A Novel Neural Based Rainfall Nowcasting System in Hong Kong

Tommy W.S. Chow, S. Y. Cho

Research output: Journal PublicationArticlepeer-review

Abstract

This paper describes the development of a new approach of rainfall nowcasting (very short term forecasting) by using a neural network. This approach consisted of extracting the information from radar images and evaluating past rain gauge records to provide short term rainfall forecasting. All meteorology data were provided by the Royal Observatory of Hong Kong (ROHK). Pre-processing procedures were essential for this neural network rainfall nowcasting. The forecast of rainfall every half an hour is such that a storm warning signal can be delivered to the public in advance. The network architecture was based on a recurrent Sigma-Pi network. The results are very promising and this neural based rainfall nowcasting system was capable of providing a reliable rain storm warning signal to the Hong Kong public in advance.

Original languageEnglish
Pages (from-to)245-264
Number of pages20
JournalJournal of Intelligent Systems
Volume7
Issue number3-4
DOIs
Publication statusPublished - Dec 1997
Externally publishedYes

Keywords

  • geographical cells
  • Logarithm scale weighting factor
  • Rain Storm Colour Code Warning Signal
  • Rainfall Nowcasting
  • Recurrent Sigma-Pi Network
  • Terrain characteristics

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Artificial Intelligence

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