用新型滤波算法提升野火蔓延实时预测精度
Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter
- 引入基于扩散模型的集合评分滤波器，融合遥感观测与数值预测
- 在真实野火场景下实现更高精度、更稳定且计算更快的预测
- 适合需要高精度实时预测的消防与应急管理部门使用
随着野火破坏性加剧、扑救成本上升，精准高效的实时火势蔓延预测对有效管控至关重要。数据同化通过融合遥感等观测数据与数值模型预测结果，在提升预测精度方面发挥关键作用。本文系统研究了一种新提出的基于扩散模型的滤波算法——集合评分滤波器（EnSF）在实时野火蔓延数据同化中的应用。该方法利用基于得分的生成式扩散模型，已在高维非线性滤波问题中展现出优越性能，特别适用于复杂野火模型的滤波任务。文中提供了详细技术实现，并通过数值实验验证：EnSF在准确性、稳定性与计算效率上均表现优异，证明其是野火数据同化中一种鲁棒且实用的新方法。相关代码已公开。
原文摘要 · Abstract (English)
As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation plays a vital role by integrating observations (such as remote-sensing data) and fire predictions generated from numerical models. This paper provides a comprehensive investigation on the application of a recently proposed diffusion-model-based filtering algorithm -- the Ensemble Score Filter (EnSF) -- to the data assimilation problem for real-time active wildfire spread predictions. Leveraging a score-based generative diffusion model, EnSF has been shown to have superior accuracy for high-dimensional nonlinear filtering problems, making it an ideal candidate for the filtering problems of wildfire spread models. Technical details are provided, and our numerical investigations demonstrate that EnSF provides superior accuracy, stability, and computational efficiency, establishing it as a robust and practical method for wildfire data assimilation. Our code has been made publicly available.
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