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.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Environmental Chemistry
- Earth and Planetary Sciences(all)