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 a stochastic sampling technique called '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. Uncertainties in humidity, temperature and wind profiles are considered. It is shown that the uncertainty in temperature profile seems to have a stronger influence on the received SPLs as compared to the effect of uncertainty in wind profile. Uncertainties in the meteorological conditions are shown to have a greater impact on the noise levels received near the ground (such as the average SPLs measured by a sound level meter), as the aircraft height is increased.
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