Non-seismic tsunami real-time forecasting under data assimilation approach – case study for Hunga Tonga - Hunga Ha’apai volcanic eruption tsunami using S-net data

Naotaka YAMAMOTO CHIKASADA; Wataru Suzuki

Presentation information

[J] Oral

S (Solid Earth Sciences ) » S-CG Complex & General

[S-CG55] Reducing risks from earthquakes, tsunamis & volcanoes: new applications of realtime geophysical data

Mon. May 23, 2022 3:30 PM - 5:00 PM 301B (International Conference Hall, Makuhari Messe)

convener:Masashi Ogiso(Meteorological Research Institute, Japan Meteorological Agency), convener:Masumi Yamada(Disaster Prevention Research Institute, Kyoto University), Yusaku Ohta(Research Center for Prediction of Earthquakes and Volcanic Eruptions, Graduate School of Science, Tohoku University), convener:Naotaka YAMAMOTO CHIKASADA(National Research Institute for Earth Science and Disaster Resilience), Chairperson:Masumi Yamada(Disaster Prevention Research Institute, Kyoto University), Yusaku Ohta(Research Center for Prediction of Earthquakes and Volcanic Eruptions, Graduate School of Science, Tohoku University), Masashi Ogiso(Meteorological Research Institute, Japan Meteorological Agency)

3:30 PM - 3:45 PM

[SCG55-01] Non-seismic tsunami real-time forecasting under data assimilation approach – case study for Hunga Tonga - Hunga Ha’apai volcanic eruption tsunami using S-net data

*Naotaka YAMAMOTO CHIKASADA¹, Wataru Suzuki¹ (1.National Research Institute for Earth Science and Disaster Resilience)

Keywords:Tsunami, Tonga volcano eruption, Data assimilation

Hunga Tonga - Hunga Ha’apai (HTHH) volcano explosively erupted on around 1PM Jan 15^th, 2022 (JST) and induced tsunami not only around volcano region but also far field. By using densely and widely distributed ocean bottom pressure gauges network, we could detect and forecast tsunami before reaching to the east coast of Japan using data assimilation approach (TDAC: Tsunami Data Assimilation Code) provided by Maeda et al. (2015). In our experimental operation, we continuously assimilate observed data taken by the Seafloor observation network for earthquakes and tsunamis (S-net) and forecast tsunami height in the eastern region of Japan in every 30 seconds. In this presentation, we present the possibilities to detect and forecast tsunami height on the east coast of Japan before reaching tsunami around them. And we discuss the effect of noise to perform forecast continuously.
A part of this work was supported by JSPS KAKENHI Grant Numbers JP18K04674 and JP21K21353. We used Global tsunami Terrain Model (GtTM https://doi.org/10.17598/NIED.0021) created by supporting of the Kurata Grants from the Hitachi Global Foundation.