Outdoor sound propagation can be modeled numerically, but it is difficult to validate the results using measurements. The main objective of this master’s thesis was to quantify meteorological effects on outdoor sound propagation using noise measurements and simple weather data obtained by the Hamburg airport for air traffic control purposes. 91,000 A320 measurements from the period august 22 - august 23 were recorded. This data is supplemented by weather profiles measured at the Hamburg Weathermast. To analyze the data, we first perform a Principal Component Analysis (PCA) to investigate the correlation of the combined noise, flight and weather parameters. The Analysis of Variance (ANOVA) method is used to investigate whether there are significant differences in the A-weighted maximum sound pressure level LAS,max values between different meteorological parameters. In addition, we use the mean decrease in impurity (MDI) and permutation importance of a random forest model to evaluate how weather parameters can be used in the prediction of noise from aircraft overflights. A positive and negative temperature gradient differed on average by 2.13 - 2.61 dB, p-value < 0.05. A measured difference of 1.2 dB on average, p-value 0.00, was found between the upwind and downwind classes. When predicting noise with a Random Forest model, the removal the weather parameters would lead to a loss of 14.85% accuracy on the training data. Overall, the noise can be predicted with an accuracy of 1 dB at a specific measuring station for the A320 type depending on the state of the source, e.g. aircraft configuration and flight procedures. From the measurement data, it is possible to derive audible differences for humans depending on the weather conditions. However, we can’t yet predict noise for all aircraft types and locations.