A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times
Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constraints motivate this work. First, the GBBEPx satellite fire radiative power (FRP) product used to initialize NOAA's GEFS-Aerosols is available with about a 1.5-day latency, so each forecast cycle relies on the most recently available, but already outdated, fire observations. Second, these fire inputs are then held fixed throughout the subsequent 5-day operational forecast, or 7 days in the GSL experimental system, effectively assuming no evolution in fire activity. We develop a data-driven model that predicts global FRP one to seven ...
| Authors | Li Zhang, Jun Wang, Isidora Jankov et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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