Multivariate Stochastic Downscaling for Semicontinuous Data

Lucia Paci*, Carlo Trivisano, Daniela Cocchi

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in libroChapter


The paper proposes a Bayesian hierarchical model to scale down and adjust deterministic weather model output of temperature and precipitation with meteorological observations, extending the existing literature along different directions. These non-independent data are used jointly into a stochastic calibration model that accounts for the uncertainty in the numerical model. Dependence between temperature and precipitation is introduced through spatial latent processes, at both point and grid cell resolution. Occurrence and accumulation of precipitation are considered through a two-stage spatial model due to the large number of zero measurements and the right-skewness of the distribution of positive rainfall amounts. The model is applied to data coming from the Emilia-Romagna region (Italy).
Lingua originaleEnglish
Titolo della pubblicazione ospiteClassification, (Big) Data Analysis and Statistical Learning
EditorF Mola, C Conversano, M Vichi
Numero di pagine9
Stato di pubblicazionePubblicato - 2018


  • BICAR prior
  • Hierarchical modeling
  • Precipitation
  • Temperature
  • Weather numerical forecasts


Entra nei temi di ricerca di 'Multivariate Stochastic Downscaling for Semicontinuous Data'. Insieme formano una fingerprint unica.

Cita questo