Evaluation of the WRF-Chem Performance for gaseous pollutants over the United Arab Emirates

Abstract

Abstract. This study presents a comprehensive evaluation of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in simulating meteorological parameters and concentrations of gaseous pollutants across the United Arab Emirates (UAE) for the months of June and December 2018, representing the contrasting climatic conditions of summer and winter. The assessment of WRF-Chem performance involved comparisons with ground-based observations for meteorological parameters and satellite retrievals from the TROPOspheric Monitoring Instrument (TROPOMI) for gaseous pollutants. The assessment of gaseous pollutants using the WRF-Chem model revealed distinct patterns in the estimation of pollutant levels across different areas and seasons. The comparison with TROPOMI column concentration revealed the model's strengths in simulating tropospheric NO2 and total O3 spatio-temporal patterns, although it had deficiencies in modelling the total CO column concentrations. The model exhibited a strong correlation with TROPOMI retrievals, with correlation coefficients ranging between 0.71 and 0.95 for summer and 0.86 to 0.94 for winter among these gaseous pollutants. It tended to slightly overestimate NO2 levels, with a higher discrepancy observed in summer (0.24 x 1015 molecules/cm2) compared to winter (0.19 x 1015 molecules/cm2). When comparing WRF-Chem to TROPOMI-CO data, the discrepancies were more pronounced, showing an overestimation of 0.48 x 1018 molecules/cm2 in summer and a significant underestimation of 1.13 x 1018 molecules/cm2 in winter. The model consistently underestimated ozone levels in both seasons, by 0.15 x 1018 and 0.20 x 1018 molecules/cm2, respectively. Meteorological evaluations revealed the model's tendency to underestimate the 2-m temperature in summer and overestimate it in winter, with mean biases ranging from -2.17 to +1.19 °C and a Root Mean Square Error in the range of 0.8 to 5.9 °C among the stations. The model showed enhanced performance for the 10-m wind speed and downward shortwave radiation flux, reflecting advancements over previous studies. Therefore, the WRF-Chem model effectively simulates key meteorological parameters and pollutants over the UAE, demonstrating significant regional-scale prediction skills. Areas for further model refinement are also identified and discussed. Integrating model predictions with satellite and ground-based data is emphasized for advancing air quality monitoring and enhancing predictive accuracy of atmospheric pollutants in this region.