Bias-correction of wind speeds to improve PM2.5 predictability in chemical transport model

Wind speed is a critical factor influencing the transport and dispersion of atmospheric pollutants in air quality models. However, numerical weather prediction (NWP) models, such as the weather research and forecasting (WRF) model, typically overestimate surface wind speeds, leading to inaccuracies in air quality predictions. To address this limitation, we developed an Artificial Intelligence (AI)-based Wind Field Correction (WFC) model aimed at improving PM_2.5 forecasts over East Asia. The WFC model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and trained on eight years of data, incorporating WRF-simulated meteorological variables as input features and in situ, ship-based, buoy, and radiosonde observations as targets. The WFC model effectively reduced the positive bias in WRF-simulated wind speeds, achieving a 90.15% reduction at the surface level and a 94.6% reduction from the surface to 850 hPa. The bias-corrected wind fields, when incorporated into the GIST Multiscale Air Quality model (GMAQ v1.0) developed by the Gwangju Institute of Science and Technology (GIST), resulted in substantial improvements in PM_2.5 predictablity. In Central Eastern China (CEC), the wind field correction mitigated the underestimation of PM_2.5 by suppressing excessive plume dilution in the model. In South Korea (SK), the correction slowed down accelerated plume advection, leading to a closer agreement between the simulated and observed PM­_2.5 plume locations. In addition, the correction enhanced the representation of daily PM­_2.5 variability and improved statistical metrics over the capital cities of Seoul and Beijing.