Abstract's details

Fostering collaborations for designing high level ocean data products : the case for community data challenges.

Sammy Metref (MEOM-IGE, Université Grenoble Alpes, France)


Emmanuel Cosme (Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France); Clément Ubelmann (Datlas, Grenoble, France); Julien Le Sommer (Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France); Aurélie Albert (Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France); Maxime Ballarotta (Collecte Localisation Satellites, 31520 Ramonville-Saint-Agne, France); Adekunle Ajayi (Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France ; Space Sense, 75001 Paris, France); Florian Le Guillou (Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France); Maxime Beauchamp (IMT Atlantique, Lab-STICC, Université Bretagne Loire, Brest, France); Ronan Fablet (IMT Atlantique, Lab-STICC, Université Bretagne Loire, Brest, France)

Event: 2022 Ocean Surface Topography Science Team Meeting

Session: Science III: Mesoscale and sub-mesoscale oceanography

Presentation type: Type Poster

Algorithms using trainable components, data assimilation and other inverse techniques are becoming unavoidable tools for designing high-level data products leveraging oceanic observations for a wide range of applications. These fast and promising advances are speeding up the pace of technological and scientific progress in our field. However, because of the variety of approaches, sometimes emanating from different communities and scattered research groups, keeping track of progress is becoming more difficult. This is why fostering community-driven, intercomparison frameworks would be of great benefit for creating the next-generation high level ocean data products and to the oceanographic community in general.
Our project BOOST-SWOT ( “Building Of Ocean Surface Topography maps with SWOT”) has led to a number of methodological developments for sea surface height cartography and for the preprocessing of the SWOT ocean data. This includes: inverse techniques for filtering SWOT observational noise, algorithms for mapping SSH with data assimilation techniques applied to simple dynamical models, and approaches for optimally accounting for high-frequency internal wave dynamics in mapping algorithms. In order to assess the potential and the limitations of the above algorithms, we have implemented a series of collaborative data challenges that have been shared across different research groups. These collaborative data challenges have been implemented on the basis of observational data, state-of-the-art numerical model simulations and evaluation metrics. They are meant to provide well-posed benchmarks for intercomparing inversion methods for the mapping of altimeter data.
In this presentation, we will take a look back at the BOOST-SWOT results and at the benefits and issues raised during the challenges. On the basis of the BOOST-SWOT experience, we will try to describe what are, from our perspective, the key elements for a fruitful collaboration across different research groups through open data challenges: easy to access open source codes and data, co-designed evaluation metrics and blind evaluation references. We will conclude on what could be the first steps towards developing and maintaining systematic collaboration frameworks for our community in the future.


Poster show times:

Room Start Date End Date
Mezzanine Tue, Nov 01 2022,17:15 Tue, Nov 01 2022,18:15
Mezzanine Thu, Nov 03 2022,14:00 Thu, Nov 03 2022,15:45
Sammy Metref
MEOM-IGE, Université Grenoble Alpes