Reference : Correction of inertial oscillations by assimilation of HFradar data in a model of the...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/203793
Correction of inertial oscillations by assimilation of HFradar data in a model of the Ligurian Sea
English
Vandenbulcke, Luc mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GHER > >]
Barth, Alexander mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Beckers, Jean-Marie mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
26-Nov-2016
Ocean Dynamics
Springer Science & Business Media B.V.
Topical Collection on the 47th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 4–8 May 2015
Yes (verified by ORBi)
International
1616-7341
1616-7228
Heidelberg
Germany
[en] Data assimilation ; High-frequency radar ; Ligurian Sea
[en] This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ensemble of ROMS models covering the Ligurian Sea, and nested in the Mediterranean Forecasting System, is coupled with two WERA high-frequency radars. A sensitivity study allows to determine optimal parameters for the ensemble filter. By assimilating observations in a single point, the obtained correction shows that the forecast error covariance matrix represents the inertial oscillations, as well as large- and meso-scale processes. Furthermore, it is shown that the velocity observations can correct the phase and amplitude of the inertial oscillations. Observations are shown to have a strong effect during approximately half a day, which confirms the importance of using a high temporal observation frequency. In general, data assimilation of HF radar observations leads to a skill score of about 30 % for the forecasts of surface velocity.
http://hdl.handle.net/2268/203793
10.1007/s10236-016-1012-5
FP7 ; 283580 - SANGOMA - Stochastic Assimilation for the Next Generation Ocean Model Applications

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