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Multisite Weather Generators Using Bayesian Networks: An Illustrative Case Study for Precipitation Occurrence

Abstract: ABSTRACT: Many existing approaches for multisite weather generation try to capture several statistics of the observed data (e.g. pairwise correlations) in order to generate spatially and temporarily consistent series. In this work we analyse the application of Bayesian networks to this problem, focusing on precipitation occurrence and considering a simple case study to illustrate the potential of this new approach. We use Bayesian networks to approximate the multi-variate (-site) probability distribution of observed gauge data, which is factorized according to the relevant (marginal and conditional) dependencies. This factorization allows the simulation of synthetic samples from the multivariate distribution, thus providing a sound and promising methodology for multisite precipitation series generation.

 Fuente: Water Resources Research July 2020 Volume56, Issue7 e2019WR026416

 Editorial: American Geophysical Union

 Fecha de publicación: 01/07/2020

 Nº de páginas: 18

 Tipo de publicación: Artículo de Revista

 DOI: 10.1029/2019WR026416

 ISSN: 0043-1397,1944-7973

 Proyecto español: CGL2015-66583- R