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Abstract The Guangdong Lightning Mapping Array (GDLMA), as the first LMA in China, was deployed in Guangzhou, Guangdong Province, China, in November 2018 by the Chinese Academy of Meteorological Sciences and New Mexico Institute of Mining and Technology. An evaluation was conducted using Monte Carlo and an aircraft track. The average timing uncertainty of GDLMA is 35 ns based on the distributions of reduced chi‐square values. Based on the aircraft track, the average horizontal error is 13 m and the average vertical error is 41 m at an altitude of 4–5 km over the network, consistent with the Monte Carlo results. Location errors outside the network exhibit noticeable directionality. The ability to characterize lightning channels varies with different location errors. In locations that are far from the network center, only the basic structure of lightning flash can be presented, while closer to the network, the flash channel structure can be mapped well. Compared with Low‐to‐Mid Frequency E‐field Detection Array (MLFEDA), they were generally similar in overall structure, and some lightning flash characteristics such as flash duration and coverage area exhibited consistency. However, GDLMA demonstrated better flash channel structure characterization capability, while MLFEDA performed better in processes such as leader/return strokes. In addition, based on the comparison of spatial positions of one‐on‐one discharge events, we found that very high frequency sources were more located ahead of low frequency sources in the direction of lightning channel development. 

The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.