Understanding Predictability of the Ocean

Abstract

Abstract : The long-term scientific goals of this research are as follows: (1) to develop an understanding of how some observations affect ocean predictability, (2) to further develop the state-of-the-art ROMS 4D-Var by extending the observational types and applications, (3) to gain experience and develop ideas regarding limitations to the predictability of oceanic processes, and (4) to train a new generation of students in data assimilation and ROMS. The primary objectives of this project are as follows: explore the capabilities of a real-time ocean state-estimation and prediction system; assess how particular observations may affect predictability; compare these results with full ocean-state estimates generated from the PSI EX acoustic experiment; and provide the means to build a research program for early career scientists. With this YIP award, my aim is to characterize the factors that control predictability in the ocean, particularly around Hawaii and the North Philippine Sea. Accomplishing this goal will require a number of studies of the role of internal tides interacting with mesoscale energy. It also will require quantifying the role of observations in understanding such difficult dynamical regimes, extending the capability of the assimilation procedure to utilize advanced observational datasets (high-frequency radar and acoustic tomography), making further improvements to the state-estimation procedure, and quantifying the role of errors in models. As part of the NOAA-funded Integrated Ocean Observing System (IOOS) effort, I lead the ocean modeling effort of the Pacific IOOS (PacIOOS) region. The ocean model used in this research is the ONR-funded Regional Ocean Modeling System (ROMS) a free-surface, hydrostatic, primitive equation ocean model discretized with a terrain-following vertical coordinate system. ROMS has been successfully used to model many regions of the world's ocean and is a widely used community resource.