Ternary forecast of heavy snowfall in the Honam area,Korea |
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| Authors: | Keon Tae Sohn Jeong Hyeong Lee Young Seuk Cho |
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| Institution: | Department of Statistics, Pusan National University, Busan 609-735, Korea;Division of Management Information Science, Dong-A University, Busan 604-714, Korea;Department of Statistics, Pusan National University, Busan 609-735, Korea |
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| Abstract: | The objective of this study is to improve the statistical modeling
for the ternary forecast of heavy snowfall in the Honam area in Korea. The
ternary forecast of heavy snowfall consists of one of three values, 0 for less
than 50 mm, 1 for an advisory (50--150 mm), and 2 for a warning (more than
150 mm). For our study, the observed daily snow amounts and the numerical model
outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years
(2001 to 2005) are used as observations and potential predictors respectively.
For statistical modeling and validation, the data set is divided into training
data and validation data by cluster analysis. A multi-grade logistic regression
model and neural networks are separately applied to generate the probabilities
of three categories based on the model output statistic (MOS) method. Two
models are estimated by the training data and tested by the validation data.
Based on the estimated probabilities, three thresholds are chosen to generate
ternary forecasts. The results are summarized in 3×3 contingency tables
and the results of the three-grade logistic regression model are compared to
those of the neural networks model. According to the model training and model
validation results, the estimated three-grade logistic regression model is
recommended as a ternary forecast model for heavy snowfall in the Honam area. |
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| Keywords: | ternary forecast of heavy snow MOS multi-grade logistic regression neural networks threshold |
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