Preprints
https://doi.org/10.5194/acp-2021-223
https://doi.org/10.5194/acp-2021-223

  12 Apr 2021

12 Apr 2021

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Xinxin Ye1, Pargoal Arab1, Ravan Ahmadov2,3, Eric James2,3, Georg A. Grell2, Bradley Pierce4, Aditya Kumar4, Paul Makar5, Jack Chen5, Didier Davignon6, Greg Carmichael7, Gonzalo Ferrada7, Jeff McQueen8, Jianping Huang8, Rajesh Kumar9, Louisa Emmons10, Farren L. Herron-Thorpe11, Mark Parrington12, Richard Engelen12, Vincent-Henri Peuch12, Arlindo da Silva13, Amber Soja14,15, Emily Gargulinski15, Elizabeth Wiggins15, Johnathan W. Hair15, Marta Fenn15, Taylor Shingler15, Shobha Kondragunta16, Alexei Lyapustin13, Yujie Wang13, Brent Holben13, David Giles13, and Pablo E. Saide1,17 Xinxin Ye et al.
  • 1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA
  • 2Cooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, CO, USA
  • 3NOAA Global Systems Laboratory, Boulder, CO, USA
  • 4University of Wisconsin – Madison Space Science and Engineering Center, Madison, WI, USA
  • 5Air Quality Research Division, Environment and Climate Change Canada, Ontario, Canada
  • 6Canadian Meteorological Centre Operations, Environment and Climate Change Canada, Quebec, Canada
  • 7College of Engineering, University of Iowa, Iowa City, IA, USA
  • 8NOAA/NWS National Centers for Environment Prediction, Boulder, CO, USA
  • 9Research Application Laboratory (RAL), National Center for Atmospheric Research (NCAR), Boulder, CO, USA
  • 10Atmospheric Chemistry Observations and Modeling (ACOM) Laboratory, NCAR, Boulder, CO, USA
  • 11Washington State Department of Ecology, Lacey, Washington, USA
  • 12European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
  • 13NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 14National Institute of Aerospace, Hampton, VA, USA
  • 15NASA Langley Research Center, Hampton, VA, USA
  • 16Center for Satellite Applications and Research, NOAA, Boulder, CO, USA
  • 17Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA

Abstract. Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from twelve state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, the U.S., August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within one day are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by FRP-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019 mainly over the transported smoke plumes, owing to the underestimated emissions on the 7th. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center’s Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper with the day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported one-day-old smoke. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Xinxin Ye et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-223', Anonymous Referee #1, 07 May 2021
  • RC2: 'Comment on acp-2021-223', Alan Wei Lun Lim, 16 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-223', Anonymous Referee #1, 07 May 2021
  • RC2: 'Comment on acp-2021-223', Alan Wei Lun Lim, 16 May 2021

Xinxin Ye et al.

Data sets

MODIS/Terra+Aqua MAIAC Land Aerosol Optical Depth Daily L2G 1 km SIN Grid (MCD19A2 v006) Alexei Lyapustin and Yujie Wang https://lpdaac.usgs.gov/products/mcd19a2v006/

Air Quality Data Collected by OpenAQ platfrom U.S. EPA AirNow network https://openaq.org/

Air Quality Data Collected at Outdoor Monitors Across the US U.S. EPA https://www.epa.gov/outdoor-air-quality-data

Distributed Regional Aerosol Gridded Observation Networks (DRAGON)- FIREX-AQ 2019 Brent Holben, David Giles, et al. https://aeronet.gsfc.nasa.gov/new_web/DRAGON-FIREX-AQ_2019.html

Fire Influence on Regional to Global Environments and Air Quality NASA/LARC/SD/ASDC https://doi.org/10.5067/SUBORBITAL/FIREXAQ2019/DATA001

Model code and software

WRF-Chem Grell et al. https://ruc.noaa.gov/wrf/wrf-chem/

Xinxin Ye et al.

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Short summary
Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of twelve real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles, for improving smoke forecasts.
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