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See detailTrace metal dispersion and uptake in the Gulf of Cadiz
Beckers, Jean-Marie ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2002, September)

Detailed reference viewed: 8 (2 ULiège)
See detailTwo-ways, doubly nested primitive equation model of the Ligurian Sea
Barth, Alexander ULiege; Rixen, M.; Alvera Azcarate, Aïda ULiege et al

Conference (2002, April)

In the framework of the SOFT project (Satellite-based Ocean ForecasTing), the GHER 3D primitive equation model is implemented in its double nesting version. A low res- olution model (15 ) simulates the ... [more ▼]

In the framework of the SOFT project (Satellite-based Ocean ForecasTing), the GHER 3D primitive equation model is implemented in its double nesting version. A low res- olution model (15 ) simulates the circulation in the Mediterranean Sea. This model provides the boundary conditions for an intermediate resolution model (3 ) located in the Gulf of Lions and the Ligurian Sea. The next nesting level is a one-minute resolu- tion model centered in the Ligurian Sea. Each "child" model depends on its "parent" model by the boundary conditions. At each time step the mean values of the "child" model are also injected in the "parent" model. The conservation laws require the inter- polation of the boundary values to be consistent with the finite volume discretisation of the model. A method taking also into account the difference of the land-sea mask in the "child" and "parent" model, will be discussed. The models run over a time period of 1 months starting the 1st January 1999. The output of the high resolution model is compared with the Sea Surface Temperature measured by the ATSR-2. The results show that nested and two ways coupled models are a powerful approach to solve open boundary problems and to use different grid resolutions in a numerically efficient way. [less ▲]

Detailed reference viewed: 16 (1 ULiège)
See detailSoft project: a new ocean forecasting system based on satellite data
Pascual, A.; Orfila, A.; Alvarez, A. et al

Conference (2002)

The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite data, evolutionary programming and numerical ocean models. To achieve this objective two steps ... [more ▼]

The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite data, evolutionary programming and numerical ocean models. To achieve this objective two steps are proposed: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build "intelligent" systems that, learning from the past ocean variability (provided by satellite data) and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results. Keywords: forecasting, satellite data, empirical orthogonal functions, numerical models, genetic algorithms, neural networks, Mediterranean Sea. [less ▲]

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See detailNon-linear neural networks forecasting of sea level anomaly in the Alboran Sea
Rixen, M.; Barth, Alexander ULiege; Beckers, Jean-Marie ULiege

Conference (2002)

Forecasts based on artificial intelligence (AI) concepts exploit past time series of satellite images to infer near future ocean conditions at the surface by feed-forward non-linear neural networks. The ... [more ▼]

Forecasts based on artificial intelligence (AI) concepts exploit past time series of satellite images to infer near future ocean conditions at the surface by feed-forward non-linear neural networks. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a two-dimensional surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the time independent amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea and the Alboran Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period October 1992 to March 2000. The learning procedure is applied to each mode individually. The final forecast is then reconstructed from the EOFs and the forecasted amplitudes, and compared to the real observed field, the persistence and linear forecasts for validation purposes. [less ▲]

Detailed reference viewed: 15 (1 ULiège)