EGU2020-12059, updated on 13 Jan 2022
https://doi.org/10.5194/egusphere-egu2020-12059
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Advances in operational air quality and aerosol prediction at NOAA/National Weather Service

Ivanka Stajner1 and the Global Aerosol and Regional Air Quality Prediction Team*
Ivanka Stajner and the Global Aerosol and Regional Air Quality Prediction Team
  • 1NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland, United States of America
  • *A full list of authors appears at the end of the abstract

NOAA is developing the Unified Forecast System (UFS) (https://ufscommunity.org/) as the source system for operational numerical weather prediction applications.  The UFS will be a coupled, comprehensive Earth modeling system with community contributions. The UFS is designed to streamline and simplify NOAA/National Weather Service operational modeling suite.  Integration of air quality predictions into the UFS began with testing of the Community Multiscale Air Quality modeling system (CMAQ) predictions driven by the operational version of the Global Forecast System (GFS), which includes the Finite-Volume Cubed-Sphere (FV3) dynamical core since June 2019.  In addition to system integration, this testing allows us to extend ozone and PM2.5 predictions to 72 hours (from 48 hours that operational predictions currently cover).  Integration of global aerosol prediction based on the Goddard Chemistry Aerosol Radiation and Transport (GOCART) scheme into the UFS begun by including it into one member of the Global Ensemble Forecast System (GEFS-Aerosol). GEFS-Aerosol predictions demonstrate a substantial improvement for both composition and variability of aerosol distributions over those from the currently operational standalone global aerosol prediction system.

The use of satellite observations in atmospheric composition and air quality predictions is increasing at NOAA.  Real-time estimates of biomass burning emissions for predictions are based on satellite data.  Challenges for these emissions involve detection of fires, the strength and composition of the emissions, altitude of the plume rise, temporal distribution of the emissions and the uncertainty in persistence or change of emissions during the forecast period. Representation of changing fire emissions in the model becomes more important with increasing prediction length.  Assimilation of Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) observations is under development to constrain aerosol distribution in the global system. Initial testing shows promise for improvement of predictions as well as limitations indicating a need for refinements in quality control, data assimilation impacts on aerosol composition and vertical distribution, as well as a need for bias correction of satellite observations.   Plans for the next-generation regional system include assimilation of satellite retrievals of VIIRS AOD and Sentinel-5 Precursor Tropospheric Ozone Monitoring Instrument (S5P TROPOMI) NO2. Satellite data also play an important role in verification of aerosol predictions. Additional uses of satellite data include verification and evaluation of model predictions such as aerosol vertical profile with TROPOMI aerosol layer height product as well as efforts to constrain and update anthropogenic emissions.

This presentation will overview advances and challenges in model development and the use of satellite data for operational atmospheric composition and air quality predictions at NOAA.

Global Aerosol and Regional Air Quality Prediction Team:

Ivanka Stajner, Jeff McQueen, Jianping Huang, Ho-Chun Huang, Li Pan, Partha Bhattacharjee, Daryl Kleist, Cory Martin, Andrew Collard, Dorothy Koch, Jose Tirado-Delgado, Georg Grell, Li Zhang, Mariusz Pagowski, Rick Saylor, Pius Lee, Barry Baker, Youhua Tang, Daniel Tong, Patrick Campbell, Gregory Frost, Stuart McKeen, James Wilczak, Irina Djalalova, Shobha Kondragunta, Sarah Lu, Shih-Wei Wei

How to cite: Stajner, I. and the Global Aerosol and Regional Air Quality Prediction Team: Advances in operational air quality and aerosol prediction at NOAA/National Weather Service, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12059, https://doi.org/10.5194/egusphere-egu2020-12059, 2020.