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dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorGopalakrishnan, Ganesh
dc.contributor.authorLatif, Hatem
dc.contributor.authorToye, Habib
dc.contributor.authorZhan, Peng
dc.contributor.authorKartadikaria, Aditya R.
dc.contributor.authorViswanadhapalli, Yesubabu
dc.contributor.authorYao, Fengchao
dc.contributor.authorTriantafyllou, George
dc.contributor.authorLangodan, Sabique
dc.contributor.authorCavaleri, Luigi
dc.contributor.authorGuo, Daquan
dc.contributor.authorJohns, Burt
dc.date.accessioned2016-01-28T07:13:27Z
dc.date.available2016-01-28T07:13:27Z
dc.date.issued2015-04
dc.identifier.urihttp://hdl.handle.net/10754/595105
dc.description.abstractDespite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We have also developed advanced visualization tools to interactively analyze the forecasts and their ensemble-based uncertainties.
dc.relation.urlhttp://adsabs.harvard.edu/abs/2015EGUGA..17.4443H
dc.titleThe Red Sea Modeling and Forecasting System
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBeacon Development Company
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.conference.date12-17 April, 2015
dc.conference.nameEGU General Assembly 2015
dc.conference.locationVienna, Austria
dc.contributor.institutionScripps Institution of Oceanography (SIO), San Diego, USA
dc.contributor.institutionHellenic Centre for Marine Research (HCMR), Gournes, Greece
dc.contributor.institutionInstitute for Marine Science (ISMAR), Bologna, Italy
kaust.personHoteit, Ibrahim
kaust.personLatif, Hatem
kaust.personToye, Habib
kaust.personZhan, Peng
kaust.personKartadikaria, Aditya R.
kaust.personViswanadhapalli, Yesubabu
kaust.personYao, Fengchao
kaust.personLangodan, Sabique
kaust.personGuo, Daquan
kaust.personJohns, Burt


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