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dc.contributor.authorRen, Lei
dc.contributor.authorHartnett, Michael
dc.date.accessioned2018-09-20T16:22:42Z
dc.date.available2018-09-20T16:22:42Z
dc.date.issued2017-09-08
dc.identifier.citationRen, Lei; Hartnett, Michael (2017). Hindcasting and forecasting of surface flow fields through assimilating high frequency remotely sensing radar data. Remote Sensing 9 (9),
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10379/13630
dc.description.abstractIn order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE) values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS) technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east-west and north-south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE) was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time.
dc.publisherMDPI AG
dc.relation.ispartofRemote Sensing
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectremote sensing
dc.subjectnudging
dc.subjectdata assimilation
dc.subjectsurface currents
dc.subjectcodar
dc.subjectforecasting
dc.subjecthindcasting
dc.subjectgalway bay
dc.subjectradars
dc.subjectmodeling system roms
dc.subjecthf radar
dc.subjectmonterey bay
dc.subjectpart i
dc.subjectocean
dc.subjectcurrents
dc.subjectsea
dc.subjecttransport
dc.subjectdrifter
dc.subjectimpact
dc.titleHindcasting and forecasting of surface flow fields through assimilating high frequency remotely sensing radar data
dc.typeArticle
dc.identifier.doi10.3390/rs9090932
dc.local.publishedsourcehttp://www.mdpi.com/2072-4292/9/9/932/pdf
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland