用深度学习量化臭氧模型误差，助力健康决策。
Uncertainty Quantification for Surface Ozone Emulators using Deep Learning
- 基于贝叶斯与分位数回归构建带不确定性评估的U-Net模型
- 在北美和欧洲2019年6月实现臭氧残差区域估测，误差可量化
- 识别出适合校正的观测站，揭示土地利用对残差的影响
空气污染是全球性健康威胁，2023年全球94%人口暴露于不安全污染水平。地表臭氧（O3）及其驱动因素难以建模，传统物理模型在影响人类健康的尺度上实用性不足。基于深度学习的代理模型虽能捕捉复杂气候模式，但缺乏可解释性，难以支持政策与公共卫生决策。本文采用带有不确定性感知的U-Net架构，结合贝叶斯与分位数回归方法，预测多模型多成分化学数据同化（MOMO-Chem）模型的地表臭氧残差（偏差）。在2019年6月的北美和欧洲区域，验证了该方法对偏差区域估计的能力。对比两种不确定性量化（UQ）方法的评分，识别出适合与不适合用于MOMO-Chem偏差修正的地面观测站，并评估土地利用信息在地表臭氧残差建模中的作用。
原文摘要 · Abstract (English)
Air pollution is a global hazard, and as of 2023, 94\% of the world's population is exposed to unsafe pollution levels. Surface Ozone (O3), an important pollutant, and the drivers of its trends are difficult to model, and traditional physics-based models fall short in their practical use for scales relevant to human-health impacts. Deep Learning-based emulators have shown promise in capturing complex climate patterns, but overall lack the interpretability necessary to support critical decision making for policy changes and public health measures. We implement an uncertainty-aware U-Net architecture to predict the Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) model's surface ozone residuals (bias) using Bayesian and quantile regression methods. We demonstrate the capability of our techniques in regional estimation of bias in North America and Europe for June 2019. We highlight the uncertainty quantification (UQ) scores between our two UQ methodologies and discern which ground stations are optimal and sub-optimal candidates for MOMO-Chem bias correction, and evaluate the impact of land-use information in surface ozone residual modeling.
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