Editorial Type:
Article
Article Type:
Research Article

Experiments in Short-Term Precipitation Forecasting Using Artificial Neural Networks

Robert J. Kuligowski Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania

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Ana P. Barros Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania

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Print Publication:
01 Feb 1998
DOI:
https://doi.org/10.1175/1520-0493(1998)126<0470:EISTPF>2.0.CO;2
Page(s):
470–482
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Abstract

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 700-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.

Corresponding author address: Dr. Ana Paula Barros, Dept. of Civil and Environmental Engineering, The Pennsylvania State University, 206 Sackett Building, University Park, PA 16802-1408.

Abstract

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 700-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.

Corresponding author address: Dr. Ana Paula Barros, Dept. of Civil and Environmental Engineering, The Pennsylvania State University, 206 Sackett Building, University Park, PA 16802-1408.

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