Eddy identification from along-track altimeter data with multi-modal deep learning

Eddies are circular rotating water masses, which are usually generated near the large ocean currents, e.g., Gulf Stream. Monitoring eddies and gaining knowledge on eddy statistics over a large region are important for fishery, marine biology studies, and testing ocean models.

At mesoscale, eddies are observed in radar altimetry, and methods have been developed to identify, track and classify them in gridded maps of sea surface height derived from multi-mission data sets. However, this procedure has drawbacks since much information is lost in the gridded maps. Inevitably, the spatial and temporal resolution of the original altimetry data degrades during the gridding process. On the other hand, the task of identifying eddies has been a post-analysis process on the gridded dataset, which is, by far, not meaningful for near-real time applications or forecasts. In the EDDY project at the University of Bonn, we aim to develop methods for identifying eddies directly from along track altimetry data via a machine (deep) learning approach.

Since eddy signatures (eddy boundary and highs and lows on sea level anomaly, SLA) are not possible to extract directly from along track altimetry data, the gridded altimetry maps from AVISO are used to detect eddies. These will serve as the reference data for Machine Learning. The eddy detection on 2D grid maps is produced by open-source geometry-based approach (e.g., py-eddy-tracker, Mason et al., 2014) with additional constraints like Okubo-Weiss parameter. Later, Sea Surface Temperature (SST) maps of the same region and date (also available from AVISO) are used for manually cleaning the reference data. Noting that altimetry grid maps and SST maps have different temporal and spatial resolution, we also use the high resolution (~6 km) ocean modeling simulation dataset (e.g., FESOM, Finite Element Sea ice Ocean Model). In this case, the FESOM dataset provides a coherent, high-resolution SLA and SST, salinity maps for the study area and is a potential test basis to develop the deep learning network.

The single modal training via a Conventional Neural Network (CNN) for the 2D altimetry grid maps produced excellent dice score of 86%, meaning the network almost detects all eddies in the Gulf Stream, which are consistent with reference data. For the multi-modal training, two different training networks are developed for 1D along-track altimetry data and 2D grid maps from SLA and SST, respectively, and then they are combined to give the final classification output. A transformer model is deemed to be efficient for encoding the spatiotemporal information from 1D along track altimetry data, while CNN is sufficient for 2D grid maps from multi-sensors.

In this presentation, we show the eddy classification results from the multi-modal deep learning approach based on along track and gridded multi-source datasets for the Gulf stream area for the period between 2017 and 2019. Results show that multi-modal deep learning improve the classification by more than 20% compared to transformer model training on along-track data alone.