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

作者：Wilkin John^*; Rosenfeld Leslie; Allen Arthur; Baltes Rebecca; Baptista Antonio; He Ruoying; Hogan Patrick; Kurapov Alexander; Mehra Avichal; Quintrell Josie; Schwab David; Signell Richard; Smith Jane

来源：Journal of Operational Oceanography, 2017, 10(2): 115-126.

DOI：10.1080/1755876X.2017.1322026

• Coastal ocean
• modelling
• forecasting
• real-time
• operational
• data assimilation
• cyberinfrastructure
• skill assessment
• model coupling
• observing system design
• GOOS

This paper outlines strategies that would advance coastal ocean modelling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the US Integrated Ocean Observing System (IOOS (R)) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of US-based researchers with a cross-section of coastal oceanography and ocean modelling expertise and community representation drawn from Regional and US 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, modelling 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, US 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.

• 出版日期2017
• 单位rutgers