Hierarchical Bayesian modeling of Atlantic storms based on the sea surface temperature field†
Nyamsuren Batsuren1 · Seunghyun Hong2 · Yongku Kim3
123Department of Statistics, Kyungpook National University
3KNU G-LAMP Project Group, KNU Institute of Basic Sciences, Kyungpook National University
3KNU G-LAMP Project Group, KNU Institute of Basic Sciences, Kyungpook National University
Correspondence to: † This research was supported by Global-Learning & Academic research institution for Master’s·PhD students, and Postdocs (G-LAMP) Program of the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. RS-2023-00301914).
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
2 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
3 Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: kim.1252@knu.ac.kr
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
2 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
3 Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: kim.1252@knu.ac.kr
Received August 6, 2024; Revised August 19, 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
- The increasing intensity and frequency of Atlantic tropical storms and hurricanes in recent decades have underscored the need for a deeper understanding of the factors influencing these events. The complex interplay between oceanic and atmospheric conditions, particularly sea surface temperatures (SSTs), has been widely recognized as a key driver of hurricane activity. In this paper, we introduce a novel statistical model that explores the relationship between Atlantic tropical storm occurrences and climate factors, with a particular focus on the spatial variability inherent in the climate system. By employing a hierarchical Bayesian modeling framework and incorporating key climate predictors such as global surface temperature, the North Atlantic Oscillation, and the Atlantic Multidecadal Oscillation, we aim to capture the dynamic and spatially heterogeneous nature of the factors influencing hurricane activity. Our model, utilizing climate data from 1900 to 2002, demonstrates the ability to explain a significant portion of the trends and variability in Atlantic tropical storm activity. The results highlight the importance of considering spatial dynamics in understanding and predicting hurricane occurrences.
- Keywords : Atlantic tropical storm, Bayesian analysis, hierarchical modeling, sea surface temperatures
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