An autoregressive spatio-temporal precipitation model

被引：6

作者：

Sigrist, Fabio ^{[1
]}

Kuensch, Hans R. ^{[1
]}

Stahel, Werner A. ^{[1
]}

机构：

[1] ETH, Seminar Stat, CH-8092 Zurich, Switzerland

来源：

1ST CONFERENCE ON SPATIAL STATISTICS 2011 - MAPPING GLOBAL CHANGE | 2011年 / 3卷

关键词：

Precipitation modeling; Space-time model; Bayesian hierarchical model; Markov chain Monte Carlo (MCMC) method; Censoring; Gaussian random field; LIKELIHOOD-ESTIMATION; COVARIANCE FUNCTIONS; TIME; RAINFALL;

D O I：

10.1016/j.proenv.2011.02.002

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

A spatio-temporal model for precipitation is presented. It is assumed that precipitation follows a censored and power-transformed normal distribution. Through a regression term, precipitation is linked to covariates. Spatial and temporal dependencies are accounted for by a latent Gaussian variable that follows a Markovian temporal evolution combined with spatially correlated innovations. Such a specification allows for nonseparable covariances in space and time. Further, the Markovian structure yields computational efficiency and it exploits in a natural way the unidirectional flow of time. In addition, the model is space as well as time resolution consistent. The model is applied to three-hourly Swiss rainfall data, collected at 26 stations.

引用

页码：2 / 7

页数：6

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