Home > Published Issues > 2023 > Volume 11, No. 3, September 2023 >
JOIG 2023 Vol.11(3): 271-281
doi: 10.18178/joig.11.3.271-281

Solar Radiation and Weather Analysis of Meteorological Satellite Data by Tensor Decomposition

N. Watanabe 1, A. Ishida 2, J. Murakami 3, and N. Yamamoto 3,*
1. Advanced Course of Electronics and Information Systems Engineering, Kumamoto College, National Institute of Technology, Koshi, Japan; Email: ae22watanabe@g.kumamoto-nct.ac.jp (N.W.)
2. Faculty of Liberal Arts, Kumamoto College, National Institute of Technology, Koshi, Japan;
Email: ishida@kumamoto-nct.ac.jp (A.I.)
3. Faculty of Electronics and Information Systems Engineering, Kumamoto College, National Institute of Technology, Koshi, Japan; Email: jun@kumamoto-nct.ac.jp (J.M.)
*Correspondence: naoki@kumamoto-nct.ac.jp (N.Y.)

Manuscript received February 10, 2023; revised March 21, 2022, accepted May 10, 2023.

Abstract—In this study, the data obtained from meteorological satellites were analyzed using tensor decomposition. The data used in this paper are meteorological image data observed by the Himawari-8 satellite and solar radiation data generated from Himawari Standard Data. First, we applied Higher-Order Singular Value Decomposition (HOSVD), a type of tensor decomposition, to the original image data and analyzed the features of the data, called the core tensor, obtained from the decomposition. As a result, it was found that the maximum value of the core tensor element is related to the cloud cover in the observed area. We then applied Multidimensional Principal Component Analysis (MPCA), an extension of principal component analysis computed using HOSVD, to the solar radiation data and analyzed the Principal Components (PC) obtained from MPCA. We also found that the PC with the highest contribution rate is related to the solar radiation in the entire observation area. The resulting PC score was compared to actual weather data. From the result, it was confirmed that the temporal transition of the amount of solar radiation in this area can be expressed almost correctly by using the PC score.

Keywords—Tensor decomposition, Higher-Order Singular Value Decomposition (HOSVD), Multidimensional Principal Component Analysis (MPCA), meteorological image data, solar radiation data, Himawari-8

Cite: N. Watanabe, A. Ishida, J. Murakami, and N. Yamamoto, "Solar Radiation and Weather Analysis of Meteorological Satellite Data by Tensor Decomposition," Journal of Image and Graphics, Vol. 11, No. 3, pp. 271-281, September 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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