太阳黑子自动识别与特征参量自动提取

日面上黑子数目反映了太阳活动水平的高低.黑子形态的复杂性和磁场的非势性与太阳活动爆发密切相关.随着高时空精度的太阳观测数据量的急剧增长,快速准确地自动识别日面上的黑子以及对黑子群特征自动提取已成为太阳活动预报的现实需求.本文针对SDO/HMI的活动区白光数据,利用数学形态法开展黑子自动识别研究,并在黑子识别基础上对黑子群的面积和黑子数进行了计算.通过对利用2011-2017年HMI活动区数据计算得到的黑子群面积和黑子数与NOAA/SWPC发布的活动区相应参量进行比较,发现本文计算结果与SWPC发布数据的变化趋势基本一致,相关性较好.其中黑子群面积的相关系数为0.77,黑子数的相关系数为0.79.研究结果表明,利用本文方法对SDO/HMI数据进行处理,能够得到高时间分辨率的黑子群特征参量,可为太阳活动预报提供及时准确的输入.

Automatic Detection of Sunspots and Extraction of Sunspot Characteristic Parameters

Sunspots 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.