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Comprehensive Marine Observing Experiment Based on High-Altitude Large Unmanned Aerial Vehicle (South China Sea Experiment 2020 of the 'Petrel Project')

Authors :
Kebing Tang
Xikun Fei
Liangxu Li
Wenwu Peng
Jianping Luo
Rongkang Yang
Daxin Liu
Xiaoxia Li
Hongbin Chen
Qiyun Guo
Zhang Xuefen
Xia Sun
Xiaodong Han
Guo Ran
Source :
Advances in Atmospheric Sciences. 38:531-537
Publication Year :
2021
Publisher :
Springer Science and Business Media LLC, 2021.

Abstract

In collaboration with 12 other institutions, the Meteorological Observation Center of the China Meteorological Administration undertook a comprehensive marine observation experiment in the South China Sea using the Yilong-10 high-altitude large unmanned aerial vehicle (UAV). The Yilong-10 UAV carried a self-developed dropsonde system and a millimeter-wave cloud radar system. In addition, a solar-powered unmanned surface vessel and two drifting buoys were used. The experiment was further supported by an intelligent, reciprocating horizontal drifting radiosonde system that was deployed from the Sansha Meteorological Observing Station, with the intent of producing a stereoscopic observation over the South China Sea. Comprehensive three-dimensional observations were collected using the system from 31 July to 2 August, 2020. This information was used to investigate the formation and development processes of Typhoon Sinlaku (2020). The data contain measurements of 21 oceanic and meteorological parameters acquired by the five devices, along with video footage from the UAV. The data proved very helpful in determining the actual location and intensity of Typhoon Sinlaku (2020). The experiment demonstrates the feasibility of using a high-altitude, large UAV to fill in the gaps between operational meteorological observations of marine areas and typhoons near China, and marks a milestone for the use of such data for analyzing the structure and impact of a typhoon in the South China Sea. It also demonstrates the potential for establishing operational UAV meteorological observing systems in the future, and the assimilation of such data into numerical weather prediction models.

Details

ISSN :
18619533 and 02561530
Volume :
38
Database :
OpenAIRE
Journal :
Advances in Atmospheric Sciences
Accession number :
edsair.doi...........25db3b9bcbd265434150beba04c77bd9
Full Text :
https://doi.org/10.1007/s00376-020-0314-1