Evaluation and intercomparison of wildfire smoke forecasts from multiple...

Ye, X., P. Arab, R. Ahmadov, E. James, G. A. Grell, B. Pierce, A. Kumar, P. Makar, J. Chen, D. Davignon, G. Carmichael, Guajardo Ferrada, J. McQueen, J. Huang, R. Kumar, L. Emmons, F. L. Herron-Thorpe, M. Parrington, R. Engelen, V. Peuch, A. da Silva, A. Soja, E. Gargulinski, L. Wiggins, J. W. Hair, M. A. Fenn, T. Shingler, S. Kondragunta, A. Lyapustin, Y. Wang, B. Holben, D. M. Giles, and Saide Peralta (2021), Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire, Atmos. Chem. Phys., 21, 14427-14469, doi:10.5194/acp-21-14427-2021.

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

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