Reprint 898 Forecasting Headwind Profiles and Low Level Windshear Using LIDAR Velocity Data and a Chaotic Oscillatory-based Neural Network
نویسندگان
چکیده
Two Doppler LIght Detection And Ranging (LIDAR) systems have been operated by the Hong Kong Observaotry (HKO) at the Hong Kong International Ai rport (H KIA) for the det ection and alerting of low-level windshear to be encountered by the aircraft. The windshear al erting algorithm is based on the automatic identification of abrupt changes of headwinds along the glide paths of HKIA, which are measured by the glide-path scans of the LIDARs. To give earlier windshear alerts to the aircraft, forecasting of the headwind profiles would be required. The present paper discusses the forecast of headwinds based on the past LIDAR data and a chaotic oscillatory neural network (CONN). The LIDAR’s headwind data in the previous 30 days or so are used to train the CONN, which is then used to forecast the headwind profiles in the next hour. For two selected cases as presented in the paper, the CONN forecasts successfully capture the evolution of the headwind profiles. Moreover, the use of CONN fore cast to give windshear alerts is demonstrated in one sea breeze case. The forecast alerts are generally comparable with those based on the actual LIDAR observations. As such, based on the limited number of sea-breeze induced windshear episodes considered in the paper, the application of CONN to LIDAR data has the potential of forecasting the major features of the evolution of the headwind profiles.
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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