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For more than a decade the Environmental Protection Agency’s (EPA) flagship chemical transport model, the Community Multiscale Air Quality (CMAQ) model, has relied on meteorological input from the Weather Research and Forecasting (WRF) model that is maintained by the National Center for Atmospheric Research (NCAR). The EPA has recently been developing support for coupling CMAQ with NCAR’s new meteorological option, the Model for Prediction Across Scales (MPAS). MPAS provides new and unique scientific capabilities and workflow advantages that benefit its application for air quality modeling. Here we document our most recent configuration of MPAS with CMAQ (MPAS-CMAQ) and present results from multi-year simulations. Surface ozone and fine particles (PM2.5) simulated by MPAS-CMAQ compares reasonably well with observations. July surface ozone shares similar patterns and magnitudes with other similar modeling products. Comparison with ozonesondes shows that MPAS-CMAQ suffers from a low ozone bias in the free troposphere. We share a case study on emissions of nitrogen oxides (NOx) from lightning to demonstrate the extended capabilities of MPAS-CMAQ for development and testing of CMAQ features.

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Recent Advancement of EPA’s Global Air Quality Modeling System: MPAS-CMAQ

  • 会议论文

  • 发表日期：

    2025年

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  • Jeff Willison,
  • Jonathan Pleim,
  • David Wong,
  • Robert Gilliam,
  • O. Russell Bullock,
  • Jerold A. Herwehe,
  • Christian Hogrefe,
  • George Pouliot,
  • Golam Sarwar,
  • Rohit Mathur,
  • Fahim Sidi

摘要

For more than a decade the Environmental Protection Agency’s (EPA) flagship chemical transport model, the Community Multiscale Air Quality (CMAQ) model, has relied on meteorological input from the Weather Research and Forecasting (WRF) model that is maintained by the National Center for Atmospheric Research (NCAR). The EPA has recently been developing support for coupling CMAQ with NCAR’s new meteorological option, the Model for Prediction Across Scales (MPAS). MPAS provides new and unique scientific capabilities and workflow advantages that benefit its application for air quality modeling. Here we document our most recent configuration of MPAS with CMAQ (MPAS-CMAQ) and present results from multi-year simulations. Surface ozone and fine particles (PM2.5) simulated by MPAS-CMAQ compares reasonably well with observations. July surface ozone shares similar patterns and magnitudes with other similar modeling products. Comparison with ozonesondes shows that MPAS-CMAQ suffers from a low ozone bias in the free troposphere. We share a case study on emissions of nitrogen oxides (NOx) from lightning to demonstrate the extended capabilities of MPAS-CMAQ for development and testing of CMAQ features.