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Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio‐temporal models
Author(s) -
Gladish D.W.,
Wikle C.K.
Publication year - 2014
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2266
Subject(s) - nonlinear system , quadratic equation , rank (graph theory) , scale (ratio) , parametric statistics , mathematics , statistical physics , computer science , mode (computer interface) , state space , mathematical optimization , physics , statistics , geometry , combinatorics , quantum mechanics , operating system
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.