A Dynamical Ensemble Approach to Characterizing Uncertainties in the Prediction of Air Quality Downstream of the Massive Western US Wildfires of 2020

Many forecast centers across the world are tasked each day with predicting ozone (O3), fine particulate matter (PM2.5), and other harmful pollutants. These predictions help guide advisories and various societal decision processes aimed at reducing humanity’s detrimental exposure and risks tied to poor air quality. Unfortunately, air quality forecasts still suffer from errors emanating from the driving datasets, inaccurate emissions, and a yet incomplete understanding of air quality processes.

As a vivid example of air quality challenges in the US, in the summer and autumn of 2020, record-breaking, long-lasting wildfires across numerous western US states subjected broad swaths of the western and central US to hazardous periods of extremely poor air quality. This perilous time period is likely portentous of air quality challenges to come as climate change further enhances western heat and drought. Therefore, these wildfires and their attendant smoke plumes serve as the basis of this presentation. Specifically, this study incorporates a dynamical ensemble based on the National Oceanic and Atmospheric Administration (NOAA)’s Rapid Refresh Forecast System coupled with the Community Multiscale Air Quality (RRFS-CMAQ). The ensemble is constructed using 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. The period of study covers August - October 2020 when some of the most expansive plumes of wildfire smoke were generated. The large RRFS-CMAQ ensemble is evaluated against observations across the US using NOAA’s Model and ObservatioN Evaluation Toolkit (MONET) and Model Evaluation Tools (MET) Plus. Particular attention is given to quality-controlled observations of O3 and PM2.5, which are measured at more than 900 stations across the continental US. The ensemble validation and analysis of the uncertainty will be the central focus of this presentation. The ultimate goal of the overall project will be 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. Meeting this objective will provide a cost-effective air quality ensemble for operational forecasting.