Chinese Annals of Mathematics, Series B

, Volume 34, Issue 1, pp 29–64

Non-Gaussian Test Models for Prediction and State Estimation with Model Errors

Article

DOI: 10.1007/s11401-012-0759-3

Cite this article as:
Branicki, M., Chen, N. & Majda, A.J. Chin. Ann. Math. Ser. B (2013) 34: 29. doi:10.1007/s11401-012-0759-3

Abstract

Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiquitous in applications in contemporary science and engineering where the statistical ensemble prediction and the real time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test models have a crucial role to provide firm mathematical underpinning or new algorithms for vastly more complex scientific phenomena. Here, a class of statistically exactly solvable non-Gaussian test models is introduced, where a generalized Feynman-Kac formulation reduces the exact behavior of conditional statistical moments to the solution to inhomogeneous Fokker-Planck equations modified by linear lower order coupling and source terms. This procedure is applied to a test model with hidden instabilities and is combined with information theory to address two important issues in the contemporary statistical prediction of turbulent dynamical systems: the coarse-grained ensemble prediction in a perfect model and the improving long range forecasting in imperfect models. The models discussed here should be useful for many other applications and algorithms for the real time prediction and the state estimation.

Keywords

PredictionModel errorInformation theoryFeynman-Kac frameworkFokker planckTurbulent dynamical systems

2000 MR Subject Classification

60G2560H1060H3082C3194A15

Copyright information

© Fudan University and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA