A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico

Handle URI:
http://hdl.handle.net/10754/562921
Title:
A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico
Authors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Hoar, Timothy J.; Gopalakrishnan, Ganesh; Collins, Nancy S.; Anderson, Jeffrey L.; Cornuelle, Bruce D.; Köhl, Armin; Heimbach, Patrick
Abstract:
This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed. © 2013 Elsevier B.V.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Dynamics of Atmospheres and Oceans
Issue Date:
Sep-2013
DOI:
10.1016/j.dynatmoce.2013.03.002
Type:
Article
ISSN:
03770265
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorHoar, Timothy J.en
dc.contributor.authorGopalakrishnan, Ganeshen
dc.contributor.authorCollins, Nancy S.en
dc.contributor.authorAnderson, Jeffrey L.en
dc.contributor.authorCornuelle, Bruce D.en
dc.contributor.authorKöhl, Arminen
dc.contributor.authorHeimbach, Patricken
dc.date.accessioned2015-08-03T11:15:34Zen
dc.date.available2015-08-03T11:15:34Zen
dc.date.issued2013-09en
dc.identifier.issn03770265en
dc.identifier.doi10.1016/j.dynatmoce.2013.03.002en
dc.identifier.urihttp://hdl.handle.net/10754/562921en
dc.description.abstractThis paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed. © 2013 Elsevier B.V.en
dc.publisherElsevier BVen
dc.subjectData assimilationen
dc.subjectEnsemble Kalman filteren
dc.subjectGulf of Mexicoen
dc.subjectOcean state estimationen
dc.titleA MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexicoen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalDynamics of Atmospheres and Oceansen
dc.contributor.institutionNational Center of Atmospheric Research (NCAR), Boulder, CO, United Statesen
dc.contributor.institutionScripps Institution of Oceanography, University of California, San Diego, United Statesen
dc.contributor.institutionInstitute of Oceanography, University of Hamburg, Hamburg, Germanyen
dc.contributor.institutionMassachusetts Institute of Technology (MIT), Boston, MA, United Statesen
kaust.authorHoteit, Ibrahimen
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