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https://doi.org/10.5194/acp-2020-8
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-8
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Mar 2020

Submitted as: research article | 16 Mar 2020

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This preprint is currently under review for the journal ACP.

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

Hyun Cheol Kim1,2, Tianfeng Chai1,2, Ariel Stein1, and Shobha Kondragunta3 Hyun Cheol Kim et al.
  • 1Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD, 20740, MD, USA
  • 2Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD, 20740, USA
  • 3National Environmental Satellite, Data and Information Service, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA

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 NOAA’s HYSPLIT and 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 days; −27 % average RMSE change) and hindcast modes (29 out of 38 days; −6 % average RMSE change).

Hyun Cheol Kim et al.

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Status: open (until 11 May 2020)
Status: open (until 11 May 2020)
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Hyun Cheol Kim et al.

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Short summary
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, that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. Using NOAA HYSPLIT and GOES Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level, and outperforms current operaitonal systme.
Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop...
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