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Abstract
Abstract. 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.
Highlights
A novel method to correct ocean surface-velocity field predictions has been developed by implementing variational data assimilation of two-dimensional Lagrangian drifter trajectories in the Mediterranean ocean Forecasting System (MFS)
The scope of the present study is to show some encouraging first results due to the implementation of the new driftertrajectory assimilation scheme
It will be shown that the three-dimensional dataassimilation scheme (OceanVar) in MFS is capable of simultaneously correcting the surface and sub-surface velocity fields through Lagrangian trajectory increments
Summary
A novel method to correct ocean surface-velocity field predictions has been developed by implementing variational data assimilation of two-dimensional Lagrangian drifter trajectories in the Mediterranean ocean Forecasting System (MFS). Assimilation of drifter observations in ocean models has previously been attempted using various numerical methods, e.g. optimal interpolation (Molcard et al, 2003), nudging (Fan et al, 2004), and Kalman filtering (Ozgokmen et al, 2003), all with promising results. In order to guarantee stateof-art ocean analyses, a three-dimensional data assimilation scheme denoted OceanVar (Dobricic and Pinardi, 2008) is under continuous development, and recently glider observations (Dobricic et al, 2010) and Argo-float trajectories (Nilsson et al, 2011) have been successfully assimilated. The modified MFS is able to maintain previous quality levels of the daily-mean sea surface height (SSH), temperature, and salinity model fields
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