针对空气质量长期预报中误报多、实时性差的问题，提出新算法提升预警可靠性。
Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization
- 采用分组相对策略优化，按类别设计奖励机制，对不同预报错误差异化惩罚。
- 在东亚区域实现48-120小时实时预报，误报率降低47.3%，F1分数保持领先。
- 开源真实观测与高分辨率CMAQ-OBS数据集，助力本地化空气质量预警系统建设。
准确预测颗粒物（PM）浓度场的长期变化对公共健康决策至关重要。然而，在地形复杂、大气动力强的地区（如东亚），实现可靠预报仍具挑战。尽管像Aurora这样的基础模型具备全球泛化能力，但常忽略区域特异性动态，且依赖非实时输入，限制了其在本地预警系统中的实用性。为此，我们构建并发布了面向东亚的真实观测与高分辨率CMAQ-OBS数据集，将区域误差降低59.5%，支持48–120小时的实时预报，满足公共卫生预警需求。然而，传统逐点目标无法反映不对称的运营成本：误报削弱公众信任，漏报则危及生命。这种成本偏差导致监督微调（SFT）模型过度预测，造成高误报率。为此，我们提出分组相对策略优化（GRPO），引入类别奖励与课程式滚动策略，使预测更契合实际运行优先级。实验表明，相比仅使用SFT的基线，该框架将误报率降低47.3%，同时保持有竞争力的F1分数，验证了其在长提前期场景下实际应用的有效性。代码与数据集已公开于https://github.com/kaist-cvml/FAKER-Air。
原文摘要 · Abstract (English)
Accurate long horizon forecasting of particulate matter (PM) concentration fields is essential for operational public health decisions. However, achieving reliable forecasts remains challenging in regions with complex terrain and strong atmospheric dynamics such as East Asia. While foundation models such as Aurora offer global generality, they often miss region-specific dynamics and rely on non-real-time inputs, limiting their practical utility for localized warning systems. To address this gap, we construct and release the real-world observations and high-resolution CMAQ-OBS dataset for East Asia, reducing regional error by 59.5% and enabling real-time 48-120 hour forecasts critical for public health alerts. However, standard point-wise objectives cannot reflect asymmetric operational costs, where false alarms deteriorate public trust while missed severe events endanger populations. This cost mismatch causes SFT models to over-predict and yield high False Alarm Rates. We introduce Group-Relative Policy Optimization (GRPO) with class-wise rewards and curriculum rollout to align predictions with operational priorities. Experimental results demonstrate that our framework significantly improves the reliability of the forecast. Compared to the SFT-only baseline, our model reduces the False Alarm Rate by 47.3% while achieving a competitive F1-score, proving its effectiveness for practical, real-world air quality forecasting systems on long lead time scenarios. Code and dataset are publicly available at https://github.com/kaist-cvml/FAKER-Air.
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