Ponente
Descripción
NOAA provides operational predictions of air quality (AQ) for the United States and aerosols globally. We present recent progress in the development of NOAA’s next generation AQ and aerosols predictions, within the new Unified Forecast System (UFS) framework.
Development of AQ predictions focuses on better representation of wildfire impacts. A new limited-area, high-resolution Rapid Refresh Forecast System weather model is online coupled with EPA’s Community Multiscale AQ chemistry model to form the RRFS-CMAQ system. Anthropogenic emissions come from EPA’s National Emissions Inventories and wildfire emissions from the NESDIS Blended Global Biomass Burning Emissions Product. Initial evaluations of RRFS-CMAQ use FIREX-AQ and routine AQ measurements. Planned model refinements include increased resolution, dynamic lateral boundary conditions, diurnal variations in wildfire emissions, and smoke plume rise. Data assimilation of AirNow PM2.5 observations, VIIRS Aerosol Optical Depth (AOD) and TROPOMI NO2 retrievals constrain pollutant concentrations. A machine learning emulator is being developed for chemical transformations and explored for tracer transport to reduce computational requirements. A bias correction post-processing procedure is planned to improve prediction accuracy.
Aerosol prediction in the global coupled UFS under development uses the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model from NASA’s repository. The coupled UFS also includes the Finite Volume cubed sphere (FV3) dynamical core with the Global Forecast System (GFS) physics for atmosphere, Modular Ocean Model (MOM6), Los Alamos Sea Ice Model (CICE6), WAVEWATCH III for waves and NOAH-MP for land. Testing and evaluation of UFS aerosols during NASA’s airborne Atmospheric Tomography campaign and FIREX-AQ field missions focuses on aerosol composition and vertical distribution. In combination with routine AOD and surface observations, this allows us to improve model representation of wildfires, plume rise or dust emissions as we develop coupled UFS aerosol prediction, include AOD data assimilation, explore aerosol-radiation interactions, and provide aerosol lateral boundary conditions for RRFS-CMAQ.
