XIA Xin,WANG Dongmeng,HE Nan.Design and Application of Automatic Classification Method for Chengdu Regional Weather Station[J].Journal of Chengdu University of Information Technology,2023,38(03):324-329.[doi:10.16836/j.cnki.jcuit.2023.03.012]
成都区域气象站自动分型方法设计及应用
- Title:
- Design and Application of Automatic Classification Method for Chengdu Regional Weather Station
- 文章编号:
- 2096-1618(2023)03-0324-06
- 分类号:
- TP311.11
- 文献标志码:
- A
- 摘要:
- 为快速区分下垫面不同气象站类型,采用一种区域气象站自动分型方法。选取两个日气温极小值之差在-5 ℃～5 ℃站点,以0.1 ℃为步长划分档位,按照温差值的高低顺序排列温差; 统计90天内,各档温差值出现的频数,得到反映某个温差位拥有多少样本量的频次分析序列,取样本量的中位数频次进行归一化处理,即为特征频率,记为F; 设置量化因子Kf量化距离影响因素,下垫面一致性的影响可用Kf体现。最终结果表明,经运算分型站点被自动分为A、B、C 3个大群,通过观察A群和C群可知,乡村型站点与城镇型站点在地理分布上被清晰地自动区分,通过对A群C群进行抽样检查,及对城市站点与乡村站点的数据比对,验证了运算分型的有效性。
- Abstract:
- In order to quickly classify different types of weather stations on the underlying surface, an automatic classification method of regional weather stations is proposed. First,the temperature data selected from the stations where the difference between the two minimum daily temperature values is between -5 ℃ and +5 ℃, is divided with a step of 0.1 ℃ as, and sorted according to the temperature difference value; then the frequency analysis sequence reflecting the sample size of a certain temperature difference position can be obtained from the temperature difference values of each interval within 90 days. And the median frequency of the normalized sample size is taken as the characteristic frequency, marked as F; finally, the quantization factor is set Kf quantifies the factors affecting distance, and the influence of the underlying surface consistency can be reflected in Kf.The final results show that, the stations are automatically divided into 3 groups: A group, B group, and C group. By analyzing groups A and C, it can be concluded that the geographical distribution of rural and urban stations is clearly defined. The validity of the operational classification was verified by sampling inspection of group A and group C and comparing the data of urban and rural sites.
参考文献/References:
[1] 李林,孙赫敏,乔晓燕,等.探测环境对北京气象站气温观测值的影响分析[J].气象与环境学报,2019(4):120-126.
[2] 徐新良.基于气象站观测数据的京津冀城市扩展对气温变化的影响研究[J].地球信息科学学报,2019(7):1086-1096.
[3] 罗丽,郭建侠,陈一枝,等.公路路面热效应对环境气温的影响分析[J].气象科技,2018(2):403-411.
[4] 徐园园,任永建,许存华,等.城市化对咸宁地区气温变化的贡献分析[J].江西农业学报.2017(5):101-106.
[5] 岳艳霞,智利辉,王磊,等.石家庄市区域自动气象站气温数据适用性分析[J].气象与环境学报,2017(2):53-62.
[6] 张媛,任国玉.无参考序列条件下地面气温观测资料城市化偏差订正方法:以北京站为[J].地球物理学报,2014(7):2197-2207.
[7] 李娇,任国玉,任玉玉,等.资料均一化对沈阳站气温趋势和城市化偏差分析的影响[J].大气科学学报,2014(3):297-303.
[8] 茆佳佳,沈雪峰,郭建侠,等.水体对气温观测影响的试验分析[J].气象科技, 2014(3):496-502.
[9] 沈滢洁,王成刚,曹乐,等.屋顶绿化对城市降温效应的模拟分析——以南京市为例[J].气象,2017(5):610-619.
[10] 贾红莉.不同下垫面对气温观测的影响分析[J].南方农业,2016,10(9):220-221.
[11] 玉坤.空间数据聚类在气象灾害预警中的应用[J].电子技术与软件工程,2018(9):164-165.
[12] 张瑶,历超,王子洋.聚类分析在气象数据处理中的应用[J].林业勘查设计,2010(3):37-39.
[13] 严中伟,李珍,夏江江.气候序列的均一化——定量评估气候变化的基础[J].中国科学:地球科学,2014,44(10):2101-2111.
[14] 贺南,文斌,陈乐,等.一种新的气象数据分析和误差诊断方法[J].成都信息工程大学学报,2019,34(4):342-345.
[15] 张学文.熵气象学[M].北京:气象出版社,1992:153.
[16] 屠其璞,翁笃鸣,武全,等.气候资料超短序列订正方法讨论(上)[J].南京气象学院学报,1978(1):59-67.
[17] 李庆祥,刘小宁,张洪政,等.定点观测气候序列的均一性研究[J].气象科技,2003(1):3-10.
[18] 周子康.山区气温超短序列延长方法的讨论[J].农业气象,1983(2):53-61.
[19] 吴雪.城市化进程对气温变化的分析——以北京、郑州、南京、杭州城市为例[J].内蒙古气象,2019(3):42-45.
[20] 汤绪.超大城市气象观测网的设计与应用,[J].气象科技进展,2016(4):8-17.
[21] 贺南,夏昕,杨乐,等.一种区域自动气象站客观分群方法[P].发明专利,ZL2020.1.1253654.7.
备注/Memo
收稿日期:2022-07-18