Statistics for Dynamic Models with Application in the Marine Sciences

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Successful environmental prediction requires making effective use of simulation models and available observations. Statistical modeling provides a means of combining these two sources of information, as well as other scientific knowledge, in order to learn as much as possible about the system under consideration. This procedure is sometimes referred to as data assimilation, and is based on Bayesian statistical principles. Key application areas are in weather, climate, and marine forecasting. Special estimation methods must be developed and applied since complex environmental models are constantly being refined as scientific and computational advances are incorporated in the simulation code. Observations have become increasingly sophisticated ranging from monitoring time series, spatial imagery from satellites, and complex autonomous instruments that probe and adaptively transit the ocean depths. In the fusion of data and model lies the promise of more skillful environmental prediction, along with better scientific understanding of these systems. My proposed work deals with further development of advanced data assimilation that uses state-of-the-art statistical methods to move us towards this goal.