Accurate modeling of aircraft fuel consumption, emissions, and noise is crucial in evaluating new air transportation operational procedures and policies to abate negative environmental impacts. The Aviation Environmental Design Tool (AEDT) is a comprehensive software package developed to address this requirement. Although the modeling of departure operations around airports is of great interest to policy makers and communities, AEDT’s default departure procedures, assumptions of maximum takeoff thrust, and current weight estimations do not fully represent real-world operational flight conditions. With more access to flight data, this paper first presents the development of improved departure profiles that fine-tune the previous modeling assumptions to more accurately reflect real-world operations. We then present a systematic analysis of the new departure profiles through a large-scale computer experiment and follow-up statistical analysis. The result provides comprehensive and valuable insights on the sensitivity of assumptions, quantification of the new profiles’ impacts on estimating aviation environmental impacts, and variability among different aircraft models. Lastly, the statistical learning method elastic net is used to find the driving factors behind the complex patterns observed in the noise results.