基于EEMD-CES的地面气温观测资料质量控制方法研究
投稿时间: 2017-12-05  最后修改时间: 2018-03-14  点此下载全文
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作者单位E-mail
叶小岭 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 xyz.nim@163.com 
陈洋 南京信息工程大学 ch_ya_0686@163.com 
基金项目:国家自然科学基金项目(41675156),江苏省高校优势学科建设工程资助项目(PAPD),江苏省六大人才高峰项目(WLW-021)与南京信息工程大学人才启动项目(2243141701053)共同资助
中文摘要:为了尽可能减少或去除我国地面气温观测资料中含有的噪声成分,提高观测资料质量,本文提出一种新的单站质量控制算法。该算法融合了集合经验模态分解法(Ensemble Empirical Mode Decomposition,EEMD)和三次指数平滑法(Cubic Exponential Smooth,CES);首先利用EEMD方法将气温观测资料分解为一系列相对平稳的本征模分量,并基于能量密度和相关性准则从中分析筛选出目标分量,以完成资料重构;最后利用CES方法对重构资料建立单站质量控制模型,形成了一种基于EEMD-CES的地面观测资料质量控制算法。为检验该方法的可行性与适用性,本文选取全国九个观测站2008年地面逐时气温观测资料进行质量控制,并对比传统的单站质量控制法、经验模态分解法和三次指数平滑法的质量控制效果。试验结果表明,基于EEMD-CES的质量控制方法能有效的标记出数据的可疑值,相比传统方法,具有更高的检错率和更强的适应性。
中文关键词:质量控制,气温,时间序列,集合经验模态分解,三次指数平滑
 
A Quality Control Method of Surface Temperature Observations Based on EEMD-CES Algorithm
Abstract:In order to reduce or remove the noise components contained in observation data of surface temperature in our country as much as possible and improve the quality of observation data, a new single station quality control algorithm is proposed in this paper. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) and Cubic Exponential Smooth (CES). First, the EEMD method is used to decompose the temperature observation data into a series of relatively stable intrinsic mode function. And then based on the energy density and the correlation criterion, the target component is analyzed and screened out to complete the data reconstruction. Finally, the CES method is used to establish a single station quality control model with the reconstructed data and forms a surface observations quality control method based on EEMD-CES algorithm. In order to test the feasibility and applicability of this method, this paper selects the surface hourly temperature observations from nine stations throughout the country for quality control in 2008. The results are compared with the quality control effects of the traditional single station quality control method, empirical mode decomposition method and cubic exponential smoothing method. The experimental results show that the quality control method based on EEMD-CES can mark the suspicious value of data effectively, and has a higher error detection rate and stronger adaptability than the traditional method.
keywords:Quality control, Temperature, Time series, Ensemble empirical mode decomposition, Cubic exponential smoothing
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