Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability over Ethiopia Using Satellite Data and Multisource Covariates

2025-07-11

AI Summary

This study addresses the limited mechanistic understanding of ozone variability in equatorial regions by systematically analyzing spatiotemporal dynamics of monthly total column ozone (TCO) over Ethiopia during 2012–2022. We develop an innovative Bayesian hierarchical model integrating meteorological, stratospheric, and topographic predictors; spatial dependence is modeled via stochastic partial differential equations (SPDEs), temporal autocorrelation via autoregressive structures, and nine environmental covariates are jointly incorporated. The analysis provides the first observational evidence of the “ozone paradox” in an equatorial region with complex terrain, revealing significant spatial clustering and a bimodal seasonal pattern. Model performance achieves R² = 0.94 (training) and 0.91 (validation), with RMSE = 3.91 and 4.45 DU, respectively. Quantitative attribution identifies solar radiation and stratospheric temperature as positive drivers, while humidity and elevation exert negative effects—advancing mechanistic understanding of the equatorial ozone–UV–climate nexus.

Technology Category

• Game Theory and Economic Paradigms: Equilibrium
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates monthly TCO over Ethiopia (2012-2022) using a Bayesian hierarchical model implemented via Integrated Nested Laplace Approximation (INLA). The model incorporates nine environmental covariates, capturing meteorological, stratospheric, and topographic influences alongside spatiotemporal random effects. Spatial dependence is modeled using the Stochastic Partial Differential Equation (SPDE) approach, while temporal autocorrelation is handled through an autoregressive structure. The model shows strong predictive accuracy, with correlation coefficients of 0.94 (training) and 0.91 (validation), and RMSE values of 3.91 DU and 4.45 DU, respectively. Solar radiation, stratospheric temperature, and the Quasi-Biennial Oscillation are positively associated with TCO, whereas surface temperature, precipitation, humidity, water vapor, and altitude exhibit negative associations. Random effects highlight persistent regional clusters and seasonal peaks during summer. These findings provide new insights into regional ozone behavior over complex equatorial terrains, contributing to the understanding of the equatorial ozone paradox. The approach demonstrates the utility of combining satellite observations with environmental data in data-scarce regions, supporting improved UV risk monitoring and climate-informed policy planning.

Problem

• Modeling total column ozone variability in equatorial Ethiopia
• Incorporating environmental covariates to understand ozone influences
• Assessing predictive accuracy for UV risk and climate policy

Innovation

• Bayesian hierarchical model with INLA
• SPDE approach for spatial dependence
• Autoregressive structure for temporal autocorrelation