Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”

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.

Multiagency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning

“…Model 3 has the lowest false alarm rate, but also the lowest hit rate. Overall, the MMA ensemble works better than the individual models in extreme events air quality forecast, consistent with prior research (Li et al, 2020;Makkaroon et al, 2023).…”

“…Li et al (2020) used an ensemble forecast to predict surface PM2.5 during the 2018 California Camp Fire event using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model with different emissions, plume heights, and model setups. Makkaroon et al (2023) successfully demonstrated a multi-model ensemble forecast system that effectively simulated the 2020 western US "Gigafire", with the ensemble mean outperforming individual models. These studies highlight the potential of ensemble forecasting to improve the predictability of wildfire air quality.…”

“…While multi-model ensemble often outperforms single-model forecasts, some challenges remain. The ensemble mean does not work best all the time (Xian et al, 2019;Makkaroon et al, 2023). For instance, insufficient diversity among models in the multi-model ensemble can limit the ability of the ensemble to capture the full uncertainties and variability tied to different inputs and assumptions.…”

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“…Li et al (2020) used an ensemble forecast to predict surface PM 2.5 during the 2018 California Camp Fire event using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model with different emissions, plume heights, and model setups. Makkaroon et al (2023) successfully demonstrated a multimodel ensemble forecast system that effectively simulated the 2020 western U.S. "Gigafire," with the ensemble mean outperforming individual models. These studies highlight the potential of ensemble forecasting to improve the predictability of wildfire air quality.…”

“…While multimodel ensemble often outperforms single-model forecasts, some challenges remain. The ensemble mean does not work best all the time (Xian et al 2019;Makkaroon et al 2023). For instance, insufficient diversity among models in the multimodel ensemble can limit the ability of the ensemble to capture the full uncertainties and variability tied to different inputs and assumptions.…”