Co-Authors: Ivanka Stajner, Fanglin Yang, Jeffrey McQueen, Raffaele Montuo, Ho-Chun Huang, Kai Wang, etc.
The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality (AQ) forecasting system within the Unified Forecast System (UFS), aimed at improving the representation of wildfire emissions and their impact on air quality predictions. The system couples the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model with the UFS-based atmospheric model in an online mode, utilizing satellite-derived fire data, Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE), for calculating fire emissions. The UFS-AQM system has been approved to replace the existing operational prediction model.
In this presentation, I will provide an overview of the UFS-AQM online system. I will also present case studies demonstrating its superior performance in predicting particulate matter with a diameter less than 2.5 micrometers (PM2.5) and ozone (O3) concentrations during intense wildfire events in the summer of 2023. Furthermore, comprehensive evaluations of the UFS-AQM will be presented to demonstrate its readiness for operational implementation. Finally, I will outline future plans aimed at enhancing NOAA’s regional air quality prediction system further.
Dr. Jianping Huang recently joined the Environmental Modeling Center at NOAA/NWS/NCEP as a Physical Scientist. He earned his PhD from the Hong Kong University of Science and Technology and completed postdoctoral research at North Carolina State University and Yale University. Currently, Dr. Huang serves as the project lead, overseeing the transition of the UFS-AQM online system from research to operation (AQMv7). For the next steps, he will lead the development of the RRFS-AQM online prediction system within the UFS framework, facilitating its transition from research to operation for high-resolution applications.
