设为首页
加入收藏
Quality Detection of Surface Historical Basic Meteorological Data
-
摘要: 为深入了解地面基础气象资料中存在的问题，进一步提高资料质量，综合利用国家级和省级气象资料部门存储的1951—2009年2474个国家级地面气象站观测的气温、气压、水汽压、相对湿度、风向、风速、降水量7种要素信息化基础数据，检测并分析了数据中存在的问题。结果显示：国家级和省级气象部门存储的资料中均存在大量与实际观测数据不符的信息化问题，包括资料的替代问题、要素数据类似缺测问题以及数据录入错误等；还存在国家级和省级气象部门保存的基准基本站资料不一致现象，包括资料序列长短不同、对外服务时提供自动还是人工观测数据不一致、更正不同步造成的数据不同等。该文针对上述资料问题给出了详细的检测方法及检测结果。为了确保数据的正确性，有必要在此次数据质量检测经验的基础上，对所有历史月报数据文件中的所有要素观测值进行彻底检测与更正。Abstract: Surface basic meteorological data are observational values from surface stations, including hourly values, daily extreme and cumulative values. Chinese surface historical basic meteorological data consists of observations from more than 2400 national stations since as early as 1951, including 20 kinds of elements, such as air temperature, air pressure, humidity, wind, precipitation, evaporation and so on. These data are the basis of regional and global climate change researches and predictions, synoptic dynamic analysis and public meteorological services. The digitization of these data is started by China Meteorological Adminiatration in 1979. High quality digital data should be faithfulness to the paper reports. But incorrect data and data missing problems caused by digitization and restoring have been found, besides those caused by error observations. Resolving these problems will contribute to the improvement of operational application accuracy, scientific researches and data processing.In order to identify quality problems of Chinese surface basic meteorological data and improve the data quality, several methods are applied to detect the data quality of air temperature, pressure, humility, wind and precipitation observed by 2474 national surface stations in China from 1951 to 2009. The above data are collected based on National Meteorological Information Center (NMIC) and provincial archived electronic data files. Results show that a large number of error data different from actual measurements are stored in both NMIC and provincial archived data files, which are caused by incorrect digitization. For example, some electronic data files are replaced by other station measurements, some meteorological elements aren't observed for a period, and some data are miss-typed. Some flaws between NMIC and provincial archived data files also shows, including the data file differences in time span and various data source (automatic or manual observation), and data difference induced by asynchronous correction of wrong data. It is impossible to detect all the above data problems using conventional data quality control methods, so some special methods are proposed for each kind of problem. The proportion of incorrect measurements of the whole data is low, though its number is huge. For example, for more than 2400 national stations, only 0.06 percent of data are wrong copied, and for more than 700 baseline stations, only 0.34 percent of precipitation data are missing. The data quality detecting method mainly focuses on temperature, pressure, humidity, wind and precipitation data from about 700 baseline stations, and data problems are verified by checking the monthly paper reports but not corrected. With this work experience, thorough data problem detections and corrections on the whole elements from historical electronic data files are carried out by China Meteorological Administration. A review of this program and the results will be published soon.
-
Key words:
- surface;
- basic meteorological data;
- data quality;
- quality detection
-
图 1 1951—2009年被替代的信息化数据文件时间 (a) 和空间 (b) 分布
(图b中圆点表示台站位置，数字表示所属省份的站月数)
Fig. 1 Temporal distribution (a) and spatial distribution (b) of the number of replaced electronic monthly data files from 1951 to 2009
(dots denote station location and figures denote the total number of replaced electronic monthly data files in a province in Fig. 1b)
图 2 1951—2009年各要素整月无数据站月数历年变化情况
Fig. 2 Annual number of whole-month data missing of air temperature, air pressure, vapor pressure, relative humility, wind and precipitation amount from 1951 to 2009
图 3 国家级与省级存储基准基本站资料时间跨度差异分布
Fig. 3 Distribution of the time span difference between NMIC and provincial data observed by national base stations
表 1 1971—2000年中国基准基本站地面信息化资料错情[6]
Table 1 Information about surface meteorological data in error of base stations in China from 1971 to 2000[6]
要素 错误资料站数及月数 资料年份 错误原因 地面所有要素 4站共4个月全月地面所有要素资料均错 1981，1984，1992 用其他站或其他月资料替代了本站本月的资料 1站1个月从云量开始所有要素资料均错 1994 用其他站或其他月资料替代了本站本月的资料 风速 10站共85个月全月风速资料错 1980—1994 资料均缩小10倍录入 小型蒸发 1站1个月全月蒸发资料错 1990 资料均扩大10倍录入 地温 3站10个月0cm地温全月错 1989, 1994 已取消地温观测，但全月该资料却错用0表示 3站14个月极端最高地面温度全月错 1989, 1994 已取消地温观测，但全月该资料却错用0表示 1站1个月5~15 cm地温全月错 1984 地温超刻度, 将符号位穿错 1站2个月20 cm地温全月错 1994 地温表坏，资料应缺测处理, 而信息化资料却全为0 1站8个月40 cm地温比气候平均值偏高7.5℃ 1971 地温表瓷板断裂，资料仅供参考 日照时数 1站1月1 d资料错 1976 录入错误 日照百分率 1站1月27 d资料错 1993 录入错误 极端最高气温 1站1月1 d资料错 1995 资料缩小10倍录入 相对湿度 1站1个月全月相对湿度方式位错 1995 录入错误 1站1个月11 d相对湿度错 1992 录入错误 最大冻土深度 3站3个月共15 d资料错 1980, 1987, 1992 录入错或缩小10倍录入 最大积雪深度 2站2个月共2 d资料错 1977, 1985 资料扩大10倍录入 
下载: 导出CSV
表 2 国家级存储的1951—2009年756个基准基本站资料各要素整月无数据核查结果 (单位：站月)
Table 2 Verification results of whole month data missing of 6 meteorological elements observed by 756 national surface base stations from NMIC during 1951—2009(unit: month)
要素 整月无数据
现象发生的
时间整月无数
据量已核查
报表量原因及相应站月数 信息化遗
漏或错误有观测数据，按
规定可不信息化无观测任务
或缺测气温 1951—1965年 20 20 8 0 12 气压 1951—1984年 7525 3613 22 1276 2315 水汽压 1951—1969年 465 160 142 0 18 相对湿度 1951—1969年 314 154 139 0 15 风向风速 1951—1997年 3062 881 6 859 16 降水 1951—2006年 1473 1451 1433 0 18 合计 12859 6279 1749 2135 2395
下载: 导出CSV
表 3 1951—2006年731个基准基本站不一致数据核查结果
Table 3 Verification results of the difference between NMIC and provincial data observed by 731 national base stations from 1951 to 2006
要素 不一致总数 核查数 省级正确率/% 国家级正确率/% 均不正确率/% 气温 15672 12633 37.6 62.3 0.1 气压 22053 16387 44.9 55.0 0.1 水汽压 109963 14082 36.1 63.6 0.3 相对湿度 34838 16835 43.6 56.2 0.2 风向 53762 47869 64.8 35.1 0.1 风速 35809 26894 21.3 78.1 0.6 降水量 20166 18299 27.3 72.5 0.2 合计 292263 152999 43.33 56.45 0.22
下载: 导出CSV
-
[1] 中国气象局.地面气象观测规范.北京:气象出版社, 2003. [2] 中国气象局.常规气象资料信息化模式文本汇编.北京:气象出版社, 2001. [3] 刘小宁, 任芝花.地面气象资料质量控制方法研究概述.气象科技, 2005, 33(3):199-203. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200503001.htm [4] 刘小宁, 鞠晓慧, 范邵华.空间回归检验方法在气象资料质量检验中的应用.应用气象学报, 2006, 17(1):37-43. doi: 10.11898/1001-7313.20060106 [5] 任芝花, 熊安元.地面自动站观测资料三级质量控制业务系统的研制.气象, 2007, 33(1):19-24. doi: 10.7519/j.issn.1000-0526.2007.01.003 [6] 任芝花, 刘小宁, 杨文霞.极端异常气象资料的综合性质量控制与分析.气象学报, 2005, 63(4):526-533. doi: 10.11676/qxxb2005.052 [7] 任芝花, 熊安元, 邹风玲.中国地面月气候资料质量控制方法的研究.应用气象学报, 2007, 18(4):516-523. doi: 10.11898/1001-7313.20070412 [8] 任芝花, 许松, 孙化南, 等.全球地面天气报历史资料质量检查与分析.应用气象学报, 2006, 17(4):412-420. doi: 10.11898/1001-7313.20060404 [9] 任芝花, 赵平, 张强, 等.适用于全国自动站小时降水资料的质量控制方法.气象, 2010, 36(7):123-132. doi: 10.7519/j.issn.1000-0526.2010.07.019 [10] 陶士伟, 张跃堂, 陈卫红, 等.全球观测资料质量监视评估.气象, 2006, 32(6):53-58. doi: 10.7519/j.issn.1000-0526.2006.06.009 [11] 陶士伟, 仲跻芹, 徐枝芳, 等.地面自动站资料质量控制方案及应用.高原气象, 2009, 28(5):1202-1209. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200905029.htm [12] 王海军, 杨志彪, 杨代才, 等.自动气象站实时资料自动质量控制方法及其应用.气象, 2007, 33(10):102-106. doi: 10.7519/j.issn.1000-0526.2007.10.015 [13] 王海军, 刘莹.综合一致性质量控制方法及其在气温中的应用.应用气象学报, 2012, 23(1):69-76. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120108&flag=1 [14] 杨萍, 刘伟东, 仲跻芹, 等.北京地区自动气象站气温观测资料的质量评估.应用气象学报, 2011, 22(6):706-715. doi: 10.11898/1001-7313.20110608 [15] 中国气象局. 中华人民共和国气象行业标准: 地面气象观测资料质量控制. 2010. [16] Eischeid J K, Baker C B, Karl T R. The quality control of long-term climatological data using objective data analysis.J Appl Meteor, 1995, 34:2787-2795. doi: 10.1175/1520-0450(1995)034<2787:TQCOLT>2.0.CO;2 [17] Fillipov V V. Quality Control of Meteorological Data.World Weather Watch Planning Report, WMO-No.26, 1968. [18] Igor Zahumensk. Guidelines on Quality Control Procedures for Data from Automatic Weather Stations.Expert Team on Requirements for Data from Automatic Weather Stations, Third Session, WMO, 2004. [19] Peterson T C, Vose R S, Schmoyer R, et al. Global historical climatology network (GHCN) quality control of monthly temperature data. Int J Climatol, 1998, 18:1169-1179. doi: 10.1002/(ISSN)1097-0088 [20] Sciuto G, Bonaccorso B, Cancelliere A, et al. Quality control of daily rainfall data with neural networks.J Hydro, 2009, 364:13-22. doi: 10.1016/j.jhydrol.2008.10.008 [21] Song Feng, Qi Hu, Qian Weihong. Quality control of daily meteorological data in China, 1951—2000: A new dataset.Int J Climatol, 2004, 24:853-870. doi: 10.1002/(ISSN)1097-0088 [22] Vejen F, Jacobsson C, Fredriksson U, et al. Quality Control of Meteorological Observations Automatic Methods Used in the Nordic Countries.Climate Report, No.8, 2002. [23] WMO. Guide on the GOS. WMO-No.488, PART Ⅵ, 1989. [24] WMO. Manual on GDPS. WMO-No.485, Volume 1:Ⅱ.1-4, 1992. [25] WMO. Guide on the GDPS. WMO-No.305, Chapter 6, 1993. -
点击查看大图
计量
- 摘要浏览量: 3153
- HTML全文浏览量: 1128
- PDF下载量: 1753
- 被引次数: 0
