Science Inventory

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

Citation:

Chen, X., Y. Zhang, K. Wang, D. Tong, P. Lee, Y. Tang, J. Huang, P. Campbell, J. McQueen, H. Pye, B. Murphy, AND D. Kang. 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. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 14:3969–3993, (2021). https://doi.org/10.5194/gmd-14-3969-2021

Impact/Purpose:

We have used bias analyses in this work to identify several areas of weakness for further improvement and development of next-generation National Air Quality Forecast (NAQFC). Our work and the further studies can provide information and scientific basis for the development and implement of a science-based bias correction method in next-generation NAQFC.

Description:

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, 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 compared to 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 compared to 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 and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It 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.