Japan Geoscience Union Meeting 2023

Presentation information

[J] Oral

M (Multidisciplinary and Interdisciplinary) » M-SD Space Development & Earth Observation from Space

[M-SD35] Future Missions of Satellite Earth Observation

Thu. May 25, 2023 1:45 PM - 3:00 PM 104 (International Conference Hall, Makuhari Messe)

convener:Yoshiaki HONDA(Center for Environmental Remote Sensing, Chiba University), Yukari Takayabu(Atmosphere and Ocean Research Institute, the University of Tokyo), YUKI SOFUE (Chiba university ), Keiichi Ohara(Japan Aerospace Exploration Agency), Chairperson:Yoshiaki HONDA(Center for Environmental Remote Sensing, Chiba University)

2:30 PM - 2:45 PM

[MSD35-04] Lightning Sensor from Geo-Stationary Orbit

*Tomoo Ushio1, Yuuki Wada1, Yousuke Sato2, Mitsuteru Sato2, Ryo Yoshida3, Satoru Yoshida3, Syugo Hayashi3, Eiichi Yoshikawa4 (1.Osaka University, 2.Hokkaido University, 3.JMA, 4.JAXA)

Keywords:Lightning, Geostationary orbit

Lightning sensors on geostationary satellites are included in the World Meteorological Organization (WMO)'s Vision for WIGOS in 2040 as a sensor that should consist a global observation system. The new-generation geostationary meteorological satellite in China has started operation, and the next-generation geostationary meteorological satellite in Europe is scheduled to be installed, expecting that lightning location data will become indispensable in the future.
In Japan, Global Lightning and Sprite Measurement Mission (GLIMS) has been developed and installed on the exposure module of the International Space Station "Kibo" by the group of the universities and JAXA, and scientific research has been carried out. Among them, multi-wavelength observation to distinguish between ground discharge and cloud discharge has been studied and its possibility has been shown.
This proposal proposes the research and development of Japan's original multi-wavelength lightning discharge observation sensor on a geostationary satellite that can distinguish between ground discharge and cloud discharge based on GLIMS data.