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Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System
Accepted manuscript   Open access   Peer reviewed

Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

John L. Wilkin, Leslie Rosenfeld, Arthur Allen, Rebecca Baltes, Antonio Baptista, Ruoying He, Patrick J. Hogan, Alexander Kurapov, Avichal Mehra, Josie Quintrell, …
Journal of Operational Oceanography, Vol.10(2), pp.115-126
2017
DOI:
https://doi.org/10.7282/T3HH6P0G

Abstract

Coastal ocean Modeling Forecasting Real-time Operational Data assimilation Cyberinfrastructure Skill assessment Model coupling Observing system design GOOS
This paper outlines strategies that would advance coastal ocean modeling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the U.S. Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of U.S. based researchers with a cross-section of coastal oceanography and ocean modeling expertise and community representation drawn from Regional and U.S. Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modeling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3-8 year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, U.S. Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal-academic partnerships benefiting IOOS stakeholders and end users.
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Accepted Manuscript Open Access
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http://dx.doi.org/10.1080/1755876X.2017.1322026View
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