P41  Ensemble Air Quality Modeling using the Coupled WRF-CMAQ Model

Robert Gilliam, US Environmental Protection Agency

Uncertainty in air quality modeling is largely impacted by the uncertain inputs of meteorology and emissions. Meteorology fields can be one of the largest sources of uncertainty. Transport of urban pollutants, for example, can be altered if the wind speed or direction is off by the level of instrument error. Differences in temperature or moisture can have an impact on boundary layer stability, clouds and radiation, all of which impact mixing, chemistry and photochemistry in the air quality models.

Over the last decade ensemble modeling has received attention because of the ability to better estimate the uncertainty contained in weather forecasts. Ensembles are initialized with slightly different, or perturbed initial conditions that consider measurement uncertainty. One such ensemble modeling system is the Short-Range Ensemble Forecast system (SREF) developed and managed by the National Center for Environmental Prediction (NCEP). Several models are used by the SREF and each model has a control run with multiple perturbation members. Each of these members has slightly different initial conditions that represent a possible state of the atmosphere. We leverage these varied initially conditions in retrospective modeling using four-dimensional data assimilation (grid nudging) in the coupled WRF-CMAQ model system. Sixteen WRF-CMAQ simulations were nudged towards SREF memberŐs initial conditions every six hours over a 4 day high-ozone case study in June of 2011. The variability in ozone and some meteorological variables are explored as well as an evaluation of the both the air quality and meteorological model.