Performance criteria to evaluate air quality modeling applications

A set of statistical indicators fit for air quality model evaluation is selected based on experience and literature: The Root Mean Square Error (RMSE), the bias (Bias), the standard Deviation (SD) and the correlation factor (R) are selected. Among these the RMSE is proposed as the key one for the description of the model skill. Model Performance Criteria (MPC) to investigate whether a model results are ‘good enough’ for a given application are calculated based on the observation uncertainty (U). The basic concept is to allow for model results a similar margin of tolerance (in terms of uncertainty) as for observations. U is pollutant, concentration level and station dependent, therefore the proposed MPC are normalized by U. Some existing composite diagrams are adapted to visualize model performance in terms of the proposed MPC and are illustrated in a real modeling application. The Target diagram, used to visualize the RMSE, is adapted with a new normalization on its axis, while complementary diagrams are proposed. In this first application the dependence of U on concentrations level and station is ignored, and an assumption on the pollutant dependent relative error is made. The advantages of this new approach are finally described.JRC.H.2-Air and Climat