Development of a recurrent sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art - the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide short-term rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Pre-processing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.