Data-Enabled Modeling of Wildfire Smoke Transport

在美国西部、澳大利亚和世界上许多其他地方，野火现在是一种季节性事件。野火向空气中排放污染物，造成空气质量差，对人们的健康和环境有害。社区利用高分辨率全球尺度野火烟雾模拟的结果为恶劣的空气质量做准备。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业务烟雾预测的不确定性。不确定性估计提供了对烟雾预报更全面的了解，并且可以与预报一起传达。这些估计有可能改善受烟雾影响的天气预报模型，并改善农村和下游社区的规划工作。在为期三年的项目中，每年将资助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结合起来。数值天气预报（NWP）模式的数据，包括NCEP和EMCWF，来自NOAA和美国林业局的烟雾排放模式，以及来自EPA的浓度数据将被使用。为了减少从状态空间到数据空间的最优估计的搜索空间，将开发用于4DVAR的表征方法。随着自适应网格细化（AMR）算法与伴随结点存储和检查点的并行发展，4DVAR的计算成本将进一步提高。伴随法中出现的狄拉克三角洲分布的近似，将用浸入边界法的新公式加以改进。在运输模式中产生的PM2.5浓度、风场和排放估计值将在指定的误差协方差内拟合观测值。这种数据同化过程将量化历史野火事件中烟雾预报的不确定性，这些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。

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.