Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”
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Author/Creator ORCID
Date
2023-11-20
Type of Work
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Citation of Original Publication
Makkaroon, P., D. Q. Tong, Y. Li, E. J. Hyer, P. Xian, S. Kondragunta, P. C. Campbell, et al. “Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States ‘Gigafire.’” Journal of Geophysical Research: Atmospheres 128, no. 22 (2023): e2022JD037298. https://doi.org/10.1029/2022JD037298.
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
Abstract
Wildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi-model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU-CMAQ, NACC-CMAQ, and HYSPLIT), global models (GEFS-Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP-MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS-SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.