Development of Next-Generation Air Quality Predictions for the United States

Abstract

<p>NOAA is developing a next-generation air quality prediction system using a new limited-area, high-resolution online-coupled numerical weather and atmospheric composition model. This system integrates high-resolution meteorology provided by the Rapid Refresh Forecast System (RRFS) with atmospheric column chemistry from the EPA’s Community Multiscale Air Quality (CMAQ) modeling system within the Unified Forecast System (UFS) framework (https://ufscommunity.org/). RRFS-CMAQ uses anthropogenic emissions based on the U.S. EPA’s National Emissions Inventory and natural emissions estimated from process-based emission models such as FENGSHA and Biogenic Emission Inventory System.</p><p> </p><p>In addition to the desire to unify modeling system codes for various applications and to introduce coupling of Earth system modeling components, such as those for weather and chemistry, a strong motivation for the development of RRFS-CMAQ is to provide a better representation of wildfire impacts on air quality. Wildfire emissions cause extremely high concentrations of air pollutants near fire locations. Depending on meteorological conditions, wildfire impacts can be carried downwind and affect air quality far away, even across the continent, like in recent summers when smoke from the U.S. west coast and Canadian fires impacted the eastern U.S. coast. Due to uncertainties in wildfire emission strength, evolution, composition and rise of smoke plumes, the impacts of wildfires on air quality are difficult to predict. Wildfire emissions in RRFS-CMAQ are specified by the NESDIS Blended Global Biomass Burning Emissions Product (GBBEPx). An evaluation system based on FIREX-AQ field data has been developed and used to evaluate current operational air quality predictions to establish a baseline that will be used to evaluate the prototype RRFS-CMAQ system as development continues. Planned refinements of RRFS-CMAQ include improvements in resolution, lateral boundary conditions, and the representation of wildfire emissions, such as smoke plume rise and diurnal variations of smoke emissions. Data assimilation is used to constrain distributions of atmospheric pollutants using observations of fine particulate matter (PM2.5) from AirNow, Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and NO2 retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). To improve computational efficiency, machine learning emulators are also being developed for prediction of chemical transformations and tracer transport. To improve prediction accuracy, a bias correction post-processing procedure is planned to be introduced.</p>