Multivariate Stochastic Downscaling for Semicontinuous Data

Lucia Paci*, Carlo Trivisano, Daniela Cocchi

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in libroCapitolo

Abstract

The paper proposes a Bayesian hierarchical model to scale down and\r\nadjust deterministic weather model output of temperature and precipitation with\r\nmeteorological observations, extending the existing literature along different directions.\r\nThese non-independent data are used jointly into a stochastic calibration model\r\nthat accounts for the uncertainty in the numerical model. Dependence between temperature\r\nand precipitation is introduced through spatial latent processes, at both point\r\nand grid cell resolution. Occurrence and accumulation of precipitation are considered\r\nthrough a two-stage spatial model due to the large number of zero measurements\r\nand the right-skewness of the distribution of positive rainfall amounts. The model is\r\napplied to data coming from the Emilia-Romagna region (Italy).
Lingua originaleInglese
Titolo della pubblicazione ospiteClassification, (Big) Data Analysis and Statistical Learning
EditoreSpringer
Pagine107-115
Numero di pagine9
ISBN (stampa)978-3-319-55707-6
DOI
Stato di pubblicazionePubblicato - 2018

All Science Journal Classification (ASJC) codes

  • Informatica Applicata
  • Sistemi Informativi
  • Sistemi Informativi e Gestione dell’Informazione
  • Analisi

Keywords

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

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