Efficient probabilistic estimates of surface ozone concentration using an ensemble of model configurations and direct sensitivity calculations.
Because all models are a simplification of the phenomenon they aim to represent, it is often more useful to estimate the probability of an event rather than a single "best" model result. Previous air quality ensemble approaches have used computationally expensive simulations of separately developed modeling systems. We present an efficient method to generate ensembles with hundreds of members based on several structural configurations of a single air quality modeling system. We use the Decoupled Direct Method in three dimensions to directly calculate how ozone concentrations change as a result of changes in input parameters. The modeled probability estimate is compared to observations and is shown to have a high level of skill and improved resolution and sharpness. This approach can help resolve the practical limits of incorporating uncertainty estimation into deterministic air quality management modeling applications.
Pinder
Robert W
RW
National Exposure Research Laboratory, U.S. Environmental Protection Agency, USEPA Mail Drop E243-01, Research Triangle Park, North Carolina 27711, USA. pinder.rob@epa.gov
Gilliam
Robert C
RC
Appel
K Wyat
KW
Napelenok
Sergey L
SL
Foley
Kristen M
KM
Gilliland
Alice B
AB
D010126:Ozone / Q000032:analysis*; D011336:Probability; D035501:Uncertainty
D010126:Ozone