| 定量研究雅安地形坡向坡度对降水分布的影响 |
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| 引用本文： | 周学云,高文良,吴亚平,钱正迪,邱双.定量研究雅安地形坡向坡度对降水分布的影响[J].气象科学,2019,39(3):322-335. |
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| 作者姓名： | 周学云 高文良 吴亚平 钱正迪 邱双 |
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| 作者单位： | 南京信息工程大学, 南京 210044;雅安市气象局, 四川 雅安 625000;高原与盆地暴雨旱涝灾害四川省重点实验室, 成都 610072,雅安市气象局, 四川 雅安 625000;高原与盆地暴雨旱涝灾害四川省重点实验室, 成都 610072;中国气象局 成都高原气象研究所, 成都 610072,雅安市气象局, 四川 雅安 625000,雅安市气象局, 四川 雅安 625000,雅安市气象局, 四川 雅安 625000 |
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| 基金项目： | 国家自然科学基金资助项目（91337215；41275052） |
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| 摘 要： | 利用四川省雅安市30 m分辨率基础高程数据,提取栅格的坡向和坡度参数,将雅安307个区域自动站在2017年汛期(6—9月)共50次的降水天气个例,分为16次大尺度降水和34次中小尺度降水,使用对应时次的欧洲中心细网格0.25°×0.25°再分析风场资料,根据不同的站点地形高度将风场合成平均风场,和各站点地形的坡向和坡度计算出其动力抬升作用,同时使用当天日照和天文太阳辐射值来计算地形的热力抬升作用,与对应降水过程的降水分布进行多元线性回归,根据回归的标准系数的大小确定各自变量对降水分布的影响,得出以下结论:(1)中小尺度降水中,地形的热力抬升作用对降水分布的影响作用最大,其次是海拔高度,地形的动力抬升作用在三者中对降水分布的影响最小;(2)在大尺度降水中刚好相反,地形的动力抬升对降水的分布影响作用最大,海拔次之,热力抬升作用在三者中影响作用最小;(3)日降水量最大值的站点海拔高度基本位于1 000 m左右,与抬升凝结高度对应较好;(4)从长期的统计来看,地形的动力作用和地表的植被情况对降水分布的影响最大。在实际预报工作用,根据不同的降水类型,关注不同的动力和热力作用对于判断降水分布大值区的位置有较好的参考作用。
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| 关 键 词： | 坡向坡度 地形动力抬升 地形热力抬升 降水分布 |
| 收稿时间： | 2017/12/13 0:00:00 |
| 修稿时间： | 2018/3/20 0:00:00 |
Quantitative study on the influence of terrain aspect and gradient on the precipitation distribution in Ya'an |
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| ZHOU Xueyun,GAO Wenliang,WU Yaping,QIAN Zhengdi and QIU Shuang.Quantitative study on the influence of terrain aspect and gradient on the precipitation distribution in Ya'an[J].Scientia Meteorologica Sinica,2019,39(3):322-335. |
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| Authors: | ZHOU Xueyun GAO Wenliang WU Yaping QIAN Zhengdi and QIU Shuang |
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| Institution: | Nanjing University of Information Science & Technology, Nanjing 210044, China;Ya''an Meteorological Bureau, Sichuan Ya''an 625000, China;The Plateau and Sichuan Basin Storm Rain and Dry Damege Key Laboratory of Sichuan Province, Chengdu 610072, China,Ya''an Meteorological Bureau, Sichuan Ya''an 625000, China;The Plateau and Sichuan Basin Storm Rain and Dry Damege Key Laboratory of Sichuan Province, Chengdu 610072, China;Institute of Plateau Meteorology, China Meteorologica Administration, Chengdu 610072, China,Ya''an Meteorological Bureau, Sichuan Ya''an 625000, China,Ya''an Meteorological Bureau, Sichuan Ya''an 625000, China and Ya''an Meteorological Bureau, Sichuan Ya''an 625000, China |
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| Abstract: | By using the basic 30 m resolution elevation data in Ya''an, Sichuan Province, the aspect and gradient parameters of the grids were extracted. The main 50 times of precipitation collected from the 307 automatic weather stations in Ya''an during the flood season of 2017 (June-September) were divided into 16 times of large-scale precipitation and 34 times of middle-and small-scale precipitation. At the same time, by using ECMWF 0.25°×0.25° resolution reanalysis wind field data, the composite average wind field is synthesized according to the height of different sites, and the dynamic uplifting is calculated by the aspect and gradient values of each site. The daily sunshine hours and astronomical solar radiation data were also used to calculate the thermal uplift function of the terrain. The multiple linear regression was applied to the precipitation distribution. According to the standard coefficient values of regression, the influence of each variable on the precipitation distribution was determined. The following conclusions are drawn. (1) In the middle-and small-scale precipitation, the thermal uplift has the most important effect on the precipitation distribution, followed by the altitude, and then by the dynamic uplift of the terrain. (2) On the contrary, in the large-scale precipitation, the dynamic uplift of the terrain has the greatest influence on the precipitation distribution, followed by the altitude, and then by the thermal uplift. (3) The maximum daily precipitation values mostly appeared at the stations with 1000 m high, which corresponds well to the lifting condensation level. (4) From the long-term statistics, the dynamic uplift of the terrain and the vegetation conditions on the surface have the most significant influence on the precipitation distribution. According to different precipitation scales, attention should be paid to different dynamic and thermodynamic functions to predict the location of areas with frequent precipitation. |
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| Keywords: | aspect and gradient terrain forced uplift terrain thermal uplift precipitation distribution |
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