Variation characteristics of precipitable water vapor retrieved by CORS in China Southern Power Grid during heavy rainfall

Changsheng Yun; Zhining Li; Yingzheng Shen

doi:10.1117/12.3029823

21 June 2024 Variation characteristics of precipitable water vapor retrieved by CORS in China Southern Power Grid during heavy rainfall

Changsheng Yun, Zhining Li, Yingzheng Shen

Author Affiliations +

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670W (2024) https://doi.org/10.1117/12.3029823
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Precipitable water vapor (PWV) mirrors the dynamic changes of atmospheric water vapor and can be accurately retrieved by the Global Navigation Satellite System (GNSS). Aiming at exploring the potential of the Continuously Operating Reference System (CORS) of China Southern Power Grid to contribute to meteorology, this paper first establishes a Weighted Mean Temperature (Tm) model that is more applicable in Guangzhou. Subsequently, the PWV sequence and variation characteristics during the period of heavy rainfall from June 30 to July 5, 2022, are inversed and analyzed based on the observation data of CORS in 15 reference stations around Guangzhou. The results indicate that PWV variations bear a close relationship with the actual rainfall events. The PWV increases remarkably before heavy rainfall, remains at a high level during rainfall, and gradually returns to a stable state after rainfall stops, revealing that PWV changes can serve as an indicator for heavy rainfall monitoring. Therefore, meteorological services in the CORS of Southern Power Grid can be further enhanced in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Changsheng Yun, Zhining Li, and Yingzheng Shen "Variation characteristics of precipitable water vapor retrieved by CORS in China Southern Power Grid during heavy rainfall", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670W (21 June 2024); https://doi.org/10.1117/12.3029823

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