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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


E. B. Wiggins, NASA Postdoctoral Program
Bruce E. Anderson, NASA Langley Research Center
M. D. Brown, NASA Langley Research Center
Pedro Campuzano-Jost, University of Colorado
Gao Chen, NASA Langley Research Center
James Crawford, NASA Langley Research Center
Ewan C. Crosbie, NASA Langley Research Center
Jack E. Dibb, University of New HampshireFollow
Joshua P. DiGangi, National Aeronautics and Space Administration
Glenn S. Diskin, National Aeronautics and Space Administration
M. Fenn, NASA Langley Research Center
F. Gallo, Universities Space Research Association
E. M. Gargulinski, National Institute of Aerospace
Hongyu Guo, Colgate University
J. Hair, NASA Langley Research Center
H. S. Halliday, Environmental Protection Agency
C. Ichoku, Howard University
Jose L. Jimenez, University of Colorado
Carolyn E. Jordan, NASA Langley Research Center
Joseph M. Katich, National Oceanic and Atmospheric Administration
J. B. Nowak, NASA Langley Research Center
Anne E. Perring, University of Colorado Boulder
Claire E. Robinson, NASA Langley Research Center
K. J. Sanchez, Universities Space Research Association
M. Schueneman, University of Colorado Boulder
J. P. Schwarz, NOAA Chemical Science Laboratory
T. J. Shingler, NASA Langley Research Center
M. A. Shook, NASA Langley Research Center
A. J. Soja, NASA Langley Research Center
C. E. Stockwell, University of Colorado Boulder
K. L. Thornhill, NASA Langley Research Center
K. R. Travis, NASA Langley Research Center
C. Warneke, NOAA Chemical Science Laboratory
E. L. Winstead, NASA Langley Research Center
L. D. Ziemba, NASA Langley Research Center
R. H. Moore, NASA Langley Research Center


Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high-resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low-resolution global inventories and the high-resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.


Earth Systems Research Center

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JGR: Atmospheres



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This is an open access article published by AGU in 2021 in JGR: Atmospheres, available online: