Theory and Practice of Data Assimilation in Ocean Modeling
OREGON STATE UNIV CORVALLIS COLL OF OCEANIC AND ATMOSPHERIC SCIENCES
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The long range goal of this project is to combine computational models with observational data to form the best picture of the ocean as an evolving system, and use that picture to understand the physical influences which govern the ocean s behavior. Oceanic observations are sparse and models are limited in accuracy, but taken together, one can form a quantitative description of the state of the ocean that is superior to any based on either models or data alone. Along with the goals of analysis and prediction, we seek reliable estimates of the errors in our results. We expect our results to have implications beyond data assimilation. In particular, we hope this research will lead to enhanced understanding of the implications of nonlinearity and randomness for predictability of the ocean and atmosphere. It is our long range goal to develop efficient methods for estimating useful statistical measures of errors in stochastic forecast models. Since the probability density functions PDF s of nonlinear stochastic models are not, in general, Gaussian, we must find methods for forecast evaluation based on information about the particular PDF generated by the model.
- Physical and Dynamic Oceanography
- Statistics and Probability