Computational natural wildfire simulation is a computing-intensive process. The process is also challenging because of the need to integrate data with wide spatial and temporal variability. Our study sought to simulate rapidly spreading natural wildfire with fidelity and quality through computational realization. We developed a novel probabilistic wildfire risk assessment tool whose operation was driven by real-time wildfire observations. A Gaussian transformation incorporating present and historical geographical data to the wildfire model was adopted to accommodate scale differences in the datasets. The model outputs, therefore, depict possible spread pathways using Monte Carlo simulations. We created a computational solution for deploying wildfire simulations to a highly scalable, distributed and parallel computing framework, which facilitated a fairly linear increase in the simulation run time as the computation load increased exponentially. Our computational solution synthesized and fully automated the various stages of the process, from data preparation to analysis and visualization. The platform can potentially provide real-time decision-making support to wildfire hazard management.

计算自然野火模拟是一个计算密集型过程。由于需要集成具有广泛的空间和时间可变性的数据，因此该过程也具有挑战性。我们的研究试图通过计算实现以逼真度和质量模拟快速传播的自然野火。我们开发了一种新颖的概率性野火风险评估工具，其操作由实时野火观测驱动。采用高斯变换，将当前和历史地理数据合并到野火模型中，以适应数据集中的比例差异。因此，模型输出使用蒙特卡洛模拟来描述可能的传播途径。我们创建了一个计算解决方案，用于将野火模拟部署到高度可扩展的分布式并行计算框架中，随着计算负载呈指数增长，这促进了仿真运行时间的线性增长。从数据准备到分析和可视化，我们的计算解决方案综合了流程的各个阶段并使之完全自动化。该平台可以为野火灾害管理提供实时决策支持。