Multivariate Interpolation of Wind Field Based on Gaussian Process Regression
The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the accuracy of spatial interpolation towards wind fie...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Atmosphere |
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| Online Access: | http://www.mdpi.com/2073-4433/9/5/194 |
| _version_ | 1818134343439089664 |
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| author | Miao Feng Weimin Zhang Xiangru Zhu Boheng Duan Mengbin Zhu De Xing |
| author_facet | Miao Feng Weimin Zhang Xiangru Zhu Boheng Duan Mengbin Zhu De Xing |
| author_sort | Miao Feng |
| collection | DOAJ |
| description | The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the accuracy of spatial interpolation towards wind fields. A new composited multi-scale anisotropic kernel function for weather processes using two-dimensional space information is proposed. To fix the underfitting in this kernel caused by unilateral space information, multiple variables (wind direction, air temperature, and atmospheric pressure) are introduced, which generates a multivariate correction model based on the novel kernel function and Gaussian process regression. Focusing on different weather processes, two multivariate correction models are designed. The new models pave a new way to employ multi-scale local information, and extract the anisotropy and structure information. The experiments on 10 m wind fields for the weather processes without cyclones and for the weather processes with cyclones validate the efficiency. The mean RMSE of the multivariate correction model for the weather processes without cyclones is reduced by around 15% for the u wind component compared with that of a simple composited kernel. For the weather processes with cyclones, the mean RMSE of the novel model declines by around 55% compared to that of spline, and by about 95% compared to that of back propagation neural networks. |
| first_indexed | 2024-12-11T09:07:07Z |
| format | Article |
| id | doaj.art-be90413dce3148148520543bb3098a25 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-11T09:07:07Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-be90413dce3148148520543bb3098a252022-12-22T01:13:36ZengMDPI AGAtmosphere2073-44332018-05-019519410.3390/atmos9050194atmos9050194Multivariate Interpolation of Wind Field Based on Gaussian Process RegressionMiao Feng0Weimin Zhang1Xiangru Zhu2Boheng Duan3Mengbin Zhu4De Xing5College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, ChinaBeijing Applied Meteorology Research Institute, Beijing 100029, ChinaCollege of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, ChinaThe resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the accuracy of spatial interpolation towards wind fields. A new composited multi-scale anisotropic kernel function for weather processes using two-dimensional space information is proposed. To fix the underfitting in this kernel caused by unilateral space information, multiple variables (wind direction, air temperature, and atmospheric pressure) are introduced, which generates a multivariate correction model based on the novel kernel function and Gaussian process regression. Focusing on different weather processes, two multivariate correction models are designed. The new models pave a new way to employ multi-scale local information, and extract the anisotropy and structure information. The experiments on 10 m wind fields for the weather processes without cyclones and for the weather processes with cyclones validate the efficiency. The mean RMSE of the multivariate correction model for the weather processes without cyclones is reduced by around 15% for the u wind component compared with that of a simple composited kernel. For the weather processes with cyclones, the mean RMSE of the novel model declines by around 55% compared to that of spline, and by about 95% compared to that of back propagation neural networks.http://www.mdpi.com/2073-4433/9/5/194space interpolationmachine learningwind fieldsmulti-scale anisotropy kernel functionGaussian process regression |
| spellingShingle | Miao Feng Weimin Zhang Xiangru Zhu Boheng Duan Mengbin Zhu De Xing Multivariate Interpolation of Wind Field Based on Gaussian Process Regression Atmosphere space interpolation machine learning wind fields multi-scale anisotropy kernel function Gaussian process regression |
| title | Multivariate Interpolation of Wind Field Based on Gaussian Process Regression |
| title_full | Multivariate Interpolation of Wind Field Based on Gaussian Process Regression |
| title_fullStr | Multivariate Interpolation of Wind Field Based on Gaussian Process Regression |
| title_full_unstemmed | Multivariate Interpolation of Wind Field Based on Gaussian Process Regression |
| title_short | Multivariate Interpolation of Wind Field Based on Gaussian Process Regression |
| title_sort | multivariate interpolation of wind field based on gaussian process regression |
| topic | space interpolation machine learning wind fields multi-scale anisotropy kernel function Gaussian process regression |
| url | http://www.mdpi.com/2073-4433/9/5/194 |
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