Spatially correlated temperature prediction using hierarchical Bayesian wavelet model†
Jiung Song1 · Kyeong Eun Lee2
12Department of Statistics, Kyungpook National University
Correspondence to: † This paper is a revised version of Jiung Song’s Master’s Thesis.
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: artlee@knu.ac.kr
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: artlee@knu.ac.kr
Received July 23, 2024; Revised August 13, 2024; Accepted August 19, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- Recently, abnormal temperature phenomena have been increasing all over the world. Especially in Korea, the temperature is greatly affected by geographical characteristics and seasonal air masses. This requires temperature measurements at more locations, but it is difficult for cost and geographic reasons. Commonly used Kriging analysis can reflect the spatial dependence of the surrounding area, but it isn’t easy to reflect the continuity of time. Therefore, in this study, we applied a hierarchical Bayesian model using discrete wavelet transforms (Song and Mallick, 2019) to temperature data to make predictions that reflect both temporal and spatial characteristics.
- Keywords : Global warming, hierarchical Bayesian model, temperature, wavelet
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