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.