Projects

Hindcast of Dynamic Processes of the Ocean and Coastal Areas of Europe (HIPOCAS)

Duration: 01.06.2000-31.05.2004 (48 months)

Coordination: Instituto Superior Técnico

Partners: Puertos del Estado (Spain), GKSS Forschunszentrum Geesthacht GmbG (Germany), SCOP Meteomer (France), University College Cork (Ireland)

Funding Entity: EU - Programme EESD

Project Website: http://www.mar.ist.utl.pt/hipocas

The objective is to obtain a 40-year hindcast of wind, wave, sea-level and current climatology for European waters and coastal seas for application in coastal and environmental decision processes. The initial work is to produce the atmospheric data that will serve as forcing to the circulation and wave models. To describe the small-scale atmospheric response three regional climate models will be forced with the large scale 40 years NCEP-reanalysis. The data are provided on a 50 x 50 km grid and will be used in the project to force the wave and ocean models.Circulation models will be used in the North Sea, the NorthEast Atlantic south of UK, including Azores and Canary Islands, and the Mediterranean sea. Wave models will be used in those areas and also in the Irish Sea. The predictions will be made with a typical horizontal resolution of 10km and temporal resolution of 3 hours. The wave models that will be used in the project are based on the last version of WAM, which will be used with nested grids in order to produce high-resolution information in coastal waters. In the North Sea this model will take into account tidal changes that will influence wave predictions. Altimeter data from ERS1, ERS2 and Topex-Poseidon will be sorted in order to collect the available remote sensed data including wind, wave, sea-level, storm surge and ice-sea measurements for same areas for which hindcasts are being made.Whenever the hindcast and remote sensed data are available they will be compared in order to assess the level of uncertainty involved in using the different type of data. Statistical analysis will be performed in order to extract several important long term characteristics of the data, concerning mean tendencies, variability and extremes.Finally the data will be organized in digital and paper form so as to make it easy to disseminate by the appropriate parties.

Project Team

Associated Publications