| 苏皖地面自动站资料的质量控制及结果分析 |
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| 引用本文： | 闵锦忠,王晨珏,贾瑞怡.苏皖地面自动站资料的质量控制及结果分析[J].大气科学学报,2018,41(5):637-646. |
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| 作者姓名： | 闵锦忠 王晨珏 贾瑞怡 |
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| 作者单位： | 南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044;南京信息工程大学 气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心, 江苏 南京 210044 |
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| 基金项目： | 国家重点基础研究发展计划项目（2013CB430102）；国家自然科学基金重点项目（41430427）；江苏省高校自然科学重大基础研究项目（11KJA170001） |
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| 摘 要： | 利用江苏和安徽2012—2014年151个国家站及2 600个区域站资料,对各类自动站资料的质量及其控制方法进行初步探讨。采用缺测资料统计、气候界限值检查、气候极值检查、内部一致性检查、二次迭代的空间一致性检查、时间一致性检查、持续性检查及综合决策算法分别对国家站和区域站资料进行系统的质量控制,并根据质量控制结果进行可疑站点的标记。结果显示:国家站各要素资料缺测率远低于区域站,且资料质量总体上均明显优于区域站;自动站各要素中温、压要素的质量最好,其次是相对湿度;除了空间一致性检查中风场资料的检查结果差别不大外,其余检查中区域站资料的未通过率均远高于国家站资料;将错误资料及可疑站点信息进行及时反馈,能改善实时资料质量,并为相应测站的检修与维护提供依据。
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| 关 键 词： | 国家自动气象站 区域自动气象站 缺测资料统计 质量控制 可疑站点 |
| 收稿时间： | 2016/4/17 0:00:00 |
| 修稿时间： | 2016/5/23 0:00:00 |
Quality control and result analysis for surface AWS data in Jiangsu and Anhui Provinces |
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| MIN Jinzhong,WANG Chenjue and JIA Ruiyi.Quality control and result analysis for surface AWS data in Jiangsu and Anhui Provinces[J].大气科学学报,2018,41(5):637-646. |
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| Authors: | MIN Jinzhong WANG Chenjue and JIA Ruiyi |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | It has important significance if the surface automatic weather stations (AWS) data with high spatial and temporal resolution can be fully applied in the weather forecast, but it is hard to ensure the quality of data for various reasons. AWS data from 151 national AWS and 2 600 regional AWS in Jiangsu and Anhui Provinces are selected to discuss the quality of all kinds of AWS data and the quality control(QC) scheme. Based on the AWS data from 2012 to 2014, the missing rates are estimated respectively. A systematic and sophisticated QC scheme containing the missing data statistics, the climate limit check, the climate extreme value check, the internal consistency check, the second iterated space consistency check, the time consistency check, the continuous check and the comprehensive decision-making algorithm is designed for selecting out accurate information and rejecting abnormal information. What''s more, the suspicious stations are marked according to the results of QC scheme. It turns out that not only the missing rates of national AWS data of various meteorological elements are apparently lower than the regional ones according to the statistics, but also the quality of national AWS data is obviously better than the regional ones in general. Among various elements, the quality of temperature and pressure data is best, the next is the quality of relative humidity data, and the quality of wind data is worst. The fail rates of all elements of regional AWS data are much higher than the national ones in the QC scheme, except for the wind field data in the second iterated space consistency check, which has little difference between the regional and national AWS data. If the results of QC scheme, especially the information of error data and suspicious stations can be provided to the corresponding stations in time, it is beneficial to the improvement of the real-time data quality and the maintenance and correction of corresponding instruments. |
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| Keywords: | national automatic weather stations regional automatic weather stations missing data statistics quality control suspicious station |
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