Alireza Sharifi; Alireza Vafaeinejad
Abstract
Abstract— Sea Surface Temperature (SST) data reveal the temporal and spatial distribution of warm anticyclonic eddies and cold cyclonic eddies, impacting ocean behavior. The SST ...
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Abstract— Sea Surface Temperature (SST) data reveal the temporal and spatial distribution of warm anticyclonic eddies and cold cyclonic eddies, impacting ocean behavior. The SST combined products attained adequate decisions to permit the recognition of mesoscale eddies with the introduction of altimeter operations and the availability of two or more altimeters at the same time. Climate change impacts ocean circulation and atmospheric anomalies linked to SST variations. Ocean eddies, vital for material and energy transport, require precise identification to advance oceanography. This study uses SST data from CMEMS in the Atlantic to introduce EddyNet, a deep-learning model for automatic eddy detection and classification. EddyNet's encoder-decoder architecture includes a pixel-wise classification layer, labeling each pixel as "0" (non-eddy), "1" (anticyclonic), or "2" (cyclonic). The high-resolution feature representation outperforms existing models, marking a significant leap in eddy detection accuracy and reliability. This study introduces EddyNet, a deep-learning model based on the U-Net architecture for automatic eddy detection and classification using Sea Surface Temperature (SST) data. The model was trained and evaluated on satellite imagery from the Copernicus Marine Environment Monitoring Service (CMEMS), achieving a training accuracy of 78.55%, a Dice score of 31.99%, and a precision of 0.9259. The recall values for different classes indicate that the model correctly identifies 99.51% of non-eddy pixels, 51.57% of anticyclonic eddy pixels, and 57.19% of cyclonic eddy pixels. These results demonstrate the effectiveness of deep learning in mesoscale eddy detection and highlight the potential for further optimization in classifying eddy structures with higher precision.
