的极端条件下。暴风前分层的知识对于准确的TC强度预测很重要。在过去十年中，对热带海洋的实地观测大大增加了。但是，它们仍然太稀疏，无法在近实时和小空间尺度上解决海洋分层变化。在这项研究中，基于长期观察和2004-2017年的海洋再分析数据集，我们研究了从海面温度（SST），海面盐度（SSS）和海面高度检索上层海洋分层的可能性（SSH）使用简单的回归方法。已经发现，可以使用来自观测或海洋再分析的地面数据重建超过90％的平均季节性周期以及约30％至80％的温度和盐分分层异常。简单回归可以与卫星观测一起使用，以创建高分辨率，近实时网格化的海洋分层数据集，该数据集成功地再现了海洋分层的大尺度和中尺度变化。当以简单的表达方式用于TC引起的SST冷却时，卫星衍生的分层显示出海洋分析在SST冷却解释的方差方面的改进，从而有望作为海洋对TC强化影响的近实时指标。 。