Identification, tracking, and classification of ocean eddies in along track radar altimetry data using deep learning (EDDY)

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All large ocean currents generate eddies, i.e. cyclonically or anticyclonically rotating water masses. While monitoring individual eddies has applications in marine biology and fishery, knowing eddy statistics over larger regions and time periods is required for understanding water mixing and vertical heat transport in the ocean and, thus, a prerequisite for testing ocean models. At mesoscale, eddies are observed in radar altimetry, and methods have been developed to identify, track and classify them in gridded maps of sea surface height derived from multi-mission data sets. However, this procedure has drawbacks since much information is lost in the gridding process. Instead, here we suggest to develop a method that would identify, track, and classify eddies predominantly from along-track altimetry. Additionally, we will work with multiple modalities with complementary views on the phenomenon such as from sea surface temperature maps serving to guide the procedure, which departs from our recently published (preliminary) work. Our method will be based on convolutional neural networks with task-specific network architectures that jointly exploit spatial as well as temporal information in one task. It will be applied to conventional and SAR-altimetry and validated, e.g. with results from the SWOT mission. A comprehensive benchmark with multi-modal remote sensing observations and labeled reference data will be constructed and will be made available to the public.