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http://dx.doi.org/10.5467/JKESS.2009.30.3.294

Prediction of Tropical Cyclone Intensity and Track Over the Western North Pacific using the Artificial Neural Network Method  

Choi, Ki-Seon (Department of Environmental Atmospheric Sciences, Pukyong National University)
Kang, Ki-Ryong (National Typhoon Center, Korea Meteorological Administration)
Kim, Do-Woo (Department of Environmental Atmospheric Sciences, Pukyong National University)
Kim, Tae-Ryong (National Typhoon Center, Korea Meteorological Administration)
Publication Information
Journal of the Korean earth science society / v.30, no.3, 2009 , pp. 294-304 More about this Journal
Abstract
A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.
Keywords
Tropical cyclone; artificial neural network; predictor; multiple linear regression model;
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Times Cited By KSCI : 2  (Citation Analysis)
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