@article{10902/19910,
year = {2020},
month = {7},
url = {http://hdl.handle.net/10902/19910},
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.},
organization = {We acknowledge funding provided by the project MULTI‐SDM (CGL2015‐ 66583‐R, MINECO/FEDER).},
publisher = {American Geophysical Union},
publisher = {Water Resources Research July 2020 Volume56, Issue7 e2019WR026416},
title = {Multisite Weather Generators Using Bayesian Networks: An Illustrative Case Study for Precipitation Occurrence},
author = {Legasa Ríos, Mikel Néstor and Gutiérrez Llorente, José Manuel},
}