We address the change of support problem in a non Gaussian model; in particular, we predict occurrence and accumulation of hourly rainfall in the Emilia-Romagna region by modeling the relationship between rain gauge and radar data. A basic formulation exploits radar information only at the grid cells containing rain gauges locations; an enrichment consists in exploiting neighbourhood information via a weighted mean of a latent spatial process for addressing spatial misalignment. A Bayesian hierarchical model is specified for each formulation, both sharing a zero-inflated likelihood, and with gamma distribution on the positive semiaxis. Results are assessed and compared in terms of point and probabilistic forecasts.
Addressing the change of support problem with non Gaussian distributions: an application to hourly rainfall prediction
BRUNO, FRANCESCA;COCCHI, DANIELA;GRECO, FEDELE PASQUALE;SCARDOVI, ELENA
2014
Abstract
We address the change of support problem in a non Gaussian model; in particular, we predict occurrence and accumulation of hourly rainfall in the Emilia-Romagna region by modeling the relationship between rain gauge and radar data. A basic formulation exploits radar information only at the grid cells containing rain gauges locations; an enrichment consists in exploiting neighbourhood information via a weighted mean of a latent spatial process for addressing spatial misalignment. A Bayesian hierarchical model is specified for each formulation, both sharing a zero-inflated likelihood, and with gamma distribution on the positive semiaxis. Results are assessed and compared in terms of point and probabilistic forecasts.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.