Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States

Being detrimental to human health and vegetation growth, ground-level ozone (O3) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O3 in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O3 pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O3 simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O3 over the Southeast U. S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O3 observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O3 prediction than using traditional single meteorology by CTMs.

Automated summary

Ground-level ozone is a growing concern as an air pollutant due to its detrimental effects on human health and vegetation growth. Meteorological conditions play a crucial role in the formation, diffusion, transformation, and transport of ozone in the atmosphere. This study demonstrated significant improvements in ozone prediction accuracy by using ensemble meteorological inputs compared to traditional single meteorology inputs in chemical transport models.