A dynamical ensemble approach to characterizing uncertainties in the prediction of air quality downstream of massive Western US wildfires in 2020

To help guide advisories and various societal decision processes aimed at reducing humanity’s detrimental exposure and risks tied to poor air quality, NOAA predicts ozone (O3), fine particulate matter (PM2.5), and other harmful pollutants daily. Unfortunately, air quality forecasts still suffer from errors emanating from the driving datasets, inaccurate emissions, and an incomplete understanding of air quality processes. With increasingly intense western North American wildfires producing expansive and harmful smoke plumes that impact millions of people downstream, it is important to improve predictions. This work aims to design a dynamical ensemble based on the NOAA’s Online Community Multiscale Air Quality (Online CMAQ) embedded within the UFS. The ensemble is based on perturbations of (a) meteorological and chemical initial and lateral boundary conditions, (b) anthropogenic, biogenic, and biomass burning emissions, (c) secondary organic aerosol response to temperature changes and solubility of semi-volatile organic compounds (SVOCs), and (d) removal processes including the hygroscopicity of aerosols and dry deposition velocities of O3, precursors, and SVOCs. Such a perturbation strategy leads to >50 ensemble members. In this presentation, the ensemble is evaluated against AirNOW observations of O3 and PM2.5 in the summer of 2020 when historic western US wildfires generated extensive smoke plumes. The ensemble validation and analysis of the uncertainty will be the central focus of this presentation. The project's ultimate goal is to develop down-selection techniques with calibration to reduce the ensemble size to ~10 members such that the majority of skill and ensemble quality is retained. This will provide a cost-effective air quality ensemble for NOAA’s operational air quality forecasting.