Forecasting skill of model averages

C. L. Winter, Doug Nychka

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Given a collection of science-based computational models that all estimate states of the same environmental system, we compare the forecast skill of the average of the collection to the skills of the individual members. We illustrate our results through an analysis of regional climate model data and give general criteria for the average to perform more or less skillfully than the most skillful individual model, the "best" model. The average will only be more skillful than the best model if the individual models in the collection produce very different forecasts; if the individual forecasts generally agree, the average will not be as skillful as the best model.

Original languageEnglish (US)
Pages (from-to)633-638
Number of pages6
JournalStochastic Environmental Research and Risk Assessment
Issue number5
StatePublished - 2010


  • Environmental models
  • Mean-square error
  • Model average
  • Model comparison
  • Stochastic processes
  • Uncertainty

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology
  • General Environmental Science


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