Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Xiaohua Pan; Kyo Hugo Lee; Nicholas LaHaye; Thilanka Munasinghe; Jennifer Wei; Gonzalo Gonzalez Abad; Hazem Mahmoud; Kyunghwa Lee

NASA NTRS · 20260005025

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Xiaohua Pan·Kyo Hugo Lee·Nicholas LaHaye·Thilanka Munasinghe·Jennifer Wei·Gonzalo Gonzalez Abad·Hazem Mahmoud·Kyunghwa Lee

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Abstract

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

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BibTeX RIS

Xiaohua Pan, Kyo Hugo Lee, Nicholas LaHaye, Thilanka Munasinghe, Jennifer Wei, Gonzalo Gonzalez Abad, Hazem Mahmoud, Kyunghwa Lee. Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions. https://ntrs.nasa.gov/citations/20260005025

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