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Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province
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摘要: 利用汕头、珠海和湛江地面观测站2000—2008年1—5月的海雾历史观测资料和NCEP/NCAR FNL再分析资料，采用分类与回归树 (CART) 方法对海雾及其生成前24 h的海洋气象条件进行分类分析，建立了海雾决策树预报模型；并根据现有的海雾理论认识，对海雾预报规则包含的物理意义进行讨论。10次交叉检验的结果表明：采用CART方法建立的海雾决策树预报模型有较好的预报性能，对广东沿岸海雾的预报准确率总体上可达到73%以上。根据决策树预报模型建立的海雾预报判别流程，可在业务工作中直接用于有雾/无雾判别。海雾预报判别流程同时也具有明确的物理意义，能够较好地反映水汽与海表冷却条件对平流冷却雾形成的重要性，CART方法可作为海雾业务预报的有效建模工具。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.
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图 1 地面观测站与预报参考点地理位置图
Fig. 1 The location of the surface observation stations and forecasting reference points
图 2 汕头、珠海和湛江的决策树节点数与相对成本关系
Fig. 2 The relationship between relative costs and the number of nodes for the decision trees of Shantou, Zhuhai and Zhanjiang
图 5 湛江海雾判别流程图
Fig. 5 The decision-making procedure of sea fog forecasting for Zhanjiang
表 1 用于CART方法的预报变量
Table 1 The predictor variables used in CART analysis
序号 预报变量名 海洋气象要素 1 Tsea 海表温度, 单位：℃ 2 T2 m 2 m高度气温, 单位：℃ 3 D2 m 2 m高度露点温度, 单位：℃ 4 α10 m 10 m高度风向, 单位：(°) 5 V10 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 T2 m-Tsea 气温—海表温度差, 单位：℃ 15 D2 m-Tsea 露点—海表温度差, 单位：℃ 
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表 2 决策树的预报准确率检验结果
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 1 110 13.7 17.3 86.3 83.7 珠海 0 4480 18.9 21.4 81.1 78.6 1 133 18.8 26.3 81.2 73.7 湛江 0 5031 26.8 28.5 74.2 71.5 1 412 13.4 17.6 86.6 82.4
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