Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE
Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE
复制标题
低强度火灾建模：2012 年 RxCADRE 的经验教训
DOI：
10.3390/atmos12020139
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发表时间：
2021
期刊：
影响因子：
2.9
通讯作者：
Scott L. Goodrick
中科院分区：
文献类型：
--
作者：
R. Linn;J. Winterkamp;James H. Furman;B. Williams;J. Hiers;A. Jonko;Joseph John O’Brien;Kara M. Yedinak;Scott L. Goodrick
耦合火灾-大气模型越来越多地用于研究低强度火灾，例如用于规定火灾应用的火灾。因此，需要评估这些模型准确代表边缘燃烧条件下火灾蔓延的能力。在这项研究中，风和燃料的规定火灾燃烧和大气动力学研究实验（RxCADRE）火灾活动期间收集的数据被用来生成耦合火灾-大气模拟的初始和边界条件。我们提出了一种新的方法来获得燃料表示在模型网格规模使用图像，机器学习和现场采样的组合。几种方法来产生风输入条件的模型，从八个不同的风速计测量进行了探讨。我们发现一个很强的灵敏度火灾的结果风输入。这一结果突出了关键需要包括可变的风场作为输入在模拟边缘火灾条件。这项工作突出了将基于物理的模型结果与观测结果进行比较的复杂性，这些结果在边际燃烧条件下更为严重，对风和燃料的局部变化敏感性更强，从而导致火灾结果。
Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.
将冠层参数化纳入耦合火焰-大气模型中，以改进指定燃烧的烟雾模拟
影响因子：
2.9
作者：
Mallia, Derek V.;Kochanski, Adam K.;Urbanski, Shawn P.;Mandel, Jan;Farguell, Angel;Krueger, Steven K.
通讯作者：
Krueger, Steven K.
通过 FireFlux 实验的现场观察评估 WRF-SFIRE 性能
影响因子：
5.1
作者：
Kochanski, A. K.;Jenkins, M. A.;Mandel, J.;Beezley, J. D.;Clements, C. B.;Krueger, S.
通讯作者：
Krueger, S.
优化局部尺度的烟雾和烟羽上升建模方法
影响因子：
2.9
作者：
Mallia, Derek;Kochanski, Adam;Urbanski, Shawn;Lin, John
通讯作者：
Lin, John