Stochastic Modeling and Environmental Change
Published Online: 15 JAN 2013
Copyright © 2002 John Wiley & Sons, Ltd
Encyclopedia of Environmetrics
How to Cite
Fuentes, M. and Foley, K. 2013. Ensemble Models. Encyclopedia of Environmetrics. 2.
- Published Online: 15 JAN 2013
An ensemble, or sample, of competing numerical models has been used in many applications to represent different predictions of the true state of a physical system. Ensembles of computer models (e.g. weather, climate, air quality, ocean models) are often used to forecast future states of a physical system and to quantify uncertainty in the numerical model predictions. Various statistical methods have been proposed to improve ensemble predictions from deterministic computer simulations based on actual measurements of the physical systems (e.g. data assimilation, Bayesian model averaging). Ensemble data mining methods have also been developed for a wide variety of applications to combine different versions of a statistical model (e.g. time series models, simple regression models, neural networks) to improve the predictive model performance. We present different statistical criteria that have been proposed to select or weight ensemble members for both numerical model-based and statistical model-based ensembles.
- data assimilation;
- ensemble Kalman filter;
- Bayesian Model Averaging;
- ensemble data mining;