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

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-09-01 DOI:10.18178/joig.11.3.271-281

N. Watanabe, A. Ishida, J. Murakami, N. Yamamoto

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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.

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基于张量分解的气象卫星资料太阳辐射与天气分析

在本研究中，利用张量分解对气象卫星数据进行分析。本文使用的数据是由Himawari-8卫星观测到的气象图像数据和Himawari标准数据生成的太阳辐射数据。首先，我们将高阶奇异值分解(HOSVD)作为张量分解的一种，对原始图像数据进行分解，并对分解得到的数据特征——核心张量进行分析。结果发现，核心张量元的最大值与观测区域的云量有关。在此基础上，将基于HOSVD计算的主成分分析的扩展——多维主成分分析(MPCA)应用于太阳辐射数据，并对得到的主成分进行了分析。我们还发现，贡献率最高的PC与整个观测区的太阳辐射有关。得出的PC得分与实际天气数据进行了比较。结果表明，利用PC分数可以较准确地表达该地区太阳辐射量的时间变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.