Empirical Modeling of Unsteady Flows Available to Purchase

In this work we demonstrate the utility of stochastic empirical models. Linear stochastic models have been successful at reproducing responses to forcing of large-scale atmospheric flow. However, the linear forms of the models reach their limit on highly nonlinear problems. It is shown here that for simple low dimensional problems, a quadratically nonlinear empirical model is successful at reproducing a chaotic attractor. In addition, empirical models can be used to diagnose the important dynamics of a flow system and identify the essential terms to include in a forward dynamical model. Once the empirical model has been built, it can then be used to diagnose the underlying dynamics.