STOCHASTIC COASTAL/REGIONAL UNCERTAINTY MODELLING:
SENSITIVITY, CONSISTENCY AND POTENTIAL CONTRIBUTION TO CMEMS ENSEMBLE DATA ASSIMILATION
PI and organization: Sarantis Sofianos (Univ. of Athens)
Other members of the proposing team: Vassilios Vervatis (Univ. of Athens), Pierre De Mey (CNRS/LEGOS), Nadia Ayoub (CNRS/LEGOS)
External expert involved: George Triantafyllou (HCMR) (DA experts from MO, C.-E. Testut).
Abstract: The proposed work aims at strengthening CMEMS in the areas of regional/coastal ocean uncertainty modelling, Ensemble consistency verification, and Ensemble data assimilation. The work is based on stochastic modelling of ocean physics and biogeochemistry, in the context of coastal/regional Ensemble Data Assimilation (EDA) forecasting systems, and includes novel Ensemble consistency verification methods. The work is designed for the Service Evolution framework within “Lot 3: links with coastal environment”. In a first step, we will use stochastic modelling to generate Ensembles describing uncertainties in coastal/regional domains. In a second step, we will introduce and test two methodologies aimed at checking the consistency of the above Ensembles with respect to TAC data and arrays. In addition, we wish to showcase the use of those Ensemble-modelled uncertainties in a pilot data assimilation exercise, and contribute to the CMEMS DA team guidance in support of upcoming decisions regarding the evolution of regional/coastal data assimilation schemes in CMEMS.