OS Ocean Science OS Ocean Sci. 1812-0792 Copernicus Publications Göttingen, Germany 10.5194/os-8-249-2012 Variational assimilation of Lagrangian trajectories in the Mediterranean ocean Forecasting System Nilsson J. A. U. 1 5 Dobricic S. 2 Pinardi N. 3 Poulain P.-M. 4 Pettenuzzo D. 1 National Institute of Geophysics and Vulcanology (INGV), Via Donato Creti 12, 40128 Bologna, Italy Centro EuroMediterraneo per i Cambiamenti Climatici (CMCC), Bologna, Italy Corso di Scienze Ambientali, Bologna University, Ravenna, Italy The National Institute of Oceanography and Applied Geophysics (OGS), Trieste, Italy now at: Climate Modeling and Impacts Lab (UTMEA-ENEA), Rome, Italy 30 03 2012 8 2 249 259 Copyright: © 2012 J. A. U. Nilsson et al. 2012 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://os.copernicus.org/articles/8/249/2012/os-8-249-2012.html The full text article is available as a PDF file from https://os.copernicus.org/articles/8/249/2012/os-8-249-2012.pdf

A novel method for three-dimensional variational assimilation of Lagrangian data with a primitive-equation ocean model is proposed. The assimilation scheme was implemented in the Mediterranean ocean Forecasting System and evaluated for a 4-month period. Four experiments were designed to assess the impact of trajectory assimilation on the model output, i.e. the sea-surface height, velocity, temperature and salinity fields. It was found from the drifter and Argo trajectory assimilation experiment that the forecast skill of surface-drifter trajectories improved by 15 %, that of intermediate-depth float trajectories by 20 %, and moreover, that the forecasted sea-surface height fields improved locally by 5 % compared to satellite data, while the quality of the temperature and salinity fields remained at previous levels. In conclusion, the addition of Lagrangian trajectory assimilation proved to reduce the uncertainties in the model fields, thus yielding a higher accuracy of the ocean forecasts.

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