Quantifying uncertainty associated with a numerical model output

Lucia Paci, D Cocchi, A. Gelfand

Research output: Chapter in Book/Report/Conference proceedingChapter


Environmental numerical models are deterministic tools widely used to simulate and predict complex systems. However, they are unsatisfying since they do not provide information about the uncertainty associated with their predictions. Conversely, uncertainty assessment of model outputs can be useful to guide environmental agencies in improving computer models. We propose a Bayesian hierarchical model to obtain spatially varying uncertainty associated with a numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. The model is illustrated by providing the uncertainty map associated with a temperature output over the northeastern United States.
Original languageEnglish
Title of host publicationProceedings of SIS 2014
Number of pages6
Publication statusPublished - 2014


  • Bayesian inference
  • Hierarchical modeling
  • data fusion
  • logCAR prior
  • measurement error model


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