Multi-platform experiments, numerical simulations and data science techniques for generation of new altimetric products: focus on mesoscale and sub-mesoscale variability (MANATEE)

Ananda Pascual (IMEDEA(CSIC-UIB), Spain)


Ronan Fablet (IMT-Atlantique, France); Evan Mason (IMEDEA(CSIC-UIB), Spain); Antonio Sánchez-Román (IMEDEA(CSIC-UIB), Spain); Bàrbara Barceló-Llull (IMEDEA(CSIC-UIB), Spain); Giuseppe Aulicino ( Università degli Studi di Napoli Parthenope, Italy); Yuri Cotroneo ( Università degli Studi di Napoli Parthenope, Italy); Eugenio Cutolo (IMEDEA(CSIC-UIB), Spain); Daniel Rodríguez-Tarry (IMEDEA(CSIC-UIB), Spain); Said Ouala (IMT- Atlantique, France); Manuel López-Radcenco (IMT-Atlantique, France); Yannice Faugère (CLS, France); Laura Gómez-Navarro (IGE and IMEDEA(CSIC-UIB), France); Simon Ruiz (IMEDEA(CSIC-UIB), Spain)

Event: 2020 Ocean Surface Topography Science Team Meeting (virtual)

Session: Salient results from the 2017-2020 OSTST PIs

Presentation type: Type Forum only

The general objective of the MANATEE project is to improve the characterization of oceanic mesoscale and sub-mesoscale features (e.g. fronts, meanders, eddies and filaments) through the combined use of in situ and satellite data in synergy with numerical models and innovative computational techniques. The ultimate goal is to enhance our understanding of the impact of fine-scale processes. MANATEE has assessed our actual capability to map the SSH variability for a range of scales (15-100 km) traditionally not resolved by conventional altimeters through the development of a multidisciplinary expertise in physical oceanography and computational science.

In this presentation we review some of the results obtained in the framework of this project including those obtained from the synergy of in situ and satellite observations with supporting numerical simulations during dedicated multi-platform field experiments in the western Mediterranean Sea aimed at estimating fine-scale horizontal and vertical currents (e.g. Abacus, PRE-SWOT, Calypso2018 and Calypso2019). We summarize the lessons learned in terms of advantages and limitations of the present approaches that combine satellite data with other cutting-edge and well established observational techniques and numerical modeling.

Future directions in preparation for SWOT are also addressed, including machine learning and deep learning strategies, and the need to observe and resolve a range of scales that will contribute to enhance our understanding of ocean currents associated with meso- and submesoscale features, with impacts on longer climatic scales.
Ananda Pascual