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

合作研究：了解空气质量模型的野火烟雾排放预测

• 批准号: 2011557
• 负责人: Amber Soja
• 金额: $ 5.51万
• 依托单位: National Institute of Aerospace Associates
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011557&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Predictions; Wildfire

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.

该项目将使用高分辨率模型与机器学习算法相结合，以改善对野火行为和蔓延的预测。气象预报、燃烧的燃料类型、地形地貌和消防工作的程度等变量将纳入分析。这项工作的目的是通过利用最近的实地活动和具有增强能力的高分辨率卫星提供的数据，提高对火灾行为的认识，将对多起火灾事件进行空气质量模拟，模拟使用由排放预测驱动的天气研究和化学预报模型。将使用WRF模型以及基于耦合大气-荒地火灾环境模型的火灾蔓延模型（统称为WRF火灾）预测排放量。来自北美西部最近两个夏季火灾季节（2018年7月至9月和2019年）的数据将被纳入分析。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？这些方法能否超越持久性预测？(2)这些方法在评估由这些排放物驱动的空气质量预测与烟雾的环境观测结果时有多熟练？(3)哪些因素（例如，火灾规模、燃料类型、天气条件、地形）控制方法的技能？(4)这些方法可以在什么时间尺度上准确预测排放量？每小时？每天？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，以及它们与以前开发的方法相比如何？该奖项反映了NSF的法定使命，并被认为是值得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的支持。