| AI方法在地面气温观测资料质量控制中的应用 |
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| 引用本文： | 叶小岭,施林红,熊雄,王璐.AI方法在地面气温观测资料质量控制中的应用[J].气候与环境研究,2016,21(1):1-7. |
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| 作者姓名： | 叶小岭 施林红 熊雄 王璐 |
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| 作者单位： | 南京信息工程大学信息与控制学院, 南京 210044;南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044;江苏省大数据分析技术重点实验室, 南京 210044,南京信息工程大学信息与控制学院, 南京 210044,南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044,南京信息工程大学信息与控制学院, 南京 210044 |
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| 基金项目： | 江苏省六大人才高峰项目WLW-021,江苏省研究生创新工程省立项目SJLX_0386 |
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| 摘 要： | 提出一种基于自回归与反距离加权的空间质量控制方法——AI方法,该方法能够在时间维度和空间维度对气象资料进行质量控制。选择不同地区4个地面气象观测站(南京58238站,连云港58044站,无锡58353站,徐州58027站)2007年逐时气温观测数据作为被控对象,检验该方法在气温资料质量控制中的适用性。通过对添加的随机人为误差的检验发现,该方法能够有效地标记出存疑数据,相对于反距离加权和空间回归,该方法具有更好的检验效果,稳定性高、适应性强,适用于平原或丘陵地带。
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| 关 键 词： | 大气探测 地面气温 质量控制 自回归 反距离加权 空间回归 |
| 收稿时间： | 2015/3/30 0:00:00 |
Application of AI Method to Quality Control in Surface Temperature Observation Data |
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| YE Xiaoling,SHI Linhong,XIONG Xiong and WANG Lu.Application of AI Method to Quality Control in Surface Temperature Observation Data[J].Climatic and Environmental Research,2016,21(1):1-7. |
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| Authors: | YE Xiaoling SHI Linhong XIONG Xiong and WANG Lu |
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| Institution: | Institute of Information and Control, NanjingUniversity of Information Science and Technology, Nanjing 210044;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044;Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing 210044,Institute of Information and Control, NanjingUniversity of Information Science and Technology, Nanjing 210044,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 and Institute of Information and Control, NanjingUniversity of Information Science and Technology, Nanjing 210044 |
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| Abstract: | A method of spatial quality control (called AI for short), based on auto-regression and inverse distance weighting (IDW), is proposed. The method enables quality control of meteorological data in both the temporal and spatial dimension. Aimed at assessing the applicability of the method, in this study, hourly temperature observational data for the year 2007 from four surface meteorological stations in different regions (Nanjing station 58238, Lianyungang station 58044, Wuxi station 58353, and Xuzhou station 58027) were selected as controlled objects to carry out quality control using the AI method. Compared with IDW and the spatial regression test (SRT) in discriminating artificial errors, it is shown that the proposed method can mark suspicious data effectively. Furthermore, it is highly effective, stable, adaptable, and applicable in both plain and hilly areas. |
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| Keywords: | Atmospheric detection Surface temperature Quality control Auto-regression Inverse distance weighting Spatial regression test |
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