JpGU-AGU Joint Meeting 2017

Presentation information

[EE] Poster

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

[M-GI28] [EE] Data assimilation: A fundamental approach in geosciences

Mon. May 22, 2017 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall HALL7)

convener:Shin'ya Nakano(The Institute of Statistical Mathematics), Yosuke Fujii(Meteorological Research Institute, Japan Meteorological Agency), SHINICHI MIYAZAKI(Graduate School of Science, Kyoto University), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science)

[MGI28-P08] Multi-scale localization with NICAM-LETKF using real observations

*KEIICHI KONDO1, Takemasa Miyoshi1 (1.RIKEN Advanced Institute for Computational Science)

Keywords:data assimilation, Multi-scale data assimilation, Ensemble Kalman Filter

Covariance localization plays an essential role in the ensemble Kalman filter (EnKF) with a limited ensemble size. Localization limits the influence of observations and reduces the impact of sampling errors. To enhance localization, our previous studies proposed and investigated a multi-scale localization method named the “dual localization” method which coupled two separate localization scales using an intermediate AGCM under the perfect model scenario. The results showed consistent improvement over a traditional single localization approach. In this study, we further extended the previous study to use the real-world observations with the non-hydrostatic icosahedoral atmospheric model (NICAM) and to investigate how well the dual localization method captures the multi-scale covariance structures. The results showed that the dual-localization method produced generally better spatial correlation patterns. We will present the newest results up to the time of the meeting.