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区，野火现在是季节性的。 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。 社区使用高分辨率全球规模野火烟雾模拟的结果来为恶劣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导致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，并且可以与预测一起沿着进行通信。这些估计有可能改善受烟雾影响的天气预测模型，以及农村和下游社区的规划工作。该项目将支持两名研究生和一名本科生，每年为三年的项目。将实施弱约束四维数据同化（4DVAR），将联合收割机风场、排放和浓度数据与描述野火烟雾产生的PM2.5浓度输送的偏微分方程相结合。将使用数值天气预报（NWP）模式（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。 将代表方法发展为4DVAR，以减少搜索空间的最佳估计从状态空间到数据空间。 4DVAR的计算成本将进一步改善的自适应网格细化（AMR）的并行存储和检查点的伴随开发算法的进步。 狄拉克δ分布的近似，出现在伴随方法，将得到改进，一个新的配方的启发浸没边界法。 PM2.5浓度的估计，风场和排放量的估计在运输模式中产生的将适合在指定的误差协方差的观测。 这个数据同化程序将量化的不确定性，从历史的野火事件，可用于估计未来野火事件的烟雾预报的不确定性，这反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.