LIDAR and Chaotic Oscillatory-based Neural Network for Wind Shear Forecasting
نویسندگان
چکیده
Wind shear, which refers to sudden and sustained changes in the wind direction and speed, could be hazardous to aviation. Windshear is rather difficult to predict due to its transient and sporadic nature. Moreover, the causes of wind shear may be different at different airports. In some places it is caused by microbursts, while in other places wind shear may result from meso-scale weather phenomena and terrain effect. Thus, algorithms and techniques used to predict wind shear caused by microbursts, as in [1], may not be applicable at another airport where wind shear and turbulence arise from other meteorological conditions. This paper focuses on the use of chaotic oscillatory-based neural networks (CONN) for predicting wind shear arising from meso-scale weather phenomenon at the Hong Kong International Airport. Using historical weather data from the Hong Kong Observatory, simulations show that CONN is able to forecast wind shear with a reasonable level of accuracy for a sea breeze event.
منابع مشابه
Reprint 759 Short-term Wind Forecasting at the Hong Kong International Airport by Applying Chaotic Oscillatory-based Neural Network to LIDAR Data
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