Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio-temporal models.

Many environmental spatio-temporal processes are best characterized by nonlinear dynamical evolution. Recently, it has been shown that general quadratic nonlinear models provide a very flexible class of parametric models for such processes. However, such models have a very large potential parameter space that must be reduced for most practical applications, even when one considers a reduced rank state process. We provide a parameterization for such models, which is motivated by physical arguments of wave mode interactions in which medium scales influence the evolution of large-scale modes. This parameterization has the potential to improve forecasts in addition to reducing the parameter space. The methodology is illustrated on real-world forecasting problems associated with Pacific sea surface temperature anomalies and mid-latitude sea level pressure. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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