首页|Studies from Tongji University Further Understanding of Machine Learning (Statistical Characteristics of Multi-scale Auroral Arc Width Based On Machine Learning)
Studies from Tongji University Further Understanding of Machine Learning (Statistical Characteristics of Multi-scale Auroral Arc Width Based On Machine Learning)
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NETL
NSTL
Amer Geophysical Union
Data detailed on Machine Learning have been presented. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “Arc width is important for understanding the generation mechanism of auroral arcs. However, the continuity or discreteness of the distribution of small and meso-large scale auroral arc widths has not been determined in previous studies.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Basic Research Plan in Shaanxi Province of China, Open Fund of State Key Laboratory of Loess and Quaternary Geology. Our news journalists obtained a quote from the research from Tongji University, “This study employs machine learning techniques to investigate the distribution of arc widths across multiple scales using multifield- of-view (multi-FOV) auroral observations. Based on the 180 degrees, 47 degrees, and 19 degrees auroral observations at the Antarctic Zhongshan Station from February to October 2012, the statistical results demonstrate that the auroral arc width spectrum is continuously distributed across small, meso, and large scales, suggesting that the mechanisms responsible for their generation are capable of producing arcs at all scales. Furthermore, the arc width distribution at each FOV can be well fitted with a log-normal function. We also find that the main widths observed at different FOVs depend on the spatial resolution of the instruments. Our work provides new observational evidence for the generation mechanism of auroral arcs. Auroral arcs are beautiful atmospheric phenomena that occur in the polar regions of the Earth. Understanding their width helps us to understand how they are formed. This study used machine learning techniques to analyze auroral observations and found that the width of auroral arcs is continuous across different scales. We also found that a log-normal function fits the distribution of arc widths well at each scale. In addition, the observed widths at different field-of-views are strongly influenced by the spatial resolution of the instrument used. This research provides new insights into the understanding of auroral arcs.”
ShanghaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTongji University
NETL
NSTL
Amer Geophysical Union
