Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method
Joint Authors
Shi, Chun-Xiang
Huang, Weimin
Wang, Shan
Hu, Jianhui
Shan, Huiling
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Due to limited number of weather stations and interruption of data collection, the temperature field data may be incomplete.
In the past, spatial interpolation is usually used for filling the data gap.
However, the interpolation method does not work well for the case of the large-scale data loss.
Matrix completion has emerged very recently and provides a global optimization for temperature field data reconstruction.
A recovery method is proposed for improving the accuracy of temperature field data by using sparse low-rank matrix completion (SLR-MC).
The method is tested using continuous gridded data provided by ERA Interim and the station temperature data provided by Jiangxi Meteorological Bureau.
Experimental results show that the average signal-to-noise ratio can be increased by 12.5%, and the average reconstruction error is reduced by 29.3% compared with the matrix completion (MC) method.
American Psychological Association (APA)
Wang, Shan& Hu, Jianhui& Shan, Huiling& Shi, Chun-Xiang& Huang, Weimin. 2019. Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method. Advances in Meteorology،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1118629
Modern Language Association (MLA)
Wang, Shan…[et al.]. Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method. Advances in Meteorology No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1118629
American Medical Association (AMA)
Wang, Shan& Hu, Jianhui& Shan, Huiling& Shi, Chun-Xiang& Huang, Weimin. Temperature Field Data Reconstruction Using the Sparse Low-Rank Matrix Completion Method. Advances in Meteorology. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1118629
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118629