| 本文已被：浏览 1305次 下载 321次 |
码上扫一扫！ |
| 基于空间回归的B样条拟合在地面气温资料质量控制中的应用 |
|
熊雄1,2, 姚薇3, 叶小岭1,2, 张颖超1,2, 杨帅4
|
|
1. 南京信息工程大学江苏省大气环境与装备技术协同创新中心，江苏 南京 210044;2. 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，江苏 南京 210044;3.江苏省突发事件预警信息发布中心，江苏 南京 210008;4.南京信息工程大学江苏省大气环境与装备技术协同创新中心，江苏 南京 210044
|
|
| 摘要: |
| 将B样条拟合算法引入到地面气温资料的质量控制当中，在分析地面气温资料空间相关性的基础上，提出一种基于空间回归的B样条拟合地面气温资料质量控制方法(SRT_BSF方法)。为了检验SRT_BSF方法的有效性及适应性，利用SRT_BSF方法对多个场景地面气温资料进行质量控制，并与反距离加权方法(IDW方法)和空间回归方法(SRT方法)进行比较分析。试验结果表明，SRT_BSF方法相对于IDW方法和SRT方法更能有效地标记出地面气温资料中的存疑数据，同时多组独立案例的分析结果说明SRT_BSF方法具有更好的稳定性和适用性。 |
| 关键词: B样条拟合 空间回归检验 质量控制 空间相关性 |
| DOI：10.16032/j.issn.1004-4965.2020.045 |
| 分类号: |
| 基金项目: |
|
| APPLICATION OF B-SPLINE FITTING BASED ON SPATIAL REGRESSION IN QUALITY CONTROL FOR SURFACE TEMPERATURE OBSERVATIONS |
|
XIONG Xiong1,2, YAO Wei3, YE Xiao-ling1,2, ZHANG Ying-chao1,2, YANG Shuai4
|
|
1. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing
University of Information Science and Technology, Nanjing 210044, China;3.Jiangsu Provincial Emergency Early Warning Release Center, Nanjing 210008, China;4.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
|
| Abstract: |
| This article applies the B-spline fitting algorithm to the quality control for surface temperature observations. After an analysis of the spatial correlation of surface air temperature data, a quality control method based on spatial regression of B-spline fitting for surface air temperature data (SRT_BSF method) is proposed. In order to test the effectiveness and adaptability of the algorithm, the quality control of surface temperature data in multiple scenes is carried out by using SRT_BSF method, and the method is compared with the inverse distance weighting method (IDW method) and spatial regression method (SRT method). The test results show that the SRT_BSF method is more effective than the IDW method and the SRT method in marking questionable data in surface temperature observation data. At the same time, the analysis results of multiple independent cases show that the SRT_BSF method is more stable and widely applicable than the other methods. |
| Key words: B-spline fitting spatial regression test quality control spatial correlation |