A Gaussian process regression-based sea surface temperature interpolation algorithm

Yongshun ZHANG; Miao FENG; Weimin ZHANG; Huizan WANG; Pinqiang WANG

doi:10.1007/s00343-020-0062-1

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A Gaussian process regression-based sea surface temperature interpolation algorithm

Physics | Updated：2023-05-20

- A Gaussian process regression-based sea surface temperature interpolation algorithm
- A Gaussian process regression-based sea surface temperature interpolation algorithm
- 海洋湖沼学报（英文） 2021年39卷第4期页码：1211-1221
- Affiliations：
  
  1.College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
  2.Key Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  Weimin ZHANG, E-mail:wmzhang@nudt.edu.cn
- Funds：
  
  the National Natural Science Foundation of China(41675097);the National Natural Science Foundation of China(41375113);© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
- DOI：10.1007/s00343-020-0062-1
  中图分类号：
- 收稿：2020-01-21，
  
  录用：2020-4-21，
  
  网络首发：2020-10-20，
  
  纸质出版：2021-07
- Accepted：
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A Gaussian process regression-based sea surface temperature interpolation algorithm[J]. 海洋湖沼学报（英文）, 2021,39(4):1211-1221.

Yongshun ZHANG, Miao FENG, Weimin ZHANG, et al. A Gaussian process regression-based sea surface temperature interpolation algorithm[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1211-1221.
A Gaussian process regression-based sea surface temperature interpolation algorithm[J]. 海洋湖沼学报（英文）, 2021,39(4):1211-1221. DOI： 10.1007/s00343-020-0062-1.

Yongshun ZHANG, Miao FENG, Weimin ZHANG, et al. A Gaussian process regression-based sea surface temperature interpolation algorithm[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1211-1221. DOI： 10.1007/s00343-020-0062-1.

摘要

Abstract

The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models. Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models. However

traditional interpolation methods (nearest neighbor interpolation

bilinear interpolation

and bicubic interpolation) lack physical constraints and can generate significant errors at land-sea boundaries and around islands. In this paper

a machine learning method is used to design an interpolation algorithm based on Gaussian process regression. The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information (sea surface wind stress

sea surface heat flux

and ocean current velocity). This greatly improves the spatial resolution of ocean features and the interpolation accuracy. The effectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature (SST). The root mean square error (RMSE) of the interpolation algorithm was 38.9%

43.7%

and 62.4% lower than that of bilinear interpolation

bicubic interpolation

and nearest neighbor interpolation

respectively. The interpolation accuracy was also significantly better in offshore area and around islands. The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.

关键词

Keywords

references

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