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Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain

Author

Listed:
  • Jorge Castillo-Mateo

    (University of Zaragoza)

  • Miguel Lafuente

    (University of Zaragoza)

  • Jesús Asín

    (University of Zaragoza)

  • Ana C. Cebrián

    (University of Zaragoza)

  • Alan E. Gelfand

    (Duke University)

  • Jesús Abaurrea

    (University of Zaragoza)

Abstract

Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Jorge Castillo-Mateo & Miguel Lafuente & Jesús Asín & Ana C. Cebrián & Alan E. Gelfand & Jesús Abaurrea, 2022. "Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 487-505, September.
    • Handle: RePEc:spr:jagbes:v:27:y:2022:i:3:d:10.1007_s13253-022-00493-3
    DOI: 10.1007/s13253-022-00493-3
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    References listed on IDEAS

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    1. Sha Li & Daniel A. Griffith & Hong Shu, 2020. "Temperature prediction based on a space–time regression-kriging model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(7), pages 1168-1190, May.
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    Cited by:

    1. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.
    2. Jorge Castillo-Mateo & Jesús Asín & Ana C. Cebrián & Jesús Mateo-Lázaro & Jesús Abaurrea, 2023. "Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature," Mathematics, MDPI, vol. 11(3), pages 1-19, February.

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