Alternative TitleAutomatic Detection of Sunspots and Extraction of Sunspot Characteristic Parameters
李泠; 崔延美; 刘四清; 雷蕾
Source Publication空间科学学报
Volume40Issue:3Pages:315-322; AR:0254-6124(2020)40:3<315:TYHZZD>2.0.TX;2-4
Keyword黑子群面积 黑子数 自动识别 数学形态法 Sunspot group area Sunspot number Automatic detection Mathematical morphology
Other AbstractSunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun's magnetic activity. With a substantial increase in the size of solar image data archives, the automated detection and verification of various features of interest are becoming increasingly important for the reliable forecast of solar activity and space weather. In order to use high time-cadence SDO/HMI data and extract the main sunspot features for forecasting solar activities, we constructed an automatic detecting sunspot procedure with a mathematical morphology tool and calculated sunspot group area and sunspot number. By comparing our results with those obtained from Solar Region Summary compiled by NOAA/SWPC, it is found that sunspot group area and sunspot number computed with our algorithm are in good agreement with the active region values compiled by SWPC, and the corresponding correlation coefficients of sunspot group area and sunspot number are 0.77 and 0.79,respectively. In this study, high time- cadence feature parameters obtained from HMI data can provide timely and accurate inputs for solar activity forecasting.
Indexed ByCSCD
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Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Affiliation1.李泠, 中国科学院国家空间科学中心
2.中国科学院大学, 北京
3.北京 100190
4.100049, 中国.
5.刘四清, 中国科学院国家空间科学中心
6.中国科学院大学, 北京
7.北京 100190
8.100049, 中国.
9.雷蕾, 中国科学院国家空间科学中心
10.中国科学院大学, 北京
11.北京 100190
12.100049, 中国.
13.崔延美, 中国科学院国家空间科学中心, 北京 100190, 中国.
14.Li Ling, National Space Science Center, Chinese Academy of Sciences
15.University of Chinese Academy of Sciences, Beijing
16.Beijing 100190
18.Liu Siqing, National Space Science Center, Chinese Academy of Sciences
19.University of Chinese Academy of Sciences, Beijing
20.Beijing 100190
22.Lei Lei, National Space Science Center, Chinese Academy of Sciences
23.University of Chinese Academy of Sciences, Beijing
24.Beijing 100190
26.Cui Yanmei, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
Recommended Citation
GB/T 7714
李泠,崔延美,刘四清,等. 太阳黑子自动识别与特征参量自动提取[J]. 空间科学学报,2020,40(3):315-322; AR:0254-6124(2020)40:3<315:TYHZZD>2.0.TX;2-4.
APA 李泠,崔延美,刘四清,&雷蕾.(2020).太阳黑子自动识别与特征参量自动提取.空间科学学报,40(3),315-322; AR:0254-6124(2020)40:3<315:TYHZZD>2.0.TX;2-4.
MLA 李泠,et al."太阳黑子自动识别与特征参量自动提取".空间科学学报 40.3(2020):315-322; AR:0254-6124(2020)40:3<315:TYHZZD>2.0.TX;2-4.
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