Data-Enabled Modeling of Wildfire Smoke Transport

野火烟雾输送的数据建模

• 批准号: 2111585
• 负责人: Donna Calhoun
• 金额: $ 54.97万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111585&HistoricalAwards=false
• 关键词: Data; Enabled; Modeling; Wildfire; Smoke

In the Western United States, Australia and many other parts of the world, wildfires are now a seasonal occurrence. Wildfires emit pollutants into the air creating poor air quality that is hazardous to people’s health and the environment. Communities use results from high resolution global scale simulations of wildfire smoke to prepare for poor air quality. This project will quantify the uncertainty in operational smoke forecasts due to incomplete knowledge of the smoke plume, wind and other weather conditions. Uncertainty estimates provide a more complete understanding of smoke forecasts, and can be communicated along with the predictions. These estimates have the potential to improve weather prediction models that are affected by smoke, and planning efforts by rural and downstream communities. This project will support two graduate students and one undergraduate student per year for each year of the three year project. Weak constraint four dimensional data assimilation (4DVAR) will be implemented to combine wind field, emission and concentration data with a partial differential equation that describes transport of PM2.5 concentrations generated by wildfire smoke. Data from numerical weather prediction (NWP) models, including NCEP and EMCWF, smoke emission models from NOAA and US Forest service, and concentration data from EPA will be used. The representer method will be developed for 4DVAR to reduce the search space for the optimal estimates from the state space to the data space. The computational cost of 4DVAR will be further improved by developing algorithmic advances for adaptive mesh refinement (AMR) in parallel with storage and checkpointing of adjoints. Approximation of the Dirac delta distributions, appearing in the adjoint method, will be improved with a new formulation inspired by the Immersed Boundary Method. Estimates of PM2.5 concentration, wind field and emission estimates arising in the transport model will fit observations within specified error covariances. This data assimilation procedure will quantify the uncertainty in operational smoke forecasts from historical wildfire events which can be used to estimate uncertainty in smoke forecasts for future wildfire events.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.

在美国西部、澳大利亚和世界许多其他地区，野火现在是季节性发生的。 野火向空气中排放污染物，造成空气质量下降，危害人们的健康和环境。 社区利用野火烟雾的高分辨率全球规模模拟结果来应对空气质量不佳的情况。该项目将量化由于对烟羽、风和其他天气条件的不完全了解而导致的烟雾预报的不确定性。 不确定性估计可以让您更全面地了解烟雾预测，并且可以与预测一起传达。这些估计有可能改善受烟雾影响的天气预报模型以及农村和下游社区的规划工作。该项目将在三年项目中每年支持两名研究生和一名本科生。将实施弱约束四维数据同化 (4DVAR)，将风场、排放和浓度数据与描述野火烟雾产生的 PM2.5 浓度传输的偏微分方程相结合。将使用来自数值天气预报 (NWP) 模型（包括 NCEP 和 EMCWF）的数据、NOAA 和美国林务局的烟雾排放模型以及 EPA 的浓度数据。 将为 4DVAR 开发表示方法，以减少从状态空间到数据空间的最佳估计的搜索空间。 通过开发自适应网格细化 (AMR) 的算法进步以及伴随的存储和检查点，4DVAR 的计算成本将得到进一步改善。 伴随法中出现的狄拉克 δ 分布的近似值将通过受浸入边界法启发的新公式得到改进。 运输模型中产生的 PM2.5 浓度、风场和排放量的估计值将符合指定误差协方差内的观测值。 该数据同化程序将量化历史野火事件烟雾预报的不确定性，可用于估计未来野火事件烟雾预报的不确定性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。