Eulerian photochem. grid models are widely used in the making of policy decisions concerning emission controls. However, significant uncertainties persist in a host of crucial input parameters. Quantifying and understanding their implications have become imperative from both the scientific and policy perspectives. Meteorol. uncertainties have a special significance because efforts to quantify them using conventional techniques based on expert ests. have failed to account for the complex correlations in their nonlinear spatial and temporal evolution. A comprehensive effort to quantify meteorol. uncertainties has been accomplished by subjecting a mesoscale meteorol. model to Monte Carlo simulations based on uncertainties in key base input parameters for the 3-day summer smog POLLUMET episode over the Swiss plateau. Monte Carlo uncertainty anal. based on the resulting meteorol. input uncertainties was then carried out using a photochem. grid model. Preliminary post-simulation anal. and statistics on the uncertainties in peak ozone estn. and the significance of considering correlations among inputs in sampling are presented.