Theory and Practice of Data Assimilation in Ocean Modeling
Abstract
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 this 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 goal 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. Ultimately, formal theory of nonlinear filtering should be adapted for application to oceanic data assimilation, in order to find the best possible scheme for assimilating data into practical models of the real nonlinear ocean. The next generation of data assimilation techniques must be specifically designed for use with nonlinear models. It is our long range goal to develop these methods.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 1997
- Accession Number
- ADA619737
Entities
People
- Robert N. Miller
Organizations
- Oregon State University