发表机构
CIRES, University of Colorado Boulder; Global Systems Laboratory, OAR/NOAA; NOAA/NWS/OMD; CIRA, Colorado State University; NOAA/NESDIS Center for Satellite Applications and Research（科罗拉多大学博尔德分校地球与大气研究学院; 美国国家海洋和大气管理局应用研究办公室全球系统实验室; 美国国家海洋和大气管理局国家天气预报办公室气象发展部; 科罗拉多州立大学空间天气与应用研究中心; 美国国家海洋和大气管理局环境卫星、数据和信息服务中心卫星应用与研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发时空图神经网络模型，利用多源数据提前1-7天预测全球火灾辐射功率，性能优于持续性预报，为气溶胶预报提供支撑。
AI 中文摘要
提前数天准确预测生物质燃烧活动对空气质量预报和气溶胶预测至关重要。两项业务约束推动了本研究：其一，用于初始化美国国家海洋和大气管理局（NOAA）GEFS-Aerosols的GBBEPx卫星火灾辐射功率（FRP）产品存在约1.5天的延迟，因此每个预报周期依赖的是最新但已过时的火灾观测数据；其二，这些火灾输入数据在后续5天的业务预报中保持固定，而在GSL实验系统中则为7天，相当于假设火灾活动无变化。我们开发了一种数据驱动模型，可基于最新可用观测数据提前1至7天预测全球FRP。该模型采用时空图神经网络，以再分析气象数据、土地覆盖与植被信息、近期火灾历史及GBBEPx FRP为训练目标，在2020-2022年数据上训练，2023-2024年数据上评估。该模型可复现全球季节循环，且性能显著优于持续性预报：在0.1°分辨率下，2023年提前1天的均方误差降低32%，提前7天降低43%；2024年分别降低24%和40%。在1°分辨率下，临界成功指数介于0.32至0.60之间。探测技能随提前期仅小幅下降，而强度技能下降更快；大火可被可靠探测，但其辐射功率被系统性低估。这些结果表明，火灾活动可提前数天进行有效预测，同时指出强度校准和小火定位是预测FRP支持业务气溶胶预报前需解决的主要挑战。
英文摘要
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is available with about a 1.5-day latency, so each forecast cycle relies on the most recently available, but already outdated, fire observations.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.
CommentsSubmitted to Artificial Intelligence for the Earth Systems
