Aircraft noise and its impact is one of the biggest challenges the aviation industry faces today. At airports, the number of operations and the flight routes are driven by the noise impact on the surrounding communities. To predict the expected aircraft noise levels around airports, noise models are used. The accuracy of these models is thus of great importance. This research aims to improve aircraft noise modelling by validation and calibration of the main input parameters using acoustic measurements. This research is focused on the Doc.29 aircraft noise model. This is an empirical model, based on Noise-Power-Distance (NPD) tables, which is convenient for fast computations of a large number of flight operations. The data set used consists of take-off measurements of the B737-800 gathered around Schiphol airport. To validate and calibrate the model with measurements, all input parameters should be correct to be able to identify where deviations come from. The power parameter thrust is an important input, but often not exactly known. The N1 rotational speed of the engines can be used to estimate the used thrust. An estimation of the N1% is performed by finding the fan tones from acoustic measurements. The found N1% is validated with Aircraft Condition and Monitoring System data (ACMS) from the measured flights. The estimation resulted in an average 4% root mean square error with the ACMS data. When using the estimated N1% as input for thrust, differences between model results and measurement are still found. These differences are assumed to be caused by errors in the NPD table. This allows altering the entries in the NPD table by adding the found differences from the measurements reduces the mean error to less than ±0.2 dBA and providing a 20-30% reduction in standard deviation. Another method of model improvement is done by looking at the measurements which have been standardised to reference conditions. The relation between thrust setting and sound level showed to be less dominant than originally expected from the NPD table. From these newfound thrust-noise relations, new NPD tables are created. This caused a 30-40% reduction in standard deviation, reducing the variation found between noise measurements and model results significantly. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD values.