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Interpolation of daily rainfall data using censored Bayesian spatially varying model

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  • K. Shuvo Bakar

    (Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO))

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.

Suggested Citation

  • K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00911-0
    DOI: 10.1007/s00180-019-00911-0
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    Cited by:

    1. Rajni Goel & Hare Krishna, 2022. "Statistical inference for two Lindley populations under balanced joint progressive type-II censoring scheme," Computational Statistics, Springer, vol. 37(1), pages 263-286, March.

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