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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/6045492 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555114172252160 |
|---|---|
| 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. |
| format | Article |
| id | doaj-art-764763074103416e9ceee5d1006fcc99 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| 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 |
| work_keys_str_mv | AT xiaolingye aqualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT yajinkan aqualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT xiongxiong aqualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT yingchaozhang aqualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT xinchen aqualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT xiaolingye qualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT yajinkan qualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT xiongxiong qualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT yingchaozhang qualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations AT xinchen qualitycontrolmethodbasedonanimprovedkernelregressionalgorithmforsurfaceairtemperatureobservations |