A Novel Mesoscale Eddy Identification Method Using Enhanced Interpolation and A Posteriori Guidance.

Mesoscale eddies are pivotal oceanographic phenomena affecting marine environments. Accurate and stable identification of these eddies is essential for advancing research on their dynamics and effects. Current methods primarily focus on identifying Cyclonic and Anticyclonic eddies (CE, AE), with anomalous eddy identification often requiring secondary analyses of sea surface height anomalies and eddy center properties, leading to segmented data interpretations. This study introduces a deep learning model integrating multi-source fusion data with a Squeeze-and-Excitation (SE) attention mechanism to enhance the identification accuracy for both normal and anomalous eddies. Comparative ablation experiments validate the model's effectiveness, demonstrating its potential for more nuanced, multi-source, and multi-class mesoscale eddy identification. This approach offers a promising framework for advancing mesoscale eddy identification through deep learning.