A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations

An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of...

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Main Authors: Xiaoling Ye, Yajin Kan, Xiong Xiong, Yingchao Zhang, Xin Chen
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2020/6045492
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author Xiaoling Ye
Yajin Kan
Xiong Xiong
Yingchao Zhang
Xin Chen
author_facet Xiaoling Ye
Yajin Kan
Xiong Xiong
Yingchao Zhang
Xin Chen
author_sort Xiaoling Ye
collection DOAJ
description An improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. Observations of 12 reference stations in Jiangsu from 1961 to 2008 and of 14 regions in China from 2010 to 2014 were selected. The analysis of surface air temperature observations was performed in terms of the mean absolute error (MAE), root mean square error (RMSE), consistency indicator (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC). The results indicate that compared with the traditional IDW and SRT methods, the IKR method has a high error detection rate. Furthermore, the IKR method achieves better predictions and fitting in the single-station and multistation regression experiments in Jiangsu and in the national multistation regression prediction experiment.
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institution Kabale University
issn 1687-9309
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publishDate 2020-01-01
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series Advances in Meteorology
spelling doaj-art-764763074103416e9ceee5d1006fcc992025-02-03T05:49:30ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/60454926045492A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature ObservationsXiaoling Ye0Yajin Kan1Xiong Xiong2Yingchao Zhang3Xin Chen4School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaAn improved kernel regression (IKR) method based on an adaptive algorithm and particle swarm optimization is proposed. Considering the limitations of current quality control methods in different regions and on multiple time scales, the kernel regression algorithm is applied to the quality control of surface air temperature observations. Observations of 12 reference stations in Jiangsu from 1961 to 2008 and of 14 regions in China from 2010 to 2014 were selected. The analysis of surface air temperature observations was performed in terms of the mean absolute error (MAE), root mean square error (RMSE), consistency indicator (IOA), and Nash–Sutcliffe model efficiency coefficient (NSC). The results indicate that compared with the traditional IDW and SRT methods, the IKR method has a high error detection rate. Furthermore, the IKR method achieves better predictions and fitting in the single-station and multistation regression experiments in Jiangsu and in the national multistation regression prediction experiment.http://dx.doi.org/10.1155/2020/6045492
spellingShingle Xiaoling Ye
Yajin Kan
Xiong Xiong
Yingchao Zhang
Xin Chen
A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
Advances in Meteorology
title A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
title_full A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
title_fullStr A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
title_full_unstemmed A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
title_short A Quality Control Method Based on an Improved Kernel Regression Algorithm for Surface Air Temperature Observations
title_sort quality control method based on an improved kernel regression algorithm for surface air temperature observations
url http://dx.doi.org/10.1155/2020/6045492
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