Jungian Data Assimilation by Synchronization of Truth and Model in a Stochastic Framework

Greg Duane

National Center for Atmospheric Research

The problem of data assimilation can be viewed as one of synchronizing two dynamical systems, one representing ``truth" and the other representing "model", with a unidirectional flow of information between the two. Synchronization of truth and model is reminiscent of Jung's notion of synchronicity between matter and mind. The dynamical systems paradigm of the synchronization of a pair of loosely coupled chaotic systems is thus broadly relevant to data assimilation, and is expected to be useful because quasi-2D geophysical fluid models have been shown to synchronize when only medium-scale modes are coupled. The synchronization approach is equivalent to standard approaches based on least-squares optimization, including Kalman filtering, except in highly non-linear regions of state space where observational noise links regimes with qualitatively different dynamics. In such narrow regions, the synchronization approach is expected to give an improvement to Kalman filtering that will apply in any situation where a computational model is intended to track a physical process.

Last Update: March 25, 2005