论文

基于不同气象站资料的甘肃省汛期降水及极端降水分析

  • 方锋 ,
  • 王静 ,
  • 刘卫平 ,
  • 王有恒 ,
  • 王鑫 ,
  • 林婧婧 ,
  • 王兴
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  • 1. 兰州区域气候中心,甘肃 兰州 730020
    2. 兰州干旱气象研究所,甘肃 兰州 730020

方锋(1977 -), 男, 陕西咸阳人, 正高级工程师, 主要从事气候变化与趋势检测研究. E-mail:

收稿日期: 2022-07-27

  修回日期: 2023-02-08

  网络出版日期: 2023-11-14

基金资助

甘肃省自然科学基金项目(21JR7RA709); 甘肃省气象局项目(GHSCXTD-2020-3)

Analysis of Flood Season Precipitation and Extreme Precipitation in Gansu Province Based on Data from Different Types of Meteorological Stations

  • Feng FANG ,
  • Jing WANG ,
  • Weiping LIU ,
  • Youheng WANG ,
  • Xin WANG ,
  • Jingjing LIN ,
  • Xing WANG
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  • 1. Lanzhou Regional Climate Center,Lanzhou 730020,Gansu,China
    2. Lanzhou Institute of Arid Meteorology,Lanzhou 730020,Gansu,China

Received date: 2022-07-27

  Revised date: 2023-02-08

  Online published: 2023-11-14

摘要

对甘肃省范围内的区域气象站和国家级气象站2011 -2020年的汛期降水和极端降水等资料进行了质量控制, 筛选出1253个区域气象站和75个国家气象站开展了降水特征分析。结果表明, 甘肃省区域站和国家站降水的平均数量非常接近, 无显著性差异, 全省区域站汛期平均降水为382.9 mm, 国家级站为379.7 mm; 区域站各月降水平均在31.7~87.1 mm; 国家站各月为30.4~86.4 mm。但两类站点降水的极端值存在显著差异, 汛期总降水量区域站最大可达917.5 mm, 本站最大为710.7 mm; 单月降水量区域站最大为776 mm, 本站为541 mm; 暴雨日数区域站最大为46 d·(10a)-1, 本站为17 d·(10a)-1; 两类站点降水极大值发生的空间位置比较接近, 但区域站的数值显著高于国家站。两种站点降水的空间趋势非常一致, 其汛期降水相关系数达0.95, 月极端降水相关系数为0.92; 而区域气象站比国家站有更多小斑块的高值和低值中心; 两类降水空间差异在甘肃省的东部和南部比较明显。总体上, 区域气象站更精细地描绘了甘肃省的降水特征, 在地形复杂区域监测到更多的极端降水事件, 降水极值相比国家站明显偏大, 这些地方的暴雨洪涝灾害防御应以区域气象站数据为依据, 增强灾害风险的防范能力。

本文引用格式

方锋 , 王静 , 刘卫平 , 王有恒 , 王鑫 , 林婧婧 , 王兴 . 基于不同气象站资料的甘肃省汛期降水及极端降水分析[J]. 高原气象, 2023 , 42(6) : 1468 -1477 . DOI: 10.7522/j.issn.1000-0534.2023.00008

Abstract

In this paper, the data quality control of the flood season precipitation and extreme precipitation of the regional meteorological stations and national meteorological stations in Gansu Province from 2011 to 2020 has been carried out, and 1253 regional meteorological stations and 75 national meteorological stations have been selected to analysis the precipitation characteristics.The results show that the average precipitation amount of the regional meteorological stations in Gansu Province is very close to that of the national meteorological stations, and there is no significant difference.The average precipitation of the regional meteorological stations in flood season in Gansu province is 382.9 mm, and that of the national meteorological stations is 379.7 mm.The average monthly precipitation of regional meteorological stations distributes from 31.7 mm to 87.1 mm, and that of national meteorological stations distributes from 30.4 mm to 86.4 mm.However, there are significant differences in the extremes of precipitation between the two types of meteorological stations.The maximum of total precipitation at regional meteorological stations in flood season even reaches 917.5 mm, the maximum monthly precipitation reaches 776 mm, and the maximum number of rainstorm days reaches 46 d·(10a)-1.However, those of national meteorological stations is 710.7 mm, 541 mm and 17 d·(10a)-1, respectively.The spatial locations of precipitation maxima of regional meteorological stations and national meteorological stations are close to each other, but the numerical values of regional stations are significantly higher than those of national stations.The spatial trends of precipitation at the two stations are very consistent, and the correlation coefficient of flood season precipitation of two types stations is 0.95, the correlation coefficient of monthly extreme precipitation is 0.92.Moreover, regional meteorological stations show many higher and less value centers of small patches than national stations; the spatial precipitation difference between the two types of meteorological stations are clearly in the east and south of Gansu Province.Overall, the regional meteorological stations describe the precipitation characteristics of Gansu Province in a more detailed way.More extreme precipitation events are monitored in those regions with complex terrain, and the extreme value of precipitation is larger than that of national meteorological stations.The rainstorm and flood disaster prevention in these regions should be based on the data of regional meteorological stations to enhance the disaster risk prevention ability.

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