Experiments in short-term precipitation forecasting using artificial neural networks

Published

Journal Article

Accurate, timely, site-specific forecasts of precipitation are important for accurately predicting streamflow and flash floods in small drainage basins. However, presently available numerical weather prediction models do not generally provide forecasts with the accuracy and/or resolution appropriate for this task. A wide variety of approaches to small-scale, short-term precipitation forecasting have been investigated by numerous authors: this paper describes a simple precipitation forecasting model based on artificial neural networks. The model uses the radiosonde-based V(M)-HPa wind direction and antecedent precipitation data from a rain gauge network to generate short-term (0-6 h) precipitation forecasts for a target location. The performance of the model is illustrated for a gauge in eastern Pennsylvania.

Full Text

Duke Authors

Cited Authors

  • Kuligowski, RJ; Barros, AP

Published Date

  • January 1, 1998

Published In

Volume / Issue

  • 126 / 2

Start / End Page

  • 470 - 482

International Standard Serial Number (ISSN)

  • 0027-0644

Digital Object Identifier (DOI)

  • 10.1175/1520-0493(1998)126<0470:EISTPF>2.0.CO;2

Citation Source

  • Scopus