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http://dx.doi.org/10.3741/JKWRA.2011.44.2.097

Application of Artificial Neural Network to Improve Quantitative Precipitation Forecasts of Meso-scale Numerical Weather Prediction  

Kang, Boo-Sik (Dept. of Civil & Environmental Engineering Univ., Dankook)
Lee, Bong-Ki (Dohwa Consulting Engineers CO.,LTD.)
Publication Information
Journal of Korea Water Resources Association / v.44, no.2, 2011 , pp. 97-107 More about this Journal
Abstract
For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.
Keywords
quantitative precipitation prediction; meso-scale numerical weather prediction; artificial neural network; COE; CORR;
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Times Cited By KSCI : 2  (Citation Analysis)
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