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

Abstract

Abstract. 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.

Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16

“…Since 2017, there has also been significant efforts at NOAA to use version 15 of FV3GFS (hereafter, GFSv15) rather than NMMB as the meteorological driver for CMAQ in the NAQFC (Huang et al, 2017(Huang et al, , 2018(Huang et al, , 2019. Huang et al (2020) and Chen et al (2021) demonstrated that a version of the GFS-driven CMAQv5.0.2 (GFSv15-CMAQ) forecasting system had partly improved O 3 predictions compared to the NMMB-driven CMAQ (NMMB-CMAQ) system but that the GFSv15-CMAQ had large biases for PM 2.5 that still need improvement.…”

“…Thus, there was an opportunity to simultaneously upgrade and streamline the meteorological coupling between the GFSv16 and a more updated, "stateof-the-science" version of CMAQ at the US EPA (US EPA, 2019; Appel et al, 2021). The current CMAQv5.0.2 used in the NMMB-CMAQ and experimental GFSv15-CMAQ is outdated scientifically with numerous deficiencies, many of which led to the elevated biases and error as described in Huang et al (2017Huang et al ( , 2020 and Chen et al (2021). Hence, there is a need to update the NAQFC to actively developing versions of both FV3GFS and CMAQ.…”

An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea

“…These virtual points have no error at all, and by introducing the virtual point, the weight value in Equation ( 10) can be reduced. The modified weight for the virtual point n v at the influence radius R is expressed as Equation (12). In Equation (12), as the number of virtual points increases, the distance of the influence of the error at each machine learning prediction point decreases.…”

“…The modified weight for the virtual point n v at the influence radius R is expressed as Equation (12). In Equation (12), as the number of virtual points increases, the distance of the influence of the error at each machine learning prediction point decreases. In this study, 4 was applied according to Pun's research results for the number of virtual points, and the distance of the radius of influence was 50 km.…”

“…As a numerical analysis method, the Community Multiscale Air Quality (CMAQ) model is mainly used. The CMAQ model predicts air quality by analyzing the movement of pollutants and chemical reactions based on data such as meteorological data and emissions [10][11][12]. As a statistical method, prediction methods using artificial intelligence technologies, such as machine learning, are widely used [13,14].…”