Quantifying uncertainty associated with a numerical model output

Lucia Paci, D Cocchi, A. Gelfand

Risultato della ricerca: Contributo in libroChapter


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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings of SIS 2014
Numero di pagine6
Stato di pubblicazionePubblicato - 2014


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


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