High-resolution WRF forecasts in the SmartAQ system: Evaluation of the meteorological forcing used for PMCAMx predictions in an urban area

Abstract

High-resolution modeling approaches are becoming a necessary step for enhancing regional air quality monitoring and forecasting. This study first evaluates the meteorological driver of the WRF- PMCAMx 1 × 1 km2 SmartAQ air quality prediction system and then assesses the impact of meteorological errors on the PM2.5 predictions. The comprehensive multi-variable verification on several timescales appraises the suitability of such a high-resolution modeling system in predicting the surface weather parameters in a major coastal urban area while gaining valuable insights into the model performance. The highest agreement between the observed and modelled values has been found for the actinometric and the thermodynamic variables, with the exception of the soil moisture which was systematically overestimated. WRF largely captures the variability of the observed values for the dynamic variables, showing better skill in the warm season, with a systematic overestimation though. The overall ability in predicting the rain events, important for the wet deposition of atmospheric pollutants is approximately 50%. Wind speed forecast errors contribute to the PM2.5 prediction errors, regardless of season and day/night hours. The impact of the wind errors is larger at almost all stations in the cold season when local emissions are elevated.