| 双流机场低能见度天气预报方法研究 |
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| 引用本文： | 冯汉中,陈永义,成永勤,罗可生.双流机场低能见度天气预报方法研究[J].应用气象学报,2006,17(1):94-99. |
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| 作者姓名： | 冯汉中 陈永义 成永勤 罗可生 |
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| 作者单位： | 1.云南大学资环学院, 昆明 650000 |
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| 摘 要： | 在信息量较大, 而预报对象与预报因子的关系又不清楚的状况下, 智能机器学习方法是解决这类问题的较好手段。利用1997—2001年成都站的常规探空资料和双流机场的地面观测资料, 使用支持向量机 (Support Vector Machines, 简称SVM) 方法, 选取多种核函数进行双流机场低能见度天气的预报建模试验。测试结果表明:以径向基函数和拉普拉斯函数构造的SVM预报模型实验效果最好, Ts评分分别为0.287和0.292, 远高于双流机场低能见度天气出现的频率 (0.155)。试验结果还表明:以径向基函数构造的SVM预报模型空报较多, 漏报较少; 而以拉普拉斯函数构造的SVM预报模型空报较少, 漏报较多。因此, 如果强调模型对低能见度天气预报的准确性, 则应采用以拉普拉斯函数构造的预报模型, 如果强调对低能见度天气的预防性, 则应采用以径向基函数构造的预报模型。
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| 关 键 词： | 低能见度天气 支持向量机 预报方法 |
| 收稿时间： | 2004-12-28 |
| 修稿时间： | 2005-04-13 |
A Study on the Forecast Method of the Low Visibility Weather of Shuangliu Airport |
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| Feng Hanzhong,Chen Yongyi,Cheng Yongqin,Luo Kesheng.A Study on the Forecast Method of the Low Visibility Weather of Shuangliu Airport[J].Quarterly Journal of Applied Meteorology,2006,17(1):94-99. |
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| Authors: | Feng Hanzhong Chen Yongyi Cheng Yongqin Luo Kesheng |
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| Institution: | 1.Institute of Resource and Circumstances of Yunnan University, Kunming 6500002.Sichuan Provincial Meteorological Observatory, Chengdu 6100723.China Meteorological Administration Training Center, Beijing 1000814.Chengdu Shuangliu Airport Management Center, Chengdu 610000 |
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| Abstract: | At Shuangliu International Airport the frequent,long-time low visibility often leads to the delay of scheduled flight and affects the safety of the planes.With the development of social economy and the air transport,the low visibility weather of Shuangliu Airport arouses great concern of the air control authority of the airport.Because a number of the factors can result in the low visibility weather,the factor mechanism is complex,and meanwhile the correlativity between the factors and the low visibility weather is insignificant,meteorologists are concerned most about how to forecast such weather as possible as they can.While there are huge amounts of information,the relation between the forecast object and the factors is unclear.Given this,intelligent machine learning technique is a good method to solve this sort of problems.In order to improve the forecast,Support Vector Machines(SVM),an intelligent machine learning technique that can solve the nonlinear problems is employed in the research to study the forecast method of the low visibility weather of the Shuangliu Airport.SVM method nonlinear is mapped by kernel function from lower dimensional space to higher.The higher nonlinear correlativity between the factors and the forecast object is indirectly expressed in implicit expression.Finally,the dependence of the factor and the object is depicted,and the model is set up by support vector.Hence,the model is not only related to the forecasting factor,but the kernel function as well.By using the data from Chengdu radiosound observatory and Shuangliu Airport surface observatory through 1997 to 2001,the forecast models of low visibility weather of Shuangliu Airport are built by mean SVM method with several kernel functions.Test results show the forecast model constructed with the radial base kernel function and the forecast model constructed with Laplace kernel function are better than the others,in which Threat Score are 0.287 and 0.292 separately,far above the frequency(0.155) of low visibility weather occurring at Shuangliu Airport.Test results also show,in the SVM model constructed with the radial base kernel function,the false alarm is higher and the omitted alarm is lower;in the SVM model constructed with Laplace kernel function,it is opposite.Therefore,if the accuracy of low visibility weather forecast is emphasized,the model constructed with Laplace kernel function should adopted;but if the precaution is emphasized,the model constructed in radial base function should be selected. |
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| Keywords: | low visibility weather Support Vector Machines forecasting method |
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