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http://dx.doi.org/10.3741/JKWRA.2006.39.8.717

Forecast of Areal Average Rainfall Using Radiosonde Data and Neural Networks  

Kim Gwang-Seob (Dept. of Civil Eng., Kyungpook National University)
Publication Information
Journal of Korea Water Resources Association / v.39, no.8, 2006 , pp. 717-726 More about this Journal
Abstract
In this study, we developed a rainfall forecasting model using data from radiosonde and rain gauge network and neural networks. The primary hypothesis is that if we can consider the moving direction of the rain generating weather system in forecasting rainfall, we can get more accurate results. We assume that the moving direction of the rain generating weather system is same as the wind direction at 700mb which is measured at radiosonde networks. Neural networks are consisted of 8 different modules according to 8 different wind directions. The model was verified using 350 AWS data and Pohang radiosonde data. Correlation coefficient is improved from 0.41 to 0.73 and skill score is 0.35. Statistical performance measures of the Quantitative Precipitation Forecast (QPF) model show improved output compared to that of rainfall forecasting model using only AWS data.
Keywords
Rainfall forecast; Automatic Weather Station; Radiosonde; Neural Networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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