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).
|Titolo della pubblicazione ospite||Classification, (Big) Data Analysis and Statistical Learning|
|Editor||F Mola, C Conversano, M Vichi|
|Numero di pagine||9|
|Stato di pubblicazione||Pubblicato - 2018|
- BICAR prior
- Hierarchical modeling
- Weather numerical forecasts