Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire
Authors: Ye Xinxin , Arab Pargoal , Ahmadov Ravan , James Eric , Grell Georg A. , Pierce Bradley , Kumar Aditya , Makar Paul , Chen Jack , Davignon Didier , Carmichael Greg R. , Ferrada Gonzalo , McQueen Jeff , Huang Jianping , Kumar Rajesh , Emmons Louisa , Herron-Thorpe Farren L. , Parrington Mark , Engelen Richard , Peuch Vincent-Henri , da Silva Arlindo , Soja Amber , Gargulinski Emily , Wiggins Elizabeth , Hair Johnathan W. , Fenn Marta , Shingler Taylor , Kondragunta Shobha , Lyapustin Alexei , Wang Yujie , Holben Brent , Giles David M. , Saide Pablo E. 
Published: September 2021

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 12 state-of-the-art air quality forecasting systems
during the Williams Flats fire in Washington State, US, 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 1 d 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 fire radiative
power (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
7 August. 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 a
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 smoke that is 1 d old. Discrepancies in model performance for
surface PM2.5 and AOD are further suggested by the evaluation of their
ratio, which cannot be compensated for 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.