Quantifying the Effect of Uncertainty in Meteorological Conditions on Aircraft Noise Propagation

Accurate prediction of aircraft noise is of importance for complying with noise regulations, and also for planning infrastructure around airports. Methods used in practice to predict aircraft noise vary considerably in complexity. The accuracy of noise level predictions can be affected by uncertainty in the input parameters (such as the mean meteorological profiles), even when high fidelity propagation models are used. This work attempts to address the effect of uncertainties in the aircraft noise propagation path (meteorological conditions) on the predicted noise levels. This is achieved with the help of stochastic sampling techniques such as Monte Carlo and Latin hypercube sampling, in conjunction with Crank-Nicolson Parabolic Equation (CNPE) method. The methodology presented in Wilson et al. (JASA, 2014) is extended to the geometry of aircraft noise propagation. The effect of uncertainties in the propagation path for each individual uncertainty (such as temperature, wind speed, wind direction) is presented along with the combined effect of incorporating multiple uncertain variables while making the prediction. [Work supported by the FAA. The opinions, findings, conclusions, and recommendations expressed in this material are those of the authors and do not necessarily reflect the views of ASCENT FAA Center of Excellence sponsor organizations.]