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-P11] Use of kernel regression in ensemble Kalman filters

*Shin'ya Nakano1 (1.The Institute of Statistical Mathematics)

Keywords:ensemble Kalman filter, kernel regression

The ensemble Kalman filters are now widely used for data assimilation in nonlinear systems in various field. In the ensemble Kalman filters, the uncertainty in a system is represented by a set of possible scenarios called ensemble. An estimate of the system state is produced by a linear combination of the ensemble members. This procedure of the ensemble Kalman filters can be rewritten in a form of the kernel regression approach. A formulation based on the kernel regression approach enables us to allow a nonlinear relationship between the state and the observation. In this study, a formulation of the ensemble Kalman filters based on the kernel regression approach is introduced and some extentions of the ensemble Kalman filters are discussed.