Wind Shear Forecasting by Chaotic Oscillatory-based Neural Networks (CONN) with Lee Oscillator (Retrograde Signalling) Model

Abstract

Wind shear is a conventionally unpredictable meteorological phenomenon which presents a common danger to aircraft, particularly on takeoff and landing at airports. This paper describes a method for forecasting wind shear using an advanced paradigm from computational intelligence, chaotic oscillatory-based neural networks (CONN). The method uses weather data to predict wind velocities and directions over a short time period. This approach may have a wide variety of applications but from the aviation forecast perspective, it can be used in aviation to generate wind shear alerts.