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 l...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 ram）, 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.展开更多
基金This research was performed for the project "Development of technique for Local Prediction", one of the research and development projects on meteorology and seismology funded by the Korea Meteorological Administration (KMA), 2005.
文摘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 ram）, 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.
