Collaborative Research: Understanding Predictions of Wildfire Smoke Emissions for Air Quality Models

该项目将使用高分辨率模型与机器学习算法相结合，以提高对野火行为和蔓延的预测。天气预报、燃烧的燃料类型、地形和灭火工作的程度等变量将包括在分析中。这项工作旨在通过利用最近现场活动和具有增强能力的更高分辨率卫星的数据来提高对火灾行为的理解。将对多起火灾事件进行基于排放预测的使用天气研究和化学预测(WRF-Chem)模型的空气质量模拟。排放量将使用WRF模型和基于耦合大气-荒地火灾环境模型的火灾蔓延模型一起预测，统称为WRF-Fire。分析将包括北美西部最近两个夏季火灾季节(2018年7月至9月和2019年)的数据。这项研究将解决以下科学问题：(1)在空气质量预测的背景下，可以使用什么方法来有效地预测生物质燃烧排放？这些方法能够超越持续性预测吗？(2)这些方法在评估由这些排放驱动的空气质量预测与周围烟雾观测的对比时有多熟练？(3)哪些因素(例如，火的大小、燃料类型、天气条件、地形)控制了方法的技能？(4)这些方法在什么时间尺度上可以准确地预测排放？每小时？每天都有吗？最多提前多少天/小时？(5)这些方法能提供关于烟雾喷射高度的信息吗？它们与以前开发的方法相比如何？这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will use high-resolution models in combination with machine learning algorithms to improve the predictions of the behavior and spread of wildfires. Variables such as weather forecasts, the type of fuel burned, the topography of the landscape, and the extent of firefighting efforts will be included in the analyses. This effort is designed to improve the understanding of fire behavior by taking advantage of data from recent field campaigns and from higher-resolution satellites with enhanced capabilities.Air quality simulations using the Weather Research and Forecasting with Chemistry (WRF-Chem) model driven by emission predictions will be performed for multiple fire events. Emissions will be predicted using the WRF model together with a fire spread model based on the Coupled Atmosphere-Wildland Fire Environment model, together known as WRF-Fire. Data from the two most recent summer fire seasons (July-September 2018 and 2019) over western North America will be included in the analyses. This research will address the following science questions: (1) What methodologies can be used to effectively forecast biomass burning emissions in the context of air quality forecasts? Can these methodologies outperform persistence forecasts? (2) How skillful are these methodologies when evaluating air quality predictions driven by these emissions against ambient observations of smoke? (3) What factors (e.g., fire size, type of fuel, weather conditions, topography) control the skill of the methodologies? (4) At what time scales can the emissions be accurately predicted by these methodologies? Hourly? Daily? Up to how many days/hours in advance? (5) Can these methodologies provide information on the injection height of smoke and how do they compare to approaches previously developed?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.