Capability of NOAA's Global Aerosol Forecast Model (GEFS-Aerosols) in Forecasting Hazardous Air Quality

NOAA's national weather service is on the way to integrate various operational prediction applications into the Unified Forecast System. A chemical component collaboratively developed by Global Systems Laboratory, Chemical Science Laboratory, and Air Resource Laboratory (GSL, CSL, ARL) was coupled online with FV3GFS using the National Unified Operational Prediction Capability (NUOPC) Layer. It is slated to replace the current operational global aerosol prediction system at National Center for Environment Prediction (NCEP) as an ensemble member in the Global Ensemble Forecast System (GEFS-aerosols). This chemical component in GEFS-Aerosols is based on WRF-Chem with the aerosol modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). The fire plume rise module is from WRF-Chem; the FENGSHA dust scheme was implemented and developed by ARL; the Blended Global Biomass Burning Emissions Product (GBBEPx) provides biomass burning emission and Fire Radiative Power data, and global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid scale tracer transport and deposition is handled inside the physics routines which required consistent implementation of positive definite tracer transport and wet scavenging in the Simplified Arakawa-Schubert (SAS) scheme. The study evaluates the capability of GEFS-Aerosols model in forecasting hazardous air quality events caused by fire, dust storm and weather conditions. Different observations from reanalysis data, in situ measurement, satellite and aircraft data are used for the evaluation. GEFS-Aerosol predictions demonstrate a substantial improvement for both composition and variability of aerosol distributions over those from the currently operational global aerosol prediction system. Meanwhile, sensitivity experiments are performed to qualify the major factors impacting air quality predictions, providing direction for further improvement.