Interpolation of daily rainfall data using censored Bayesian spatially varying model

K. Shuvo Bakar ()
Additional contact information
K. Shuvo Bakar: Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Computational Statistics, 2020, vol. 35, issue 1, No 10, 135-152

Abstract: Abstract This paper illustrates a Bayesian spatially varying space-time model to analyse daily precipitation in the Murray Darling Basin (MDB) of Australia. The model is capable of addressing data censoring at zero rainfall days using a latent Gaussian spatial field. The model is also useful for analysing data obtained from sparse locations, where spatially varying parameters are used to interpolate the rainfall estimates in high-density grids across the MDB region. We find that the proposed censored method provides a better out-of-sample predictive performance for the daily rainfall measurements compared to a non-truncated method. Additionally, it identifies location specific trends over the study region. The model is constructed under the Bayesian paradigm and the Markov chain Monte Carlo algorithm is used to obtain inferences of the model parameters. The R software spTDyn is hence extended to version 2.0 to implement the model.

Keywords: Hierarchical spatially varying model; Latent censored Gaussian model; Bayesian inference (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00180-019-00911-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00911-0

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-019-00911-0

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().