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You are here: Home / Library / RBINS Staff Publications 2016 / Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

Stéphanie Ponsar, Patrick Luyten and Valérie Dulière (2016)

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

Ocean dynamics, 66:955-971.

Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.
Peer Review, Impact Factor
Filed under: Peer Review, Impact Factor

 
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