Intelligent inversion of mesoscale eddy temperature anomaly profiles based on multi-source remote sensing data

Inversion of the underwater temperature anomaly of mesoscale eddies from sea surface remote sensing data plays an important role in understanding the three-dimensional structure of eddies. In this article, a neural network structure, named the eddy temperature anomaly inversion network (EddyTAINet), is proposed and established to invert the vertical temperature anomalies of eddies using 20-year (2002–2022) Argo profiles and multi-source remote sensing data in the northwestern Pacific Ocean. Vertical temperature anomaly profiles with 45 depth levels ranging from 10 to 1000 m are derived as the outputs of the inversion networks. The input feature sequences are extracted from the mesoscale eddy product based on sea level anomaly (SLA) data, and sea surface temperature (SST) data are also introduced into the proposed network structure to improve its accuracy. For the problem of inconsistent sizes of the 2D matrix SST data corresponding to mesoscale eddies, the spatial pyramid pooling (SPP) network module is introduced, which allows different sizes of SST data to be simply convolved and then subjected to hierarchical feature extraction to obtain fixed-size feature vectors. Validation and comparison of the network structure are performed and show the improved performance of EddyTAINet. The average root mean square error (RMSE) and mean absolute error (MAE) are 0.63 °C/0.66 °C and 0.48 °C/0.51 °C for cyclonic and anticyclonic eddies, respectively, which are more than 20% lower than the results of the network using the SLA-based data. •A mesoscale eddy temperature anomaly inversion neural network is proposed.•Multi-source data, including feature sequences and 2D SST images, are supported.•Size-variant SST data corresponding to mesoscale eddies can be adaptively adopted.•Compared to the MLP-based network, the metrics improved significantly.