In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accep...In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.展开更多
以METOP-A、Suomi-NPP历史资料作为参照,系统分析比较了2008—2020年4颗风云三号卫星的微波温度计(Microwave Temperature Sounder, MWTS)再定标历史资料质量。结果表明,4颗卫星的MWTS探测性能稳步上升,再定标数据集有效消除了遥感仪器...以METOP-A、Suomi-NPP历史资料作为参照,系统分析比较了2008—2020年4颗风云三号卫星的微波温度计(Microwave Temperature Sounder, MWTS)再定标历史资料质量。结果表明,4颗卫星的MWTS探测性能稳步上升,再定标数据集有效消除了遥感仪器在轨期间数据异常跳变、寿命期内遥感仪器辐射响应衰变、不同卫星间的辐射定标差异等因素影响,大幅提升了MWTS历史数据集的准确性和均匀性,使得再定标后的对流层和平流层通道数据与国外同类型仪器数据偏差在±0.1 K范围内。本文还重点分析比较了对流层中高层和平流层低层两个探测通道,结果表明FY-3D MWTS再定标数据和美国NOAA卫星应用研究中心STAR长序列数据集针对中高层大气的表现类似,平均亮温在时间变化和空间分布具有相似的特征,月均全球高空亮温年变化趋势差异最大为0.002 4。因此,2020年之后的FY-3D再定标数据,可以接续STAR长序列数据集,用于中高层大气的温度变化检测与分析。展开更多
基金This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103Hunan Provincial Natural Science Foundation of China,Grant No.2017JJ20162016 Science Research Project of Hunan Provincial Department of Education,Grant No.16C0269.This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province.Open project,Grant Nos.20181901CRP03,20181901CRP04,20181901CRP05 National Social Science Fund Project:Research on the Impact Mechanism of China’s Capital Space Flow on Regional Economic Development(Project No.14BJL086).
文摘In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.
