Japan Geoscience Union Meeting 2018

Presentation information

[EE] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI22] Data assimilation: A fundamental approach in geosciences

Sun. May 20, 2018 9:00 AM - 10:30 AM 302 (3F International Conference Hall, Makuhari Messe)

convener:Shin'ya Nakano(The Institute of Statistical Mathematics), Yosuke Fujii(Meteorological Research Institute, Japan Meteorological Agency), SHINICHI MIYAZAKI(京都大学理学研究科, 共同), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Chairperson:Fujii Yosuke

10:05 AM - 10:25 AM

[MGI22-05] Issues regarding maintaining ensemble spreads, balance, and high-resolution information in rapid-update-cycle radar data assimilation with the LETKF

★Invited Papers

*Guo-Yuan Lien1, Takemasa Miyoshi1 (1.RIKEN Advanced Institute for Computational Science)

Keywords:Radar data assimilation, LETKF, Rapid update cycle

Assimilation of meteorological radar data has been proven useful for analyses and short-range forecasts of convective storms. At RIKEN, we have been investigating the feasibility and usefulness of advancing the resolution of radar data assimilation with the LETKF, using the K computer resource. Ideally, it is desirable to assimilate the radar data at high spatial and temporal resolution, hopefully to extract most high-resolution information in the observation. Running a rapid-update data assimilation cycle is also thought to be beneficial in terms that it could avoid the linearization errors of highly nonlinear evolution of convective systems. However, with a typical ensemble data assimilation method, several important issues, such as the maintenance of the ensemble spreads and model balance, could prevent us from effectively using the observation information at high spatial and temporal resolution. It is very difficult to overcome all problems, but we find several techniques that are practically useful and suitable for the high-resolution convective-scale data assimilation. We will discuss these techniques, such as additive noise, observation number limit, and the deterministic analysis member, with some experimental results that show promise.