Standard Article

Ensemble Models

Stochastic Modeling and Environmental Change

  1. Montserrat Fuentes1,
  2. Kristen Foley2

Published Online: 15 JAN 2013

DOI: 10.1002/9780470057339.vnn066

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Fuentes, M. and Foley, K. 2013. Ensemble Models. Encyclopedia of Environmetrics. 2.

Author Information

  1. 1

    North Carolina State University, Raleigh, NC, USA

  2. 2

    National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

Publication History

  1. Published Online: 15 JAN 2013

Abstract

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.

Keywords:

  • data assimilation;
  • ensemble Kalman filter;
  • Bayesian Model Averaging;
  • ensemble data mining;
  • bagging;
  • boosting