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.
(2014). Addressing the change of support problem with non Gaussian distributions:an application to hourly rainfall prediction [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/31677
Addressing the change of support problem with non Gaussian distributions: an application to hourly rainfall prediction
2014-01-01
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.File | Dimensione del file | Formato | |
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