| 广东沿岸海雾决策树预报模型 |
| |
| 引用本文： | 黄健,黄辉军,黄敏辉,薛登智,毛伟康,白玉洁. 广东沿岸海雾决策树预报模型[J]. 应用气象学报, 2011, 22(1): 107-114 |
| |
| 作者姓名： | 黄健 黄辉军 黄敏辉 薛登智 毛伟康 白玉洁 |
| |
| 作者单位： | 1.中国气象局广州热带海洋气象研究所，广州 510080 |
| |
| 基金项目： | 中国气象局新技术推广项目，广东省科技三项，广东省科技厅计划项目 |
| |
| 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...
|
| 关 键 词： | 广东沿岸海雾 分类与回归树 决策树预报模型 判别流程 |
| 收稿时间： | 2010-04-29 |
Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province |
| |
| Huang Jian,Huang Huijun,Huang Minhui,Xue Dengzhi,Mao Weikang and Bai Yujie. Decision Tree Forecasting Models of Sea Fog for the Coast of Guangdong Province[J]. Journal of Applied Meteorological Science, 2011, 22(1): 107-114 |
| |
| Authors: | Huang Jian Huang Huijun Huang Minhui Xue Dengzhi Mao Weikang Bai Yujie |
| |
| Affiliation: | 1.Institute of Tropical and Marine Meteorology, CMA, Guangzhou 5100802.Guangzhou Central Meteorological Observatory, Guangzhou 5100803.Training Center of Guangdong Provincial Meteorological Bureau, Guangzhou 510080 |
| |
| 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. |
| |
| Keywords: | sea fog on the coast of Guangdong Province Classification and Regression Trees (CART) decision tree mode decision making procedure |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《应用气象学报》浏览原始摘要信息 |
|
点击此处可从《应用气象学报》下载免费的PDF全文 |
|
