Sequential Estimation of High-Dimensional Space-Time Models

• Activity Number: 490
• Type: Invited
• Date/Time: Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
• Sponsor: American Geophysical Union
• Author(s): Jonathan Stroud*+
• Companies: University of Pennsylvania

Abstract

Kalman filter methods for real-time assimilation of observations and dynamical systems typically assume knowledge of the system parameters. However, relatively little work has been done on extending state estimation procedures to include parameter estimation. Here, in the context of the ensemble Kalman filter, a Monte Carlo-based algorithm is proposed for sequential estimation of the states and model parameters. A Bayesian approach is adopted that yields analytical updating of the parameter distribution and provides samples from the posterior distribution of the states and parameters. The proposed assimilation algorithm extends standard ensemble methods, including perturbed observations, and serial and square root assimilation schemes. The method is illustrated on the Lorenz 40-variable system and a real example involving assimilation of satellite reflectance images.