Bayesian inference for wind field retrieval

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Abstract

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

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Details

Original languageEnglish
Pages (from-to)3-11
Number of pages9
JournalNeurocomputing
Volume30
Issue1-4
DOIs
StatePublished - Jan 2000

    Keywords

  • Bayesian inference, surface winds, spatial priors, Gaussian processes

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Employable Graduates; Exploitable Research

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