Improving Prediction Skill of Imperfect Turbulent Models Through Statistical Response and Information Theory

Research output: Contribution to journalArticle

Abstract

Turbulent dynamical systems with a large phase space and a high degree of instabilities are ubiquitous in climate science and engineering applications. Statistical uncertainty quantification (UQ) to the response to the change in forcing or uncertain initial data in such complex turbulent systems requires the use of imperfect models due to the lack of both physical understanding and the overwhelming computational demands of Monte Carlo simulation with a large-dimensional phase space. Thus, the systematic development of reduced low-order imperfect statistical models for UQ in turbulent dynamical systems is a grand challenge. This paper applies a recent mathematical strategy for calibrating imperfect models in a training phase and accurately predicting the response by combining information theory and linear statistical response theory in a systematic fashion. A systematic hierarchy of simple statistical imperfect closure schemes for UQ for these problems is designed and tested which are built through new local and global statistical energy conservation principles combined with statistical equilibrium fidelity. The forty mode Lorenz 96 (L-96) model which mimics forced baroclinic turbulence is utilized as a test bed for the calibration and predicting phases for the hierarchy of computationally cheap imperfect closure models both in the full phase space and in a reduced three-dimensional subspace containing the most energetic modes. In all of phase spaces, the nonlinear response of the true model is captured accurately for the mean and variance by the systematic closure model, while alternative methods based on the fluctuation-dissipation theorem alone are much less accurate. For reduced-order model for UQ in the three-dimensional subspace for L-96, the systematic low-order imperfect closure models coupled with the training strategy provide the highest predictive skill over other existing methods for general forced response yet have simple design principles based on a statistical global energy equation. The systematic imperfect closure models and the calibration strategies for UQ for the L-96 model serve as a new template for similar strategies for UQ with model error in vastly more complex realistic turbulent dynamical systems.

Original languageEnglish (US)
Pages (from-to)233-285
Number of pages53
JournalJournal of Nonlinear Science
Volume26
Issue number1
DOIs
StatePublished - Feb 1 2016

Fingerprint

Information theory
Information Theory
Uncertainty Quantification
Imperfect
Prediction
Closure
Phase Space
Dynamical system
Model
Dynamical systems
Calibration
Subspace
Fluctuation-dissipation Theorem
Three-dimensional
Model Error
Reduced Order Model
Skills
Nonlinear Response
Coupled Model
Energy Conservation

Keywords

  • Information metric
  • Linear response theory
  • Low-order statistical closure models
  • Turbulent systems

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Engineering(all)

Cite this

Improving Prediction Skill of Imperfect Turbulent Models Through Statistical Response and Information Theory. / Majda, Andrew J.; Qi, Di.

In: Journal of Nonlinear Science, Vol. 26, No. 1, 01.02.2016, p. 233-285.

Research output: Contribution to journalArticle

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