标题： 基于总柱状臭氧的分层贝叶斯建模：利用卫星数据和多源协变量解析埃塞俄比亚赤道区域的变异性 显示英文标题

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

摘要： 了解总柱状臭氧 (TCO) 的时空动态对于监测紫外线 (UV) 暴露和臭氧趋势至关重要，尤其是在赤道地区，其中的变异性仍缺乏研究。本研究利用贝叶斯分层模型通过集成嵌套拉普拉斯近似 (INLA) 对埃塞俄比亚 (2012-2022) 的月度 TCO 进行了研究。该模型结合了九个环境协变量，涵盖了气象、平流层和地形影响以及空间时间随机效应。空间相关性使用随机偏微分方程 (SPDE) 方法进行建模，而时间自相关则通过自回归结构处理。该模型表现出强大的预测准确性，训练集的相关系数为 0.94，验证集的相关系数为 0.91，均方根误差分别为 3.91 DU 和 4.45 DU。太阳辐射、平流层温度和准双年振荡与 TCO 呈正相关，而地表温度、降水、湿度、水汽和海拔则呈负相关。随机效应突显了持续的区域集群以及夏季的季节性峰值。这些发现为复杂赤道地形上的区域臭氧行为提供了新的见解，有助于理解赤道臭氧悖论。该方法展示了将卫星观测与环境数据结合在数据稀缺地区的实用性，支持改进的紫外线风险监测和气候相关的政策规划。 显示英文摘要

摘要： 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.