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dc.contributor.authorFringer, Oliver B.  Concept link
dc.contributor.authorDawson, Clint N.  Concept link
dc.contributor.authorHe, Ruoying  Concept link
dc.contributor.authorRalston, David K.  Concept link
dc.contributor.authorZhang, Y. Joseph  Concept link
dc.identifier.citationFringer, O. B., Dawson, C. N., He, R., Ralston, D. K., & Zhang, Y. J. (2019). The future of coastal and estuarine modeling: findings from a workshop. Ocean Modelling, 143, 101458.en_US
dc.description© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Fringer, O. B., Dawson, C. N., He, R., Ralston, D. K., & Zhang, Y. J. The future of coastal and estuarine modeling: findings from a workshop. Ocean Modelling, 143, (2019): 101458, doi:10.1016/j.ocemod.2019.101458.en_US
dc.description.abstractThis paper summarizes the findings of a workshop convened in the United States in 2018 to discuss methods in coastal and estuarine modeling and to propose key areas of research and development needed to improve their accuracy and reliability. The focus of this paper is on physical processes, and we provide an overview of the current state-of-the-art based on presentations and discussions at the meeting, which revolved around the four primary themes of parameterizations, numerical methods, in-situ and remote-sensing measurements, and high-performance computing. A primary outcome of the workshop was agreement on the need to reduce subjectivity and improve reproducibility in modeling of physical processes in the coastal ocean. Reduction of subjectivity can be accomplished through development of standards for benchmarks, grid generation, and validation, and reproducibility can be improved through development of standards for input/output, coupling and model nesting, and reporting. Subjectivity can also be reduced through more engagement with the applied mathematics and computer science communities to develop methods for robust parameter estimation and uncertainty quantification. Such engagement could be encouraged through more collaboration between the forward and inverse modeling communities and integration of more applied math and computer science into oceanography curricula. Another outcome of the workshop was agreement on the need to develop high-resolution models that scale on advanced HPC systems to resolve, rather than parameterize, processes with horizontal scales that range between the depth and the internal Rossby deformation scale. Unsurprisingly, more research is needed on parameterizations of processes at scales smaller than the depth, including parameterizations for drag (including bottom roughness, bedforms, vegetation and corals), wave breaking, and air–sea interactions under strong wind conditions. Other topics that require significantly more work to better parameterize include nearshore wave modeling, sediment transport modeling, and morphodynamics. Finally, it was agreed that coastal models should be considered as key infrastructure needed to support research, just like laboratory facilities, field instrumentation, and research vessels. This will require a shift in the way proposals related to coastal ocean modeling are reviewed and funded.en_US
dc.description.sponsorshipWe thank Carmen Torres at Stanford University and Jennifer Warrillow at North Carolina State University for their assistance with workshop logistics. Helpful comments and suggestions were provided by two anonymous reviewers and Hans Burchard and John Warner. The workshop and preparation of this paper were funded by U.S. National Science Foundation Grant OCE-1749613.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectCoastal ocean modelingen_US
dc.subjectPhysical processesen_US
dc.subjectModel subjectivityen_US
dc.subjectDevelopment of standardsen_US
dc.subjectHigh-resolution modelingen_US
dc.subjectParameter estimationen_US
dc.titleThe future of coastal and estuarine modeling: findings from a workshopen_US

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