Period
01.04.2021 – 31.03.2024
Funding Agency
DFG (Deutsche Forschungsgemeinschaft)
Contacts
Prof. Dr.-Ing. Jürgen Kusche
PD Dr.-Ing.-habil. Luciana Fenoglio-Marc
Prof. Dr.-Ing. Ribana Roscher
Dr. Adili Abulaitijiang
MSc Eike Bolmer
Summary
All large ocean currents generate eddies, i.e. cyclonically or anticyclonically rotating water masses. While monitoring individual eddies has applications in marine biology and fishery, knowing eddy statistics over larger regions and time periods is required for understanding water mixing and vertical heat transport in the ocean and, thus, a prerequisite for 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 gridding process. Instead, here we suggest to develop a method that would identify, track, and classify eddies predominantly from along-track altimetry. Additionally, we will work with multiple modalities with complementary views on the phenomenon such as from sea surface temperature (SST) maps serving to guide the procedure.
A machine-learning approach, utilizing (1) multi-modal data (along-track altimetry data, MODIS data, SST) which gives a comprehensive and complementary view on ocean eddies, (2) reference data in form of in-situ observations such as eddies detected in mooring data, SWOT SAR SSH data (3 years starting 2021) and (3) visual interpretation of satellite images (including SST), will provide improved results (when compared to techniques applied to gridded data) in terms of eddy identification, classification, and tracking, in particular when applied to multi-mission along-track radar altimetry data.
The project focuses on three regions, namely the North Atlantic (mainly the Gulf Stream), Agulhas current region, and Mediterranean region. Altimetry data (simulated and real, along-track) preparation including reference data (labelled eddies) preparation will be supervised by Prof. Jürgen Kusche. The development of machine learning approach and eddy identification and tracking strategy will be supervised by Prof. Ribana Roscher. Read more-->
