[2511.22169] Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.22169 (cs) [Submitted on 27 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization Authors:Inha Kang, Eunki Kim, Wonjeong Ryu, Jaeyo Shin, Seungjun Yu, Yoon-Hee Kang, Seongeun Jeong, Eunhye Kim, Soontae Kim, Hyunjung Shim View a PDF of the paper titled Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization, by Inha Kang and 9 other authors View PDF Abstract: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 ov...