Estimates of Sensitivities of Photochemical Grid Models to Uncertainties in Input Parameters, as Applied to UAM-IV on the New York Domain
Because photochemical grid models such as UAM-IV are being used to make policy decisions concerning emissions controls, it is important to know what confidence bounds we can place on the model predictions of, for example, how ozone will respond to changes in emissions. These are presently unknown. The factors influencing prediction error can be classified as input errors, as model formulation errors, or as random stochastic processes. In the present study we include among inputs such things as initial and boundary conditions, emissions, meteorological variables and chemical rate constants. Bias, imprecision and variability can contribute to the error. Formulation errors would include such things as inaccuracies in advection schemes, numerical solvers, process representations, and temporal and spatial resolution. Sometimes the distinction between input and formulation errors is not well drawn. The study described here has been limited by time and resource constraints to an examination of the prediction error associated only with input, not with model formulation, errors. We therefore implicitly assume, without justification, that the model physics and chemistry are correctly formulated. Since model formulation can influence not only simulation fidelity but how input errors are propagated through the model to output errors, we view the results of this study more as a methodological demonstration than a definitive uncertainty analysis.
KeywordsEmission Reduction Ozone Concentration Peak Ozone Base Emission Chemical Rate Constant
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