ARL Weekly News – November 17, 2023
Urbannet Instrumentation Under Evaluation at NCWCP.
Paul Kelley and Winston Luke installed a Vaisala CL51 ceilometer on the rooftop platform at NCWCP. The CL51 uses a pulsed diode LiDAR for full backscatter profiling to detect three cloud layer heights up to 15km (49,200ft). Aerosol backscatter in the boundary layer is measured to compute mixed-layer height, which is essential for transport and dispersion model input and evaluation. Although the ceilometer will be tested and evaluated at NCWCP, it will ultimately be deployed atop building SSMC3 in the NOAA’s Silver Spring campus to support the measurement and modeling of atmospheric transport and dispersion processes in the urban environment as part of ARL’s nascent UrbanNet program.
New PHEV Vehicle For GHG Monitoring.
Paul Kelley and Winston Luke traveled to the GSA Fleet Management Center in Rosedale, MD to take delivery of a second GSA leased vehicle for ASMD use. The 2021 Ford Escape Plug-in Hybrid Electric Vehicle (PHEV) will be used for general staff transport; general field support; and as a supplemental mobile sampling platform for ASMD’s Greenhouse Modeling and Monitoring Initiative. Its combined fuel economy is 40 mpg, and its electric-only range is 37 miles. It can comfortably seat 4-5 persons.
Published: Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”
Citation: Makkaroon, P., Tong, D. Q., Li, Y., Hyer, E. J., Xian, P., Kondragunta, S, P.C. Campbell, Y. Tang, B. D. Baker, M. D. Cohen, A. Darmenov, A. Lyapustin, R. D. Saylor, Y. Wang, and I. Stajner (2023). 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, e2022JD037298. https://doi.org/10.1029/2022JD037298
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.
- Ensemble mean of models provides reliable aerosol optical depth and PM5 forecasts with stronger correlation, lower bias, and the highest overall RANKs
- The ensemble approach provides a well suited exceedance probability forecast during the 2020 Gigafire events
- The multi-model ensemble approach can reduce uncertainties in air quality forecasts and improve model predictability during wildfire events
Plain Language Summary
Wildfires are a major source of air pollution emitting large quantities of particles into the air that adversely affect human health. Predicting wildfire air pollution, however, is challenging. Ensemble forecasting has been proposed to improve the model predictability. We developed a new multi-model ensemble forecast system of wildfire air pollution over North America, leveraging regional and global atmospheric models by federal agencies and academia. How well the ensemble forecast can predict wildfire pollution was evaluated with observations from satellites and ground monitors. We found that the ensemble mean can significantly reduce the forecast biases and produce more reliable forecasts during extreme wildfire fire events. The ensemble probability forecast of exceedance of the health-based National Ambient Air Quality Standards for fine particles (PM2.5) can be further applied to early warnings of severe air pollution episodes during large wildfire events. These findings highlight the potential of the ensemble approach to improve the predictability of air pollution during large wildfires.