Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System

Details

• Journal Title:
  Geoscientific Model Development
• Personal Author:
  Li, Wei ; Tang, Beiming ; Campbell, Patrick C. ; Tang, Youhua ; Baker, Barry ; Moon, Zachary ; Tong, Daniel ; Huang, Jianping ; Wang, Kai ; Stajner, Ivanka ; Montuoro, Raffaele
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory) ; CISESS (Cooperative Institute for Satellite Earth System Studies) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)
• Description:
  The air quality forecasting system is an essential tool widely used by environmental managers to mitigate adverse health effects of air pollutants. This work presents the latest development of the next-generation regional air quality model (AQM) forecast system within the Unified Forecast System (UFS) framework in the National Oceanic and Atmospheric Administration (NOAA). The UFS air quality model incorporates the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model as its main chemistry component. In this system, CMAQ is integrated as a column model to solve gas and aerosol chemistry, while the transport of chemical species is processed by UFS. The current AQM version 7 (AQMv7) is coupled with an earlier version of CMAQ (version 5.2.1). Here we describe the development of the updated AQMv7 by coupling to a “state-of-the-science” CMAQ version 5.4. The updates include improvements in gas and aerosol chemistry, dry deposition processes, and structural changes to the input/output (I/O) interface, enhancing both computational efficiency and representation of air–surface exchange processes. A simulation was conducted for the period of June–August 2023 to assess the effects of these updates on the forecast performance of ozone (O3) and fine particulate matter (PM2.5), two major air pollutants over the continental United States (CONUS). The results show that the updated model demonstrates an enhanced capability in simulating O3 over the CONUS by reducing the positive bias, leading to a reduction in the mean bias by 3 %–5 % and 8 %–12 % for hourly and the maximum daily 8 h average O3, respectively. Spatially, the updated model lowers the positive bias of hourly O3 in most of the 10 EPA regions, particularly within the central and northwest areas, while amplifying the O3 underestimation over the sites with negative bias. Similarly, the updates induce uniformly lower fine particulate matter (PM2.5) concentrations across the CONUS domain, reducing the positive bias at some sites over the northeast in August and central Great Plains. The updated model does not improve model performance for PM2.5 in the vicinity and downwind of fire emission sources, where AQMv7 shows the highest negative bias, thus indicating a focal point of model uncertainty and needed improvement. Despite these challenges, the study highlights the importance of the ongoing refinements for reliable air quality predictions from the UFS-AQM model, which is a planned future update to NOAA's current operational air quality forecast system.
• Source:
  Geoscientific Model Development, 18(5), 1635-1660
• DOI:
  https://doi.org/10.5194/gmd-18-1635-2025
• ISSN:
  1991-9603
• Format:
  pdf
• Publisher:
  Copernicus GmbH
• Document Type:
  Journal Article
• Funding:
  Grant no. NA19NES4320002 ; Grant no. NA22NES4050024I ; Grant no. NA22NES4050023D ; Grant no. NA22NES4050024I
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:04a0ea23a58fa8f8d54f337604e92ed735500522c71294c27b0d7a0379063e621d1c4177ea7cf58de7063d005841ee383d4d15c160ec73a300e99177b5b1c329
• Download URL:
  https://repository.library.noaa.gov/view/noaa/71276/noaa_71276_DS1.pdf
• File Type:
  [PDF - 11.97 MB ]