arXiv：cs.LG· Li Zhang, Jun Wang, Isidora Jankov, Yongxin Liu, Gonzalo A. Ferrada, Ravan Ahmadov, Ligia Bernardet, Haonan Chen, Shobha Kondragunta·· 4 小时前
图神经网络实现全球每日火灾辐射功率中程预测
A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times
AI 导读
研究团队开发了一种时空图神经网络模型，基于再分析气象数据、土地覆盖与植被信息及近期火灾历史，预测未来1至7天的全球火灾辐射功率（FRP）。模型以GBBEPx FRP为训练目标，用2020-2022年数据训练、2023-2024年评估。
正文
Abstract: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is available with about a 1.5-day latency, so each forecast cycle relies on the most recently available, but already outdated, fire observations.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 days ahead from the most recent available observations. The model adapts a spatiotemporal graph neural network using reanalysis meteorology, land-cover and vegetation information, recent fire history, and GBBEPx FRP as the training target. It is trained on 2020-2022 data and evaluated for 2023-2024. The model reproduces the global seasonal cycle and substantially outperforms persistence. At 0.1$^\circ$ resolution, mean squared error is reduced by 32% at one-day lead and 43% at seven days in 2023, and by 24% and 40% in 2024. At 1$^\circ$ resolution, the critical success index ranges from 0.32 to 0.60. Detection skill declines only modestly with lead time, whereas intensity skill degrades more rapidly. Large fires are detected reliably, but their radiative power is systematically underestimated. These results demonstrate useful predictability of fire activity several days ahead and identify intensity calibration and small-fire placement as the main remaining challenges before predicted FRP can support operational aerosol forecasts.
| Comments: | Submitted to Artificial Intelligence for the Earth Systems |
| Subjects: | Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11022 [physics.ao-ph] |
| (or arXiv:2610.11022v1 [physics.ao-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11022 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Li Zhang [view email]
[v1]
Thu, 8 Oct 2026 00:14:28 UTC (9,831 KB)
来源：arXiv：cs.LG · arxiv.org