Japan Geoscience Union Meeting 2021

Presentation information

[J] Oral

M (Multidisciplinary and Interdisciplinary) » M-IS Intersection

[M-IS13] Dynamics of eruption cloud and cumulonimbus; modelling and observation

Sun. Jun 6, 2021 9:00 AM - 10:30 AM Ch.25 (Zoom Room 25)

convener:Eiichi Sato(Meteorological Research Institute), Fukashi Maeno(Earthquake Research Institute, University of Tokyo), Takeshi Maesaka(National Research Institute for Earth Science and Disaster Resilience), Chairperson:Eiichi Sato(Meteorological Research Institute)

9:15 AM - 9:30 AM

[MIS13-02] PUFF Model Prediction of Volcanic Ash Plume Dispersal for Sakurajima Using MP Radar Observation

*Hiroshi Tanaka1, Haruhisa Nakamichi2, Masato Iguchi2 (1.Center for Computational Sciences, University of Tsukuba, 2.Sakurajima Volcano Research Center, DPRC, Kyoto University, Kagoshima)

Keywords:ash dispersion, Sakurajima volcano, data assimilation

In this study, a real-time volcanic ash plume prediction by the PUFF system was applied to the Sakurajima volcano (which erupted at 17:24 Japan Standard Time (JST) on 8 November, 2019), using the direct observation of the multi-parameter (MP) radar data installed at the Sakurajima Volcano Research Center. The MP radar showed a plume height of 5500 m a.s.l. around the volcano. The height was higher than the 4000 m by the PUFF system, but was lower than the observational report of 6500 m by the Japan Meteorological Agency in Kagoshima. In this study, ash particles by the MP radar observation were assimilated to the running PUFF system operated by the real-time emission rate and plume height, since the radar provides accurate plume height. According to the simulation results, the model prediction has been improved in the shape of the ash cloud with accurate plume top by the new MP radar observation. The plume top is corrected from 4000 m to 5500 m a.s.l., and the three-dimensional (3D) ash dispersal agrees with the observation. It was demonstrated by this study that the direct observation of MP radar obviously improved the model prediction, and enhanced the reliability of the prediction model.