Quantifying Uncertainties of Ground‐Level Ozone Within WRF‐Chem Simulations in the Mid‐Atlantic Region of the United States as a Response to Variability

• Published in: Journal of advances in modeling earth systems 2019-04, Vol.11 (4), p.1100-1116
• Main Authors: Thomas, Andrew, Huff, Amy K., Hu, Xiao‐Ming, Zhang, Fuqing
• Format: Article
• Language: English
• Subjects: Air quality; Boundary conditions; Boundary layers; Chemistry; Emission inventories; Emissions; Emissions control; Environmental protection; Meteorology; Mixing ratio; Nitrogen dioxide; Outdoor air quality; Ozone; Planetary boundary layer; Pollutants; Studies; Uncertainty
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2018MS001457

Understanding forecast uncertainties and error growth dynamics is a prerequisite for improving dynamical prediction of meteorology and air quality. While predictability of meteorology has been investigated over the past few decades, the uncertainties in air quality simulations are less well known. This study explores the uncertainties in predicting ground‐level ozone (O3) in the Mid‐Atlantic region of the United States during June 2016 through a series of simulations using WRF‐Chem, focusing on the sensitivity to the meteorological initial and boundary conditions (IC/BCs), emissions inventory (EI), and planetary boundary layer (PBL) scheme. The average uncertainty of ground‐level maximum 8‐hr average O3 mixing ratio (MD8‐O3) was most sensitive to uncertainties in the IC/BCs, while uncertainty in the EI was of secondary importance, and was least sensitive was to the use of different PBL schemes. Updating the NO emissions in the EI had the greatest influence on the accuracy, with an estimated decrease of 0.59 ppbv/year in the root‐mean‐square error and an average decrease of 0.63 ppbv/year in the values of modeled MD8‐O3. Our study suggests using perturbations in IC/BCs may lead to a more dispersive ensemble of O3 prediction than using different PBL schemes and/or different EI. However, considering the combined uncertainties from all three sources examined are still smaller than the averaged root‐mean‐square errors of predicted O3 against observations, there are apparent other sources of uncertainties not studied that need to be considered in future ensemble predictions of O3. Plain Language Summary Ozone, the primary pollutant in photochemical smog, is harmful to human health, particularly for children, senior citizens, and people with existing heart or lung diseases, like asthma. To protect public health, air quality forecasts of ozone are issued across the United States, primarily for metropolitan areas, where ground‐level ozone tends to be the highest. When ground‐level ozone is predicted to be exceed the daily health standard, people are advised to take steps to limit their outdoor activities. Operational air quality forecasters use predictions of ground‐level ozone from numerical air quality models as guidance for their public forecasts. To assess the uncertainty in these model predictions of ground‐level ozone, a series of simulations using the air quality model WRF‐Chem were conducted in this study through changing the inputs to the model, including