安徽省电线积冰标准冰厚的气象估算模型
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摘要
基于逐步多元线性回归和人工神经网络两种方法,利用安徽省有电线积冰观测的15个气象台站建站至2008年的观测资料,建立了安徽省3个不同区域电线积冰标准冰厚的气象估算模型。结果表明:相比人工神经网络模型,逐步多元线性回归模型预测效果较好;在覆冰机理认识上,印证了影响标准冰厚主要是气温、湿度和风速3个因子的配置,其中气温是影响覆冰的最重要因子;平原和丘陵地区的标准冰厚受当日气象条件影响更多,而高山地区与前几日及当日的气象条件均密切相关,且26个气象因子(1987—2008年资料)构建的模型的预测效果好于24个气象因子长序列(建站—2008年资料)效果。最后利用最优模型推算各区域非观冰站电线积冰标准冰厚,为冰冻灾害的评估以及风险区划的开展提供了基础。
An extreme frozen ice and snow disaster brings severe losses in Anhui Province during early 2008. Some overhead transmission lines are destroyed seriously during this event.It is important to investigate the characteristics of ice accumulation and develop appropriate models to estimate the disaster,but the research doesn't go smoothly due to the lack of data.If a quantitative relationship can be built between the meteorological elements and the ice thickness by the wire icing observation data of fewer stations,the icecovered mechanism can be understood better,which can also lay the foundation for disaster risk regionalization. Based on the long-term wire icing observation data and climate data of 16 meteorological stations in recent 50 years,several models are built by stepwise multiple linear regression and artificial neural networks, to calculate standard ice thickness of wire icing in Anhui Province. Through coordinated experiment,the optimal models built by stepwise multiple linear regression are confirmed which handle temperature,humidity,wind speed of the icing day,one day before and two days before.The models by artificial neural network perform better in simulating comparatively,but in the prediction they are very unstable,and less effective.The model driven by 26 meteorological factors(data from 1987 to 2008) perform better than the model driven by 24 factors(data of from 1960 to 2008).The best models are picked out by the results of simulating and predicting.And the absolute deviation is 1.1 mm to the north of the Huaihe River.1.3 mm to the south of the Huaihe River,6.6 mm in the area of high mountains,and the relative deviation rates of the three models are about 60%—70%.The result is better than other references.For the mechanism of icing,temperature,humidity and wind are key factors, among which temperature is the most influential one.The ice thickness of the plains and hilly areas are mainly affected by weather conditions of the day,while that of mountain areas are also affected by the weather conditions of a few days before.Finally,the ice-wire thickness at the meteorological stations without wire icing observation is calculated by the optimal model,which proves this method feasible.
An extreme frozen ice and snow disaster brings severe losses in Anhui Province during early 2008. Some overhead transmission lines are destroyed seriously during this event.It is important to investigate the characteristics of ice accumulation and develop appropriate models to estimate the disaster,but the research doesn't go smoothly due to the lack of data.If a quantitative relationship can be built between the meteorological elements and the ice thickness by the wire icing observation data of fewer stations,the icecovered mechanism can be understood better,which can also lay the foundation for disaster risk regionalization. Based on the long-term wire icing observation data and climate data of 16 meteorological stations in recent 50 years,several models are built by stepwise multiple linear regression and artificial neural networks, to calculate standard ice thickness of wire icing in Anhui Province. Through coordinated experiment,the optimal models built by stepwise multiple linear regression are confirmed which handle temperature,humidity,wind speed of the icing day,one day before and two days before.The models by artificial neural network perform better in simulating comparatively,but in the prediction they are very unstable,and less effective.The model driven by 26 meteorological factors(data from 1987 to 2008) perform better than the model driven by 24 factors(data of from 1960 to 2008).The best models are picked out by the results of simulating and predicting.And the absolute deviation is 1.1 mm to the north of the Huaihe River.1.3 mm to the south of the Huaihe River,6.6 mm in the area of high mountains,and the relative deviation rates of the three models are about 60%—70%.The result is better than other references.For the mechanism of icing,temperature,humidity and wind are key factors, among which temperature is the most influential one.The ice thickness of the plains and hilly areas are mainly affected by weather conditions of the day,while that of mountain areas are also affected by the weather conditions of a few days before.Finally,the ice-wire thickness at the meteorological stations without wire icing observation is calculated by the optimal model,which proves this method feasible.
引文
[1]国家气候中心.2008年初我国南方低温雨雪冰冻灾害及气候分析.北京:气象出版社,2008:20.
[2]鲁俊,吴必文,卢燕宇.安徽省电线积冰的特征及气象条件分析.安徽农业科学,2008,36(24):10570-10572.
[3]吴素良,蔡新玲,何晓嫒,等.陕西省电线积冰特征.应用气象学报,2009,20(2):24 7-250.
[4]张国庆,张加昆,祁栋林,等.青海东部电线积冰的初步观测分析.应用气象学报,2006,1 7(4):508-510.
[5]罗宁,文继芬,赵彩,等.导线积冰的云雾特征观测研究.应用气象学报,2008,19(1):91-95.
[6]吴素良,范建勋,姜创业,等.兰州至关中电线积冰与导线线径及高度关系.应用气象学报,2010,21(4):63-69.
[7]蒋兴良.输电线路导线覆冰机理和三峡地区覆冰规律及影响因索研究.重庆:重庆大学,1 997.
[8]Makkonn L.Estimation intensity of atmosphere ice accretion on stationary structures.JApplMeteor,1981,20:595-600.
[9]Sundin E,Makkonn L.Modeling of ice accretion on wires.J ClimateAppl Meteor,1984,23:929-939.
[10]殷水清,赵姗姗,王遵娅,等.全国电线结冰厚度分布及等级预报模型.应用气象学报,2009,20(6):722-728.
[11]谢云华.三峡地区导线覆冰与气象要素的关系.中国电力, 2005,38(3):35-39.
[1 2]蒋兴良,孙才新,顾乐观,等.三峡地区导线覆冰的特性及雾凇覆冰模型.重庆大学学报(自然科学版),1998,21(2):18-21.
[13]魏风英.现代气候统计诊断与预测技术(第二版).北京:气象出版社,2007:213-235.
[14]Maralbashi Zamini S.Developing Neural Network Models to Predict Ice Accretion Type and Rate on Overhead Transmission Lines.http://dx.doi.org/doi:10.1522/030012635, 2007.
[15]谭冠日,严济远,朱瑞兆.应用气候.上海:上海科学技术出版社,1980.
[16]谭冠日.电线积冰若干小气候特征的探讨.气象学报,1982,40 (1):1 3-23.
[1 7]吕振通,张凌云.SPSS统计分析与应用.北京:机械工业出版社,2009.
[18]田红,李春,张士洋.近50年我国江淮流域气候变化.中国海洋大学学报,2005,35(4):539-544.
[2]鲁俊,吴必文,卢燕宇.安徽省电线积冰的特征及气象条件分析.安徽农业科学,2008,36(24):10570-10572.
[3]吴素良,蔡新玲,何晓嫒,等.陕西省电线积冰特征.应用气象学报,2009,20(2):24 7-250.
[4]张国庆,张加昆,祁栋林,等.青海东部电线积冰的初步观测分析.应用气象学报,2006,1 7(4):508-510.
[5]罗宁,文继芬,赵彩,等.导线积冰的云雾特征观测研究.应用气象学报,2008,19(1):91-95.
[6]吴素良,范建勋,姜创业,等.兰州至关中电线积冰与导线线径及高度关系.应用气象学报,2010,21(4):63-69.
[7]蒋兴良.输电线路导线覆冰机理和三峡地区覆冰规律及影响因索研究.重庆:重庆大学,1 997.
[8]Makkonn L.Estimation intensity of atmosphere ice accretion on stationary structures.JApplMeteor,1981,20:595-600.
[9]Sundin E,Makkonn L.Modeling of ice accretion on wires.J ClimateAppl Meteor,1984,23:929-939.
[10]殷水清,赵姗姗,王遵娅,等.全国电线结冰厚度分布及等级预报模型.应用气象学报,2009,20(6):722-728.
[11]谢云华.三峡地区导线覆冰与气象要素的关系.中国电力, 2005,38(3):35-39.
[1 2]蒋兴良,孙才新,顾乐观,等.三峡地区导线覆冰的特性及雾凇覆冰模型.重庆大学学报(自然科学版),1998,21(2):18-21.
[13]魏风英.现代气候统计诊断与预测技术(第二版).北京:气象出版社,2007:213-235.
[14]Maralbashi Zamini S.Developing Neural Network Models to Predict Ice Accretion Type and Rate on Overhead Transmission Lines.http://dx.doi.org/doi:10.1522/030012635, 2007.
[15]谭冠日,严济远,朱瑞兆.应用气候.上海:上海科学技术出版社,1980.
[16]谭冠日.电线积冰若干小气候特征的探讨.气象学报,1982,40 (1):1 3-23.
[1 7]吕振通,张凌云.SPSS统计分析与应用.北京:机械工业出版社,2009.
[18]田红,李春,张士洋.近50年我国江淮流域气候变化.中国海洋大学学报,2005,35(4):539-544.
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