A Bayesian hierarchical spatio-temporal rainfall model

被引：0

作者：

Mashford, John ^{[1
]}

Song, Yong ^{[2
]}

Wang, Q. J. ^{[2
,3
]}

Robertson, David ^{[2
]}

机构：

[1] Univ Melbourne, Sch Math & Stat, Parkville, Vic, Australia

[2] CSIRO, Div Land & Water, Clayton, Vic, Australia

[3] Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic 3010, Australia

来源：

JOURNAL OF APPLIED STATISTICS | 2019年 / 46卷 / 02期

关键词：

Time series; rainfall forecasting; sequential kriging; formal properties; rainfall versus elevation model; PRECIPITATION;

D O I：

10.1080/02664763.2018.1473347

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A Bayesian hierarchical spatio-temporal rainfall model is presented and analysed. The model has the ability to deal with extensive missing or null values, uses a sophisticated variance stabilising rainfall pre-transformation, incorporates a new elevation model and can provide sub-catchment rainfall estimation and interpolation using a sequential kriging scheme. The model uses a vector autoregressive stochastic process to represent the time dependence of the rainfall field and an exponential covariogram to model the spatial correlation of the rainfall field. The model can be readily generalised to other types of stochastic processes. In this paper, some results of applying the model to a particular rainfall catchment are presented.

引用

页码：217 / 229

页数：13

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