SEMI-LAGRANGIAN MODELING OF TROPOSPHERIC OZONE

The occurrence of high concentrations of ozone in the lower part of th e troposphere is considered as one of the most important issues of tro pospheric chemistry. The chemical mechanisms of tropospheric ozone for mation are complex, and highly variable meteorological conditions cont ribute additionally to difficulties in an accurate prediction of ozone episodes. An effective way to increase our understanding of the probl em and eventually improve our ability to predict the concentration of tropospheric ozone and to formulate emission control strategies is by applying a comprehensive model representing accurately the interaction between meteorological processes and chemical reactions. This paper p resents a 3-dimensional semi-Lagrangian, chemical tracer model (CTM) f eaturing an accurate transport algorithm, comprehensive oxidants chemi stry and deposition modules. The CTM is executed in off-line mode with a semi-Lagrangian, nonhydrostatic, mesoscale meteorological model tha t contains an extensive parameterization of physical processes (includ ing a boundary layer scheme and clouds). The system of models was run for a time period of 6 days in order to generate a tropospheric ozone field during a smog episode observed in the eastern part of North Amer ica, in the beginning of August 1988. The numerical simulation was per formed on grids with resolution of 20 and 40 km with 25 vertical level s. The emissions inventory considered in the simulation included point sources, surface biogenic sources, surface mobile sources and surface non-mobile sources. An evaluation of the model results against observ ations clearly indicates the ability of the system to simulate regiona l aspects of a tropospheric ozone episode. The model performance compa res well to other models' results reported in the literature. An impor tant achievement of this work is improving the physical realism of sim ulations by using highly accurate, nonoscillatory semi-Lagrangian adve ction transport algorithms.