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，以改进对野火行为和蔓延的预测。诸如天气预报、燃烧的燃料类型、地形地貌和消防努力程度等变量将包括在分析中。这项工作的目的是通过利用最近现场战役的数据和增强功能的高分辨率卫星来提高对火灾行为的理解。利用化学天气研究与预报（WRF-Chem）模式，在排放预测的驱动下，对多个火灾事件进行空气质量模拟。排放将使用WRF模型和基于大气-林地火灾-环境耦合模型（称为WRF- fire）的火灾蔓延模型进行预测。北美西部最近两个夏季火灾季节（2018年7月至9月和2019年9月）的数据将包括在分析中。本研究将解决以下科学问题：(1)在空气质量预测的背景下，什么方法可以用来有效地预测生物质燃烧排放？这些方法能胜过持续性预测吗？(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结果时是否熟练？(3)哪些因素（例如，火灾规模、燃料类型、天气条件、地形）控制了方法的技巧？(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?每天吗?最多提前多少天/小时？(5)这些方法能否提供有关烟雾喷射高度的信息？与以前开发的方法相比如何？该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.