Making forecasts for chaotic physical processes
Department of Mathematics and Statistics, College of Enginee
Institute of Physical Science and Technology, University of Maryland
Last modified: April 25, 2006
Making a prediction for a chaotic physical process involves specifying the probability associated with each possible outcome. Ensembles of solutions are frequently used to estimate this probability distribution. However, for a typical complex system H and model L of that system, no solution of L remains close to H for all time. We propose an alternative. In this talk, we show how to ``inflate" or systematically perturb the ensemble of solutions of L so that some ensemble member remains close to H for orders of magnitude longer than unperturbed solutions of L. This is true even when the perturbations are significantly smaller than the model error.