Impact of inherent meteorology uncertainty on air quality model predictions. Issue 23 (1st December 2015)

• Record Type: Journal Article
• Authors: Gilliam, Robert C.; Hogrefe, Christian; Godowitch, James M.; Napelenok, Sergey; Mathur, Rohit; Rao, S. Trivikrama
• Is Part Of: Journal of geophysical research. Volume 120:Issue 23(2015:Dec.)
• Page Start: 12, 259
• Page End: 12, 280
• Publication Date: 2015-12-01
• Subjects: ensemble air quality -- WRF‐CMAQ -- ozone uncertainty -- ensemble FDDA; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD023674
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short‐Range Ensemble Forecast system to drive the four‐dimensional data assimilation in the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone‐mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone‐mixing ratios of the ensemble can vary as much as 10–20 ppb or 20–30% in areas that typically have higher pollution levels. Key Points: Ensemble FDDA results in large spread in meteorology and chemistry solutions Key boundary layer variables like PBL height and radiation have significant variability The uncertainty injected by ensemble FDDA caused large deviations in trajectories within a few days