Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track...

Mesoscale eddies, which are fast-moving rotating water bodies in the ocean with horizontal scales ranging from 10 km to 100 km and above, are considered to be the weather of the oceans. They are of interest to marine biologists, oceanographers, and geodesists for their impact on water mass, heat, and nutrient transport. Typically, gridded sea level anomaly maps processed from multiple radar altimetry missions are used to detect eddies. However, multi-mission sea level anomaly maps obtained by the operational processors have a lower effective spatiotemporal resolution than their grid spacing and temporal resolution, leading to inaccurate eddy detection. In this study, we investigate the use of higher-resolution along-track sea level anomaly data to infer daily two-dimensional segmentation maps of cyclonic, anticyclonic, or non-eddy areas with greater accuracy than using processed sea level anomaly grid map products. To tackle this challenge, we propose a deep neural network that uses spatiotemporal contextual information within the modality of along-track data. This network is capable of producing a two-dimensional segmentation map from data with varying sparsity. We have developed an architecture called Teddy, which uses a Transformer module to encode and process spatiotemporal information, and a sparsity invariant CNN to infer a two-dimensional segmentation map of classified eddies from the ground tracks of varying sparsity on the considered region. Our results show that Teddy creates two-dimensional maps of classified eddies from along-track data with higher accuracy and timeliness when compared to commonly used methods that work with less accurate preprocessed sea level anomaly grid maps. We train and test our method with a carefully curated and independent dataset, which can be made available upon request.