HR: 0800h
AN: IN41A-0309    [Abstracts]
TI: Ensemble Smoothing and Markov Chain Monte Carlo for Data Assimilation in Highly Nonlinear Systems
AU: * Turmon, M
EM: turmon@jpl.nasa.gov
AF: Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109 United States
AU: Chin, T M
EM: tmchin@jpl.nasa.gov
AF: Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109 United States
AU: Jewell, J B
EM: jewell@jpl.nasa.gov
AF: Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109 United States
AU: Ghil, M
EM: ghil@atmos.ucla.edu
AF: Dept. of Atmos. and Oceanic Physics, University of California, Los Angeles, Los Angeles, CA 90095 United States
AB: Current methods for atmosphere and ocean data assimilation propagate Gaussian distributions for gridded state variables forward in time. Powerful as these methods are, they do not handle outliers well and cannot simultaneously entertain multiple hypotheses about system state. The alternative of propagating the system's full probability distribution is burdensome, and ensemble methods have been introduced into data assimilation for nonlinear systems to get around this problem. By propagating an ensemble of representative states, algorithms like the Ensemble Kalman Filter (EnKF) and the Resampled Particle Filter (RPF) rely on existing modeling infrastructure and capture the weights to be assigned to the data based on the evolution of this ensemble. We present an ensemble-based smoother that is applicable to Monte Carlo filtering schemes like the EnKF and the RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother provides superior state tracking for two simple nonlinear problems, the double-well potential and the trivariate Lorenz system. The algorithm does not require retrospective adaptation of the ensemble members themselves, and is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison of its posterior distributions with ground truth provided by a Markov chain Monte Carlo algorithm.
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 3245 Probabilistic forecasting (3238)
DE: 3315 Data assimilation
DE: 4445 Nonlinear differential equations
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005