Improving the representation of fresh wildfire smoke in air quality forecasts

Speaker: Laura Thapa
Institution: UCLA AOS
Location: MS 7124
Date: May 22, 2024
Time: 3:00 pm to 4:00 pm


Wildfires are increasing in size and frequency in the Western US due to a complex interplay between climate change and landscape-scale fire exclusion practices. The smoke from these fires is degrading air quality across much of the Continental US. Chemical transport models are vital for warning the public about smoky periods, but uncertainties related to fresh smoke plumes can propagate through these models and cause errors in the resulting air quality forecasts.

We address model uncertainty related to total emissions and smoke plume vertical extent. We first present data-driven methods for predicting day-to-day changes in smoke emissions. Our top-performing model (random forest) explains 48% of the variance in observed daily emissions and outperforms the current operational assumption that emissions will remain constant over a forecast period (persistence, R2=0.02). This model primarily relies on fire weather data to inform its predictions. Next, we use aircraft observations obtained during the 2019 Western US wildfires (FIREX-AQ) to evaluate and constrain a commonly used smoke plume rise parameterization in two smoke models (WRF-Chem and HRRR-Smoke). Observations show that free tropospheric smoke layers occur in 35% of observed plumes and up to 95% of modeled plumes. False free tropospheric smoke injections were primarily associated with models overestimating fire heat flux by up to a factor of 25. Finally, we show preliminary results from WRF-Chem simulations which include random forest-derived emissions and updated heat flux values. We find that in the vicinity of large wildfires in 2020 under less severe fire weather, the random forest-derived emissions produce better predictions of aerosol optical depth than the persistence fire emissions. Overall, this work demonstrates the utility of incorporating fire observations to quantify and address uncertainties in our state-of-the-art air quality modeling systems.