Abstract
Abstract. 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.
Highlights
Burning biomass is one of the major factors affecting global air quality (Crutzen and Andreae, 1990)
This study extends the current capabilities of the National Oceanic and Atmospheric Administration (NOAA) Smoke Forecasting System (SFS) fire smoke forecast systems, most of which estimate fire emissions using the surface and thermal characteristics of detected fire locations
This study aims to develop an inverse modeling system for fire emissions based on a Lagrangian model that can resolve these transport pathways using HYSPLIT simulations and satellite observations
Summary
Burning biomass is one of the major factors affecting global air quality (Crutzen and Andreae, 1990). Fire smoke plumes directly emit both particles that can impact cardiopulmonary health and precursors (e.g., NOx, SO2, NH3, and volatile organic carbons, VOCs) (Andreae, 2019) that react to form secondary particulate matter (PM) or other pollutants, such as ozone (Dreessen et al, 2016; Jaffe and Wigder, 2012; Mok et al, 2016; Singh et al, 2012; Valerino et al, 2017) In addition to their impact on air quality, fire emissions influence direct and indirect radiative transfer, aerosol formation, and the formation of cloud condensation nuclei, and they further interact with clouds and, eventually, with the biosphere and climate.
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