Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province

Huang Jian; Huang Huijun; Huang Minhui; Xue Dengzhi; Mao Weikang; Bai Yujie

Vol.22, NO.1, 2011

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(1): 107-114

Huang Jian, Huang Huijun, Huang Minhui, et al. Decision tree forecasting models of sea fog for the coast of Guangdong Province. J Appl Meteor Sci, 2011, 22(1): 107-114.

Citation:

Huang Jian, Huang Huijun, Huang Minhui, et al. Decision tree forecasting models of sea fog for the coast of Guangdong Province. J Appl Meteor Sci, 2011, 22(1): 107-114.

Huang Jian, Huang Huijun, Huang Minhui, et al. Decision tree forecasting models of sea fog for the coast of Guangdong Province. J Appl Meteor Sci, 2011, 22(1): 107-114.

Citation:

Huang Jian, Huang Huijun, Huang Minhui, et al. Decision tree forecasting models of sea fog for the coast of Guangdong Province. J Appl Meteor Sci, 2011, 22(1): 107-114.

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Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province

1.
Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510080
2.
Guangzhou Central Meteorological Observatory, Guangzhou 510080
3.
Training Center of Guangdong Provincial Meteorological Bureau, Guangzhou 510080

Received Date: 2010-04-29
Rev Recd Date: 2010-12-06
Publish Date: 2011-02-28

Abstract

Abstract

Sea fog is a phenomenon of water vapor condensation or sublimation in marine atmospheric boundary layer and is also one of the main disastrous weathers on the coast of Guangdong Province in spring. However, there is no suitable method for operational sea fog forecasting in Guangdong due to the complexity of physical processes involved in the formation of sea fog. Therefore, historical sea fog reports from Shantou, Zhuhai and Zhanjiang surface meteorological observation and NCEP/NCAR FNL reanalysis for the period of 2000—2008 are analyzed to explore the feasibility of sea fog forecasting with a 24-hour lead time. The relationship between marine atmospheric conditions and sea fog events is examined by Classification and Regression Trees (CART), employing the NCEP/NCAR reanalysis data 24 hours before the sea fog events. Then, the decision tree models for sea fog forecasting are developed based on results of classification analysis. Finally, the physical significance of the forecasting rules is discussed based on existing theoretical knowledge on sea fog.The validation results by 10 cross-validation show that the forecasting accuracy of sea fog decision tree models developed by CART can reach 83.7%, 73.7% and 82.4% respectively for Shantou, Zhuhai and Zhanjiang on the coast of Guangdong Province. It can be interpreted or understood easily due to the clear logical relationship. The decision-making procedure can be developed and used directly to make fog/no-fog identification in operational sea fog forecasting with clear physical meanings. It also reflects the importance of the water vapor and the cooling effect of cold sea surface in the formation of advective cooling fog well. Simple calculation processes and relatively high classification accuracy make the CART an effective tool to develop sea fog forecasting model.
- sea fog on the coast of Guangdong Province;
- Classification and Regression Trees (CART);
- decision tree mode;
- decision-making procedure

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References(28)

Figures and Tables

Fig. 1 The location of the surface observation stations and forecasting reference points

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Fig. 2 The relationship between relative costs and the number of nodes for the decision trees of Shantou, Zhuhai and Zhanjiang

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Fig. 3 The decision-making procedure of sea fog forecasting for Shantou

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Fig. 4 The decision-making procedure of sea fog forecasting for Zhuhai

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Fig. 5 The decision-making procedure of sea fog forecasting for Zhanjiang

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Table 1 The predictor variables used in CART analysis

序号	预报变量名	海洋气象要素
1	T_sea	海表温度, 单位：℃
2	T_{2 m}	2 m高度气温, 单位：℃
3	D_{2 m}	2 m高度露点温度, 单位：℃
4	α_{10 m}	10 m高度风向, 单位：(°)
5	V_{10 m}	10 m高度风速, 单位：m/s
6	T₁₀₀₀	1000 hPa高度气温, 单位：℃
7	D₁₀₀₀	1000 hPa高度露点温度, 单位：℃
8	α₁₀₀₀	1000 hPa高度风向, 单位：(°)
9	V₁₀₀₀	1000 hPa高度风速, 单位：m/s
10	T₈₅₀	850 hPa高度气温, 单位：℃
11	D₈₅₀	850 hPa高度露点温度, 单位：℃
12	α₈₅₀	850 hPa高度风向, 单位：(°)
13	V₈₅₀	850 hPa高度风速, 单位：m/s
14	T_{2 m}-T_sea	气温—海表温度差, 单位：℃
15	D_{2 m}-T_sea	露点—海表温度差, 单位：℃

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Table 2 The testing results for the classification/forecasting accuracy of sea fog decision trees

站点	类别	样本数	训练误分率/%	验证误分率/%	训练成功率/%	验证成功率/%
汕头	0	2267	31.8	32.2	68.2	67.8
汕头	1	110	13.7	17.3	86.3	83.7
珠海	0	4480	18.9	21.4	81.1	78.6
珠海	1	133	18.8	26.3	81.2	73.7
湛江	0	5031	26.8	28.5	74.2	71.5
湛江	1	412	13.4	17.6	86.6	82.4

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