Forecasting skill of model averages View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-07

AUTHORS

C. L. Winter, Doug Nychka

ABSTRACT

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. More... »

PAGES

633-638

References to SciGraph publications

  • 2003-11. Maximum likelihood Bayesian averaging of uncertain model predictions in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2003-11. Another look at the conceptual fundamentals of porous media upscaling in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2003-12. Random domain decomposition for flow in heterogeneous stratified aquifers in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2004-08. Long time-scale potential predictability in an ensemble of coupled climate models in CLIMATE DYNAMICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00477-009-0350-y

    DOI

    http://dx.doi.org/10.1007/s00477-009-0350-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018913838


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