Abstract The western tropical Atlantic Ocean is a very energetic and highly variable region. It is one of the main contributors to the inter-hemispheric mass and heat transports. This study aim is to give a new picture of the space and time variability of this region using statistical tools applied to five different satellite measurements (Sea Surface Temperature, Sea Surface Salinity, ocean topography, wind stress vectors). We first processed each data set by using a Self-Organizing Maps (SOM), which is an efficient clustering methodology based on non-linear artificial neural networks to compress the information embedded in the data. The SOM was then combined with a Hierarchical Ascendant Classification (HAC) to cluster the different phenomena in a small number of classes whose physical characteristics are easy to identify. Three classes were identified which allowed us to analyse the dynamics of the North Brazil Current, and the North Equatorial Countercurrent, respectively, and their links with the Inter-Tropical Convergence Zone and the Amazon and Orinoco river runoffs. The SOM + HAC analysis gave a coherent picture of the concomitant seasonal variability of the variables. Furthermore, we were able to point out the correlations existing between salinity features recently discovered and wind, temperature, and dynamic topography structures. Applying our method to the interannual signals, we showed a year to year variability which deserves further analysis.

中文翻译：

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通过结合机器学习和卫星数据进行热带大西洋动力学分析

摘要 热带大西洋西部是一个充满活力和高度变化的区域。它是半球间质量和热量传输的主要贡献者之一。这项研究的目的是使用应用于五种不同卫星测量（海面温度、海面盐度、海洋地形、风应力矢量）的统计工具，给出该地区空间和时间变化的新图景。我们首先使用自组织映射 (SOM) 处理每个数据集，这是一种基于非线性人工神经网络的高效聚类方法，可压缩嵌入数据中的信息。然后将 SOM 与分层上升分类 (HAC) 相结合，将不同的现象聚集在少数物理特征易于识别的类别中。确定了三个类别，使我们能够分别分析北巴西流和北赤道逆流的动态，以及它们与热带间辐合带和亚马逊河和奥里诺科河径流的联系。SOM + HAC 分析给出了伴随的变量季节性变化的连贯图片。此外，我们能够指出最近发现的盐度特征与风、温度和动态地形结构之间存在的相关性。将我们的方法应用于年际信号，我们显示了每年的变化，值得进一步分析。以及它们与热带间辐合带以及亚马逊河和奥里诺科河径流的联系。SOM + HAC 分析给出了伴随的变量季节性变化的连贯图片。此外，我们能够指出最近发现的盐度特征与风、温度和动态地形结构之间存在的相关性。将我们的方法应用于年际信号，我们显示了每年的变化，值得进一步分析。以及它们与热带间辐合带以及亚马逊河和奥里诺科河径流的联系。SOM + HAC 分析给出了伴随的变量季节性变化的连贯图片。此外，我们能够指出最近发现的盐度特征与风、温度和动态地形结构之间存在的相关性。将我们的方法应用于年际信号，我们显示了每年的变化，值得进一步分析。