Airport categorizations offer a basis to derive representative scenarios for air traffic related simulation purposes. A methodology for an application specific airport categorization was developed as presented in this paper. Existing categorizations were identified to insufficiently reflect operational characteristics of airports and mostly to omit quantitative statements, which are a crucial simulation input. The presented approach shows a way to enhance an existing baseline categorization using application specific airport similarity parameters. A set of typical airports for each category can be specified by analyzing air traffic schedule data. Clustering techniques, the core element of the methodology, are applied to identify outliers, which are subsequently removed. The remaining group of airports is used to calculate the boundaries of the analyzed category as well as the representative scenario parameter values. The proposed approach is presented step by step for one category and the exemplary application in noise trading scheme simulations. Additional results for use in airport capacity analysis are provided. The presented approach offers the possibility to derive traffic scenarios that represent the characteristics of a multitude of airports within one category. In general a different set of similarity parameters can lead to different category boundaries and representative values. The results are application driven, as proven by the examples.