NNICE – a neural network aircraft icing algorithm

Abstract

Although much is known about the meteorological conditions for significant aircraft icing, research studies to date have only been successful identifying conditions for general icing, i.e. clouds in the temperature range from 0 °C to about −20 °C. The aerodynamics of ice accumulation suggest three meteorological factors, cloud liquid water, droplet size, and air temperature. Only the latter is known or forecast with a significant degree of accuracy. The first two are partial functions of the atmosphere's vertical motion which is poorly known, especially in convective situations. However, favorable patterns of relative humidity and potential instability that indicate conditions for possible convection are readily discernable from sounding or numerical forecast model data. Two neural networks were taught to sort out these patterns with respect to icing intensity. Each uses a different neuron transfer function which gives each a “personality”. The “conservative” network diagnoses light icing well but has difficulty with moderate or greater icing. On the other hand, the “radical” network finds the higher intensity icing but is not as good at lower intensities. By combining the strengths of each, NNICE makes skillful icing forecasts of all intensities.