Data-Enabled Modeling of Wildfire Smoke Transport

在美国西部、澳大利亚和世界其他许多地区，野火现在是季节性的。野火将污染物排放到空气中，造成糟糕的空气质量，对人们的健康和环境造成危害。社区使用高分辨率全球范围的野火烟雾模拟结果，为糟糕的空气质量做好准备。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造成的业务烟雾预报的不确定性。不确定性估计提供了对烟雾预报的更完整的理解，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，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资助两名研究生和一名本科生。弱约束四维数据同化(4DVAR)将风场、排放和浓度资料与描述野火烟雾产生的PM2.5浓度输送的偏微分方程结合在一起。将使用数值天气预报(NWP)模型的数据，包括NCEP和EMCWF，NOAA和美国林业局的烟雾排放模型，以及EPA的浓度数据。提出了4DVAR的代表者方法，将最优估计的搜索空间从状态空间缩减到数据空间。通过开发与伴随项存储和检查点并行的自适应网格细化(AMR)算法，将进一步提高4DVAR的计算成本。在伴随方法中出现的狄拉克三角洲分布的近似将被浸没边界方法启发的新公式所改进。在传输模式中产生的对PM2.5浓度、风场和排放的估计将在指定的误差协方差内与观测值相吻合。该数据同化程序将量化来自历史野火事件的业务烟雾预测的不确定性，可用于估计未来野火事件的烟雾预测的不确定性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.