Collaborative Research: CMG: Data Assimilation by Synchronization of Truth and Model

University Of California-San Diego, La Jolla CA

Investigators

Abstract

This project explores the idea of developing an approach to data assimilation based on methods for synchronizing two chaotic systems, one being the real atmosphere and the other being a numerical forecast model of the atmosphere. The research will progress from simple low-dimensional models to models more characteristic of weather models. One of the synchronization methods will be based on bred vectors. Innovations in data assimilation that increase the efficiency or effectiveness of environmental forecasting systems might have a number of important applications such as the prediction of terrestrial and space weather, coastal ocean currents and ecosystems, or surface and sub-surface hydrology.