Machine learning-based identification and classification of ocean eddies

The automatic detection and tracking of mesoscale ocean eddies, the ‘weather of the ocean’, is a well-known task in oceanography. These eddies have horizontal scales from 10 km up to 100 km and above. They transport water mass, heat, nutrition, and carbon and have been identified as hot spots of biological activity. Monitoring eddies is therefore of interest among others to marine biologists and fishery. 
Recent advances in satellite-based observation for oceanography such as sea surface height (SSH) and sea surface temperature (SST) result in a large supply of different data products in which eddies are visible. In radar altimetry observations are acquired with repeat cycles between 10 and 35 days and cross-track spacing of a few 10 km to a few 100 km. Therefore, ocean eddies are clearly visible but typically covered by only one ground track. In addition, due to their motion, eddies are difficult to reconstruct, which makes creating detailed maps of the ocean with a high temporal resolution a challenge. In general, they are considered a perturbation, and their influence on altimetry data is difficult to determine, which is especially limiting for the determination of an accurate time-averaged dynamic topography of the ocean.
Due to their spatio-temporal dynamic behavior the identification and tracking are challenging. There is a number of methods that have been developed to identify and track eddies in gridded maps of sea surface height derived from multi-mission data sets. However, these procedures have shortcomings since the gridding process removes information that is valuable in achieving more accurate results.
Therefore, in the project EDDY carried out at the University of Bonn we intend to use ground track data from satellite altimetry and - as a long-term goal - additional remote sensing data such as SST, optical imagery, as well as statistical information from model outputs. The combination of the data will serve as a basis for a multi-modal deep learning algorithm. In detail, we will utilize transformers, a deep neural network architecture, that originates from the field of Natural Language Processing (NLP) and became popular in recent years in the field of computer vision. This method shows promising results in terms of understanding temporal and spatial information, which is essential in detecting and tracking highly dynamic eddies.
In this presentation, we introduce the deep neural network used in the EDDY project and show the results based on gridded data sets for the Gulf stream area for the period 2017 and first results of single-track eddy identification in the region.