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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-272
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-272
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 02 Sep 2020

Submitted as: model evaluation paper | 02 Sep 2020

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This preprint is currently under review for the journal GMD.

Evaluation of the offline-coupled GFSv15-FV3-CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States

Xiaoyang Chen1, Yang Zhang1, Kai Wang1, Daniel Tong2,6, Pius Lee4,3, Youhua Tang3,4, Jianping Huang5,6, Patrick C. Campbell3,4, Jeff Mcqueen5, Havala O. T. Pye7, Benjamin N. Murphy7, and Daiwen Kang7 Xiaoyang Chen et al.
  • 1Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
  • 2Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USA
  • 3Center for Spatial Information Science and System, George Mason University, Fairfax, VA 22030, USA
  • 4Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD 20740, USA
  • 5National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, MD 20740, USA
  • 6IM Systems Group, Rockville, MD 20852, USA
  • 7Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA

Abstract. The next-generation National Air Quality Forecast Capability (NAQFC) will use the meteorology from Global Forecast System with the new Finite Volume Cube-Sphere dynamic core (GFS-FV3) to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modelling system version 5.3 (CMAQ v5.3). CMAQ v5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS-FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15-CMAQv5.0.2), has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15-CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of−0.2 °C for temperature at 2-m, 0.4 % for relative humidity at 2-m, and 0.4 m s−1 for wind speed at 10-m against the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts against the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 μm or less (PM2.5) for warm months (May-September), it significantly overpredicts annual-mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive, coarse-mode, and nitrate particle components. Underpredictions in the southeastern U.S. and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semi- or intermediate-VOCs. This work identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC, in particular, derivation of the science-based bias correction methods to improve forecasting skill for O3 and PM2.5.

Xiaoyang Chen et al.

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Short summary
The National Air Quality Forecast Capability (NAQFC) has been providing the air quality forecasts and continuously updated since 2000s. To support the development of the next-generation NAQFC, we evaluate a prototype of forecast system. The performance and the potential improvements for the system are discussed. This study can provide scientific basis for further development of NAQFC and help it to provide more accurate air quality forecasts to the public over the contiguous United States.
The National Air Quality Forecast Capability (NAQFC) has been providing the air quality...
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