|Reference : Derivation of high-resolution ocean surface fields for regional and coastal models|
|Scientific congresses and symposiums : Unpublished conference|
|Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography|
|Derivation of high-resolution ocean surface fields for regional and coastal models|
|Alvera Azcarate, Aïda [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]|
|Barth, Alexander [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]|
|He, R. [> >]|
|Helber, R. W. [> >]|
|Law, J. [> >]|
|Weisberg, R. H. [> >]|
|AGU Spring Meeting 2005|
|du 23 mai 2005 au 27 mai 2005|
|[en] Descriptive and regional oceanography ; Numerical modeling ; Ocean prediction|
|[en] Coastal ocean circulation models need high-resolution input fields, such as winds, sea surface height and heat fluxes, to represent the variability of coastal systems. Atmosphere model outputs and satellite data are usually used. However, atmosphere models are usually too coarse and do not represent the high variability of coastal systems, and satellite data do not present a complete coverage, mainly due to cloudiness. In situ observations can accurately represent the complex temporal variability of coastal regions, but usually their spatial coverage is far from optimal. Several products derived from atmosphere models, satellite images and in situ observations are prepared to use as high-resolution input fields suitable for coastal models. An optimally interpolated (OI) wind field has been realized by merging atmosphere model winds, satellite-derived winds (from quikSCAT) and in situ buoy measurements. Other fields, such as geostrophic currents, are derived from Sea Surface Height anomaly obtained from the Topex/Poseidon, Jason, ERS 1/2 and Envisat altimeter product of the CLS center, plus a MICOM mean dynamic topography. Sea Surface Temperature (SST) is also needed to correct surface heat fluxes, but satellite SST is often gappy due to clouds. Two different approaches are investigated in order to obtain complete fields, one using OI and the other using Empirical Orthogonal Functions (EOF) for the reconstruction of missing data. The EOF-based method can reconstruct different variables together, such as SST and surface chlorophyll, by using the correlation between them. This multi-variate approach is used here, and compared to the mono-variate OI product.|
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