Development of a biomass burning smoke prediction system including near-real time constraints on emissions over the Western U.S.

Biomass burning is one of the major air pollutant sources with significant global, regional and local impacts on air quality, public health, and climate. Reducing the uncertainty of biomass burning emissions predictions is critical to improve air quality forecasts and assessments of their various impacts. Model predictions and field flight campaign activities are complementary for reducing uncertainties. In this work the Weather Research and Forecasting (WRF) system is used to construct a smoke forecast system for the western continental U.S., one of the regions around the globe with frequent large wildfires. The model is configured with an aerosol-aware microphysics parameterization in a cost-efficient way for numerical weather prediction (NWP) applications. Based on an inverse modeling technique using satellite aerosol-optical-depth (AOD) retrievals, the emissions are constrained and are used to produce updated fire smoke predictions for the next forecasting cycle. Different strategies for selecting the regions where emissions are scaled will be assessed. In preparation for the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign planned for summer 2019, model predictions with and without emission constraints will be evaluated using satellite and ground-based data for the 2018 fire season, and compared to an ensemble of regional and global air quality forecasts to evaluate the effect of the near-real time emission constraints. This work will facilitate the near-real-time quantifications of fire emissions and inter-comparison among multiple forecasts, which is expected to provide improved predictions and better estimates of smoke impacts on air quality, visibility and cloud-aerosol-radiation interactions.