黄山冬半年云海预报研究
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摘要
利用2004—2014年黄山气象站地面气象观测资料和NCEP/NCAR再分析资料,分析了云海发生时气温、气压、湿度、风速等要素的垂直分布、时间演变特征,选取能够反映云海天气特征的指标作为预报因子,分析预报因子分布特征,确定其阈值及消空指标,采用指标叠套法建立了黄山冬半年各月08时云海预报模型,总体正确率为88%,T_S评分为31%。利用2015—2016年资料进行检验,总体正确率为88%,T_S评分为36%,其中3月、10月T_S评分为44%,1月T_S评分为38%,模拟结果对黄山冬半年的云海预报具有一定的参考价值。
In order to improve the forecast accuracy of cloud landscape in mountainous scenic spot,the temporal change and the vertical distribution characteristics of some meteorological elements such as temperature,humidity and so on were first analyzed based on the observed cloud data from the Huangshan weather station and the NCEP/NCAR reanalysis data during 2004-2014. Then,the forecast factors reflecting the cloud landscape weather features were selected,and their thresholds and negative indexes were determined by analyzing the distribution characteristics of the forecast factors. Finally,the forecast model of cloud landscape at 08: 00 during winter halfyear was developed with index accumulation method and its total forecasting accuracy and T_S grade was 88% and 31%,respectively. In addition,the model was further verified using the data from 2015 to 2016. As a result,the forecast accuracy of cloud landscape is 88% and the total T_S grade is 36%. Specifically,T_S grades in March and October are 44% and that in January is 38%. In conclusion,the model simulation results can provide available reference to the forecast of cloud landscape in Huangshan Mountain in winter half-year.
In order to improve the forecast accuracy of cloud landscape in mountainous scenic spot,the temporal change and the vertical distribution characteristics of some meteorological elements such as temperature,humidity and so on were first analyzed based on the observed cloud data from the Huangshan weather station and the NCEP/NCAR reanalysis data during 2004-2014. Then,the forecast factors reflecting the cloud landscape weather features were selected,and their thresholds and negative indexes were determined by analyzing the distribution characteristics of the forecast factors. Finally,the forecast model of cloud landscape at 08: 00 during winter halfyear was developed with index accumulation method and its total forecasting accuracy and T_S grade was 88% and 31%,respectively. In addition,the model was further verified using the data from 2015 to 2016. As a result,the forecast accuracy of cloud landscape is 88% and the total T_S grade is 36%. Specifically,T_S grades in March and October are 44% and that in January is 38%. In conclusion,the model simulation results can provide available reference to the forecast of cloud landscape in Huangshan Mountain in winter half-year.
引文
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[2]吴有训,杨保桂,王克强,等.黄山云海的天气气候分析[J].气象科学,2005,25(1):97-104.
[3]吴有训,王克强,杨保桂,等.黄山连续性云海过程的天气学分析[J].气象,2005,31(4):73-76.
[4]单权,冯国标,梁晓妮.雁荡山云海的时空变化特征及其与气象因子的关系[J].浙江气象,2014,35(2):34-37.
[5]乔舒婷,达勇,曹慧萍.华山云海的时间变化及其气象条件分析[J].陕西气象,2016(6):27-30.
[6]马学款,蔡芗宁,杨贵名,等.重庆市区雾的天气特征分析及预报方法研究[J].气候与环境研究,2007,12(6):795-803.
[7]汪学军.安徽九华山山区雾的气候特征与形成机理[J].干旱气象,2012,30(3):374-379.
[8]赵丽娟,牛生杰.近地层辐射过程与雾微结构的相互作用特征[J].大气科学学报,2012,35(6):673-679.
[9]马晓刚,罗思维,舒海燕,等.中国典型大雾落区基本概念模型的研究与建立[J].气象与环境学报,2013,29(1):62-67.
[10]夏凡,李昌义.基于3种能见度方案山东地区雾天气预报试验研究[J].气象与环境学报,2018,34(3):48-57.
[11]郭渠,李瑞,阮定伦,等.重庆地区冬季雾气候变化特征及其成因分析[J].气象与环境学报,2018,34(5):47-56.
[12]朱佳蓉,漆梁波.上海地区辐射雾演变成因及模式预报[J].气象与环境学报,2017,33(1):1-11.
[13]王向阳,杨彬,章敬国,等.黄山近50年的气候变化研究[J].资源开发与市场,2008,24(9):834-836.