Near real-time ocean circulation assimilation and prediction in the Intra-Americas Sea with ROMS |
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| Institution: | 1. Dept. of Oceanography, University of Hawaiì, Manoa, HI, United States;2. Dept. of Ocean Sciences, University of California at Santa Cruz, United States;3. Institute of Marine and Coastal Sciences, Rutgers University, NJ, United States;4. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, United States;5. NWRA, Colorado Research Associates Division, Boulder, CO, United States;6. Colorado Center for Astrodynamics Research, University of Colorado, Boulder, CO, United States;1. All-Russia Research Institute of Automatics, Moscow 101000, Russia;2. Institute for Theoretical and Applied Electrodynamics, Moscow 125412, Russia;3. Moscow Institute of Physics and Technology, Moscow 117303, Russia;1. Sustainable Arctic Marine and Coastal Technology (SAMCoT), Centre for Research-based Innovation (CRI), Norwegian University of Science and Technology, 7491 Trondheim, Norway;2. Department of Mathematical Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway;1. Department of Mathematics and Statistics, Air Force Institute of Technology, Dayton, OH, United States;2. Department of Mathematics, Drexel University, Philadelphia, PA, United States;1. School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410114, China;2. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha, 410114, China;3. School for Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, MA, 02744, USA;4. Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou, 510611, China |
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| Abstract: | We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting. |
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