Using LIDAR doppler velocity data and chaotic oscillatory-based neural network for the forecast of meso-scale wind field

Current research based on various approaches including the use of numerical prediction models, statistical models and machine learning models have provided some encouraging results in the area of long-term weather forecasting. But at the level of meso-scale and even micro-scale severe weather phenomena (involving very short-term chaotic perturbations) such as turbulence and wind shear phenomena, these approaches have not been so successful. This paper focuses on the use of chaotic oscillatory-based neural networks for the study of a meso-scale weather phenomenon, namely, wind shear, a challenging and complex meteorological phenomena which has a vital impact on aviation safety. Using LIDAR data collected at the Hong Kong International Airport via the Hong Kong Observatory, we are able to forecast the Doppler velocities with reasonable accuracy and validate our prediction model. Preliminary results are promising and provide room for further research into its potential for application in aviation forecasting.