Eddy Hunter: A Data Mining System for High-Resolution Eddy Signals, Leveraging Spatio-Temporal Similarities in the SWOT Satellite Data

Abstract

The SWOT mission opened a new chapter in understanding ocean dynamics, yielding constantly growing amounts of high-resolution Sea Surface Height (SSH) observations. However, this extremely valuable data source is still not fully exploited by researchers, due to technical reasons and novelty. In particular, long orbit cycles and KaRIn altimeter’s limited spatial coverage impose challenges for eddy detection and tracking using SWOT. Conversely, traditional satellite altimeters served as the backbone of mesoscale research for over 30 years, providing wide spatio-temporal coverage. The integration of such satellites and SWOT could offer possible solutions to some of the aforementioned challenges. We aim to provide a system capable of overcoming the limitations of KaRIn altimetry data, leveraging the integration between SWOT and conventional SSH altimetry. Adopting an information retrieval approach, we identify and index high-resolution eddy signals similar to a low-resolution counterpart. To do so, the Eddy Hunter System (EHS) integrates the Mesoscale Eddy Trajectory Atlas (META) and SWOT Level-3 products, by leveraging the spatio-temporal similarities in the SSH signals. The EHS extracts a spatio-temporal Region Of Interest (ROI) from every eddy observation present in the META and computes the SWOT passages that could possibly contain related signals. The introduction of “Marine Data Science” topics in the similarity-search community can provide new problems and challenges regarding information retrieval and data mining. The deployment of our system could foster advancements both in oceanographic and applied ML/AI research, providing a powerful data mining tool to build high-resolution eddy repositories and unveil previously unseen ocean phenomena