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dc.contributor.authorRen, Lei
dc.contributor.authorHartnett, Michael
dc.date.accessioned2018-09-20T16:22:41Z
dc.date.available2018-09-20T16:22:41Z
dc.date.issued2017-12-19
dc.identifier.citationRen, Lei; Hartnett, Michael (2017). Comparative study on assimilating remote sensing high frequency radar surface currents at an atlantic marine renewable energy test site. Remote Sensing 9 (12),
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10379/13629
dc.description.abstractA variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the 6 h forecasting period from the best model NDA attained 26% and 31% for east-west and north-south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations.
dc.publisherMDPI AG
dc.relation.ispartofRemote Sensing
dc.subjectdata assimilation
dc.subjectsurface currents
dc.subjectdirect insertion
dc.subjectoptimal interpolation
dc.subjectnudging
dc.subjectwind stress
dc.subjectradars
dc.subjectefdc
dc.subjectgalway bay
dc.subjectvariational data assimilation
dc.subjectensemble kalman filter
dc.subjectcoastal ocean model
dc.subjecthf radar
dc.subjectgalway bay
dc.subjectmediterranean sea
dc.subjectnumerical-model
dc.subjectsatellite data
dc.subjectsystem
dc.subjectprediction
dc.titleComparative study on assimilating remote sensing high frequency radar surface currents at an atlantic marine renewable energy test site
dc.typeArticle
dc.identifier.doi10.3390/rs9121331
dc.local.publishedsourcehttps://doi.org/10.3390/rs9121331
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