The level‐set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high‐order level‐set method using fifth‐order WENO scheme for the discretization of spatial derivatives and third‐order explicit Runge‐Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF‐Fire. The algorithm includes solution of an additional partial differential equation for level‐set reinitialization. The accuracy of the fire‐front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level‐set‐based wildfire models yields to rate‐of‐spread errors in the range 10–35% for typical grid sizes (Δ = 12.5–100 m) and considerably underestimates fire area. Moreover, the amplitude of fire‐front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF‐Fire algorithm results in rate‐of‐spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid‐order level‐set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high‐order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high‐order accurate level‐set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.

中文翻译：

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使用水平集方法的天气研究和预报模型中的精确扩火算法

水平设置方法通常用于在野外火灾模型中跟踪和传播火灾范围。在本文中，在天气研究和预报模型野地火物理学软件包WRF-Fire中实现了使用五阶WENO方案进行空间导数离散化和三阶显式Runge-Kutta时间积分的高阶水平集方法。该算法包括用于水平集重新初始化的附加偏微分方程的解。系统分析了未耦合模拟中的火锋形状和蔓延速率的准确性。结果表明，基于水平集的野火模型使用的常见实现方式导致典型网格大小（Δ= 12.5–100 m）的扩展率误差在10–35％的范围内，并且大大低估了火场面积。而且，系统地低估了在明显分辨出湍流特征的情况下火前梯度的幅度。相比之下，新的WRF-Fire算法导致的扩展率误差低于1％，并且几乎与电网无关。此外，发现在火锋和侧翼之间的急剧过渡处，火区的低估会减少≈7倍。提出了一种局部降低人工黏度的混合阶水平集方法，该方法在保持精度的同时，极大地降低了与高阶离散化相关的计算成本。对Last Last Chance野火的仿真表明，在处理复杂的燃料异质性时，高阶精确水平设置算法具有其他优势，