An Artificial Neural Network with Chaotic Oscillator for Wind Shear Alerting

Abstract

Current research based on various approaches including the use of numerical weather prediction models, statistical models, and machine learning models have provided some encouraging results in the area of longterm weather forecasting. But at the level of mesoscale and even microscale severe weather phenomena (involving very short-term chaotic perturbations) such as turbulence and wind shear phenomena, these approaches have not been so successful. This research focuses on the use of chaotic oscillatory-based neural networks for the study of a mesoscale weather phenomenon, namely, wind shear, a challenging and complex meteorological problem that has a vital impact on aviation safety. Using lidar data collected at the Hong Kong International Airport via the Hong Kong Observatory, it is possible to forecast the Doppler velocities with satisfactory accuracy and validate the prediction model with the potential to generate the wind shear alert. Experimental results are found to be comparable to the actual measurement. Moreover, the selected testing cases and results show that the value of correlation coefficient between the predicted and lidar-measured wind velocities exceeds 0.9 with various window sizes ranging from 1 to 3 h. These provide areas for further research of the proposed model and lidar technology for turbulence and wind shear forecasts.

Intelligent Low-Level Wind Shear Alert Prediction System Based on Anemometer Sensor Network and Temporal Convolutional Network (Tcn)

“…Recent research regarding wind shear prediction is dominated by numerical and Geostatistical methods. Geostatistical method used for wind shear prediction dominated by Machine Learning (ML) (Bolgiani et al, 2020;Chan and Hon, 2016;Hou and Wang, 2019;Kwong et al, 2012;Lee et al, 2020;Liu et al, 2012;Wong et al, 2008;Yan et al, 2020). Previous studies suggest numerical methods tend to have longer lead time but need massive computation power and the ML method doesn't need extensive computing resources and produces faster prediction but a shorter lead time.…”

Deep Neural Network Modeling for Big Data Weather Forecasting

“…Many significant research efforts are utilized to develop weather forecasting methods including computational intelligence technologies that have been accepted as appropriate means for weather forecasting and reported encouraging results since 1980s [6,7,17,19,21,32]. However, the coming of the big data era brings the opportunities to improve the forecasting accuracy of weather phenomena in advance.…”