Data Assimilation in Ocean Prediction
Abstract
Remotely sensed satellite observations provide ocean altimetry data at high temporal and spatial resolutions to an unprecedented accuracy of approximately 5 cms. Availability of data of such high quality and wide coverage makes it possible to address a variety of scientific questions related to physical oceanography including, for example, the accurate estimation of ocean circulation at several levels of interest. Achieving such an objective, however, presents daunting challenges in terms of the enormous size of the problems to be solved. By developing computationally efficient techniques for the direct assimilation of satellite altimetry data, this project aims at improving the reconstruction of ocean fields in global and mesoscale ocean circulation applications, and, therefore, improve the capability of nowcast and forecast Navy models. The objective of this project is to interface fast implementations of the Kalman-Bucy filters (KBf) that we have developed [1, 2, 3, 4], with the Naval Research Laboratory (NRL) Layered Ocean Model (NOARL), [5], and then carry out extensive data assimilation studies in ocean circulation. We are working in close collaboration with Dr. Harley Hurlburt and Ms. Tommy Townsend at the NRL Stennis Space Center. The research will provide a better understanding of ocean circulation by assimilating satellite altimetry data to global ocean circulation at different levels of resolution.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1998
- Accession Number
- ADA572555
Entities
People
- Jose M. Moura
Organizations
- Carnegie Mellon University