Abstract
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 shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing 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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Guoxiang Y, Hanscheng L, Qiqiang H. A meso-∂-scale of Meiyu front heavy rain — part II: The dynamical analysis of rain-band disturbance. Advances in Atmospheric Science 1987; 4(4): 485–495
Rodrignez Iturbe I, Eagleson PS. Mathematical models of rain storm events in space & time. Water Resource Res 1987; 23(1): 181–190
Collier CG, Goddard DM, Con way BJ. Real-time analysis of prediction using satellite imagery, ground-based radars conventional observations and numerical model output. Meteorol Mag (1989); 118(1398): 1–8
Yeung KK, Chang WL. Numerical simulation of mesoscale meteorological phenomena in Hong Kong. Proc Int Conf on East Asia and Western Pacific Met & Climate, Hong Kong 1989; 451–460
McCann DW. Forecasting techniques, a neural network short-term forecast of significant thunderstorm. Weather & Forecasting 1992; 7(3)
Chow TWS, Gou F. Recurrent Sigma-Pi-Linked back-propagation neural network. Neural Processing Letters 1994; 1(2): 5–8
Lam CY. Digital Radar Data as an Aid in Nowcasting in Hong Kong. Proc Nowcasting-II Symposium, Norrkoping, Sweden, 3–7 September 1984
Chow TWS, Leung CT. Neural network Piecewise Linear preprocessing for time-series prediction. Proc European Symposium on Artificial Neural Networks, Brussels, Belgium, April 1995; 327–332
Austin PM. Relation between measured radar reflectivity and Surface rainfall. Mon Weather Rev 1987; 115: 1053–1071
Pearlmutter BA. Gradient calculation for dynamic recurrent neural networks: a survey. IEEE Trans Neural Networks 1995; 6(5): 1212–1228
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Chow, T.W.S., Cho, S.Y. Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong. Neural Comput & Applic 5, 66–75 (1997). https://doi.org/10.1007/BF01501172
Issue Date:
DOI: https://doi.org/10.1007/BF01501172