A large number of oceanographic applications rely on gridded maps of Sea Surface Height (SSH) and related variables such as surface currents. Such products have been for about 20 years designed by the DUACS (Data Unification and Altimeter Combination System) project, led by the Collecte Localisation Satellites company (CLS, Toulouse) in partnership with CNES. The DUACS system, unique worldwide, processes uncalibrated along-track altimetric observations into blended ocean products. More than 6,000 users have been registered so far.
The current DUACS system processes constellations of 2 to 4 nadir-looking altimeters. The algorithm is based on a static, statistical interpolation of along-track data and delivers SSH daily maps with a resolution of 1/4°, resolving scales larger than 200 km. However, recent research has unveiled an important climatic role of finer-scale ocean surface dynamics, and motivates building higher resolution products. Concomitantly, the Surface Water and Ocean Topography (SWOT) wide-swath altimetry mission, to be launched in 2021, will provide 120-km wide SSH images at a kilometric resolution. This paves the way to gridded products with a resolution much higher than today.
Updating the DUACS system to increase resolution and incorporate SWOT will be challenging for several reasons, mostly related to SWOT: high spatial resolution, low temporal resolution, noisy measurements, huge amount of data. There is a general consensus, among the SWOT Science Team, that the next-generation algorithms for building high-resolution gridded products from space altimetry, including SWOT, will involve Numerical Ocean Circulation Models (NOCM) and Data Assimilation (DA) techniques instead of statistical interpolation.
The project aims at investigating approaches based on NOCM and DA methods to increase the spatial resolution of, and include the future SWOT data in the DUACS system. The strategy is to (i) set up a working framework with simulated SSH data, data denoising techniques, and evaluation metrics; (ii) identify the optimal NOCM to merge altimetric data, including SWOT; (iii) examine and implement the appropriate NOCM/DA system; (iv) evaluate and compare the developed system to the existing DUACS system; and (v) communicate toward the user community through a dedicated workshop. Unlike operational systems, where most computational resources are allocated to a primitive-equation model for prediction purposes, the present system will be designed to extend the observations in the first place, therefore putting most efforts into data processing and assimilation.
The consortium primarily results from a new collaboration between the Oceanography team (MEOM) of the Institut des Géosciences de l'Environnement (IGE, Grenoble) and CLS. MEOM concentrates high expertise in ocean physics, numerical modeling, and DA. CLS expertise essentially concerns the operational processing and exploitation of altimetry data. The project will also benefit from external expertise in image processing and machine learning techniques.
The project will benefit to society and/or economy for a better understanding of the climate machine, for industrial applications at sea, and for non-profit applications such as transport of pollutants, slick drift predictions, or rescue at sea. The scientific results will be disseminated through science publications and communications. The tools developed will feed back the operational DUACS system and, at mid-term, the Copernicus Marine Environment Monitoring Service (CMEMS). Both partners will benefit from the academic research transfer towards applications and will have started a new, potentially long-lasting collaboration.
Monsieur Emmanuel Cosme (Institut des Géosciences de l'Environnement (IGE))
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
CLS COLLECTE LOCALISATION SATELLITES
IGE - UGA Institut des Géosciences de l'Environnement (IGE)
Help of the ANR 505,368 euros
Beginning and duration of the scientific project: December 2017 - 48 Months