An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer

Abstract

Oceanic mesoscale eddies are relatively small, short-lived circulation patterns that are approximately in geostrophic balance. Meanwhile, eddies are omnipresent and can be characterized by dynamic sea level anomalies and temperature anomalies. This makes the eddy identification mainstream with Sea Level Anomaly (SLA). Unfortunately, nearly 90% of sea level dynamic anomalies caused by oceanic eddies cannot be observed due to insufficient resolution of satellite altimeters. Combining in-situ Expendable Bathythermograph (XBT) profiles data, and sea surface temperature data calibrated by the altimeter, this paper proposes a deep neural network to identify subsurface oceanic eddies and inverse the corresponding sea surface eddy properties. First, the eddies identified by SLA are purified to match the corresponding vertical profile dataset. Then, a neural network with a self-attention mechanism is constructed by combining the eddy vertical profile structure with temporal and spatial characteristics and external features to effectively identify the eddy. Furthermore, the eddy properties including radius, amplitude, and energy are inversion with XBT profile and SST features. Finally, the experimental results show that the accuracy of eddy classification can reach 98.22%, which demonstrates that vertical profiles can be used to classify eddies effectively. Subsequent reclassification of the outside altimeter-identified eddies recaptured about 36% of the eddies. The authenticity of the newly identified eddies can be demonstrated by statistical model validation as well as validation of sea surface temperature anomaly (SSTA). These results indicate that the subsurface eddies identification can be implemented by vertical profiles with deep learning.