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dc.contributor.authorToye, Habib
dc.contributor.authorZhan, Peng
dc.contributor.authorGopalakrishnan, Ganesh
dc.contributor.authorKartadikaria, Aditya R.
dc.contributor.authorHuang, Huang
dc.contributor.authorKnio, Omar
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2017-06-04T06:30:49Z
dc.date.available2017-06-04T06:30:49Z
dc.date.issued2017-05-26
dc.identifier.citationToye H, Zhan P, Gopalakrishnan G, Kartadikaria AR, Huang H, et al. (2017) Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1064-1.
dc.identifier.issn1616-7341
dc.identifier.issn1616-7228
dc.identifier.doi10.1007/s10236-017-1064-1
dc.identifier.urihttp://hdl.handle.net/10754/624028
dc.description.abstractWe present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.
dc.description.sponsorshipThis research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and the Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). The research made use of the resources of the Super computing Laboratory and computer clusters at KAUST.
dc.publisherSpringer Nature
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs10236-017-1064-1
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1064-1.
dc.subjectRed Sea
dc.subjectData assimilation
dc.subjectSeasonal variability
dc.subjectEnsemble Kalman filter
dc.subjectEnsemble optimal interpolation
dc.titleEnsemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBeacon Development Company
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Dynamics
dc.eprint.versionPost-print
dc.contributor.institutionScripps Institution of Oceanography, University of California San Diego, La Jolla, USA
dc.contributor.institutionStudy Program of Oceanography, Bandung Institute of Technology, Bandung, Indonesia
kaust.personToye, Habib
kaust.personZhan, Peng
kaust.personKartadikaria, Aditya R.
kaust.personHuang, Huang
kaust.personKnio, Omar
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-05-26T00:00:00Z
dc.date.published-online2017-05-26
dc.date.published-print2017-07


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