Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary observations Atmospheric Chemistry and Physics

Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings. Grant no. NA16OAR4590121

野火烟气预报长期受困于火灾排放估算的高度不确定性。我们开发了一套基于HYSPLIT的野火排放反演建模系统（HYSPLIT-based Emissions Inverse Modeling System for wildfires，简称HEIMS-fire），该系统可通过卫星观测获取的烟气羽流输送与扩散数据反演野火排放量。该系统通过代价函数量化模型预测结果与卫星观测值之间的差异，并以二者的不确定性作为权重进行加权。随后，系统通过调整烟气排放源来最小化该代价函数，直至野火烟气排放估算结果与卫星观测数据高度吻合。本系统依托HYSPLIT模型与地球静止业务环境卫星（Geostationary Operational Environmental Satellite, GOES）气溶胶/烟气产品（Aerosol/Smoke Product, GASP），可将烟气排放源强度解析为时间与垂直高度的函数。我们以2016年11月美国东南部发生的一场野火事件为研究对象，测试了该系统的性能，以及其对不同建模配置选项的敏感性，其中包括排放物的垂直分配方案，以及约束性卫星观测的时空覆盖范围。相较于当前业务化运行的BlueSky排放预测结果，本反演建模系统得到的排放估算结果在再分析模式（21天全部达标；平均均方根误差降低27%）与后报模式（38天中29天达标；平均均方根误差降低6%）中，均优于卫星观测的烟气质量载荷结果。资助编号：NA16OAR4590121