Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio-temporal models
Physically motivated scale interaction parameterization in reduced rank quadratic nonlinear dynamic spatio-temporal models
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DOI：
10.1002/env.2266
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发表时间：
2014-06-01
期刊：
影响因子：
1.7
通讯作者：
Wikle, C. K.
中科院分区：
文献类型：
--
作者：
Gladish, D. W.;Wikle, C. K.
许多环境时空过程的最佳特征是非线性动力学演化。最近，研究表明，一般二次非线性模型为此类过程提供了一类非常灵活的参数模型。然而，此类模型具有非常大的潜在参数空间，即使在考虑减少的秩状态过程时，对于大多数实际应用也必须减少该空间。我们为此类模型提供了参数化，这是由波模相互作用的物理论证驱动的，其中中等尺度影响大尺度模态的演化。除了减少参数空间之外，这种参数化还有可能改善预测。该方法说明了与太平洋海面温度异常和中纬度海平面压力相关的现实世界预测问题。版权所有 (c) 2014 约翰·威利父子有限公司
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 (c) 2014 John Wiley & Sons, Ltd.