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Atmosphere 2018, 9(5), 194; https://doi.org/10.3390/atmos9050194

Multivariate Interpolation of Wind Field Based on Gaussian Process Regression

1
College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China
2
Beijing Applied Meteorology Research Institute, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Received: 5 April 2018 / Revised: 11 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
(This article belongs to the Section Climatology and Meteorology)
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Abstract

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. View Full-Text
Keywords: space interpolation; machine learning; wind fields; multi-scale anisotropy kernel function; Gaussian process regression space interpolation; machine learning; wind fields; multi-scale anisotropy kernel function; Gaussian process regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Feng, M.; Zhang, W.; Zhu, X.; Duan, B.; Zhu, M.; Xing, D. Multivariate Interpolation of Wind Field Based on Gaussian Process Regression. Atmosphere 2018, 9, 194.

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