摘要
利用2008—2016年舟山4个海岛气象站大风资料和欧洲中心的ERA-interim再分析资料,分析了阵风因子随平均风速、风向、小时、月份等分布的气候特征,统计阵风因子与边界层的大气稳定度、250～1000m风速与10m风速的比值、6h变温等要素的相关后,选取最佳预报因子,利用BP人工神经网络方法,根据不同因子组合对阵风进行循环试报。结果表明:①平均风速较小时阵风因子波动范围大;靠近大陆站点的阵风因子及来自陆地方向气流的阵风因子偏大。②白天11:00—16:00受太阳辐射影响大气湍流相对较强,阵风因子偏大。7—9月沿海受台风影响频繁,其阵风因子要大些,而11—12月阵风因子偏大则与来流方向的地表粗糙度较大有关。③阵风因子和边界层不同高度的风速与10m风速比值,及气温具有明显正相关,与边界层大气稳定度参数具有负相关,相关结果印证了阵风主要物理成因与动量的垂直湍流输送有关。④阵风循环试报表明最佳组模型试报的绝对误差及方差均比对比组模型减少约11%～25%,具有较好的预报效果。
Based on the wind data at four weather stations in Zhoushan and ERA-interim data during 2008-2016,the climatic characteristics of gust factors with the distribution of mean wind speed,wind direction,hour and month are analyzed respectively.The correlation analysis is carried out between gust factors and atmospheric stability,the ratio of the wind speed at the levels of 250 to 1000 mto the wind speed of 10 m,and the 6-h temperature variation at different levels in the atmospheric boundary layer.Then the best predictors are selected and the cyclic tests of gust forecast are conducted based on the BP artificial neural network method.The results show that(1)When the mean wind speed is small,the gust factors fluctuate greatly.The gust factors of air flows near the mainland or from the land direction are larger.(2)The gust factors are relatively larger in 11:00-16:00,when the turbulence affected by solar radiation is relatively strong.The gust factors from July to September and November to December are also relatively larger owing to typhoon influences or the larger surface roughness respectively.(3)The relations of the gust factors with the ratio of the wind speed of 250-1000 mto the wind speed of 10 mand temperature present positive correlation,and there presents negative correlation with stability parameters at the atmospheric boundary layer.It proves that the main physical cause of the gust is relevant to the vertical turbulent transport of momentums.(4)The cyclic tests reveal the important role of stability and turbulence at the atmospheric boundary layer in gust forecast.The gust absolute errors and their variances of the optimum groups are reduced by 10%to 25%,compared with those of comparison groups at four stations.
作者
胡波
Hu Bo(Zhejiang Meteorological Observatory,Hangzhou 310017)
出处
《气象科技》
2019年第2期282-288,共7页
Meteorological Science and Technology
基金
浙江省重大科技专项重点社会发展项目(2011C13044)
浙江省自然科学基金(LY18D050001)
浙江省气象局科技计划重点项目(2017ZD09)
浙江省气象局科技计划一般项目(2018YB01)资助
关键词
阵风因子
大气稳定度
边界层
人工神经网络
湍流
gust factor
atmospheric stability
boundary Layer
artificial neural network
turbulence
作者简介
胡波,男,1975年生,硕士,副高,主要从事数值预报解释应用,Email:hubook12@163.com。
