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.
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