JpGU-AGU Joint Meeting 2020

Presentation information

[E] Oral

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

[M-GI33] Data assimilation: A fundamental approach in geosciences

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

[MGI33-12] Iterative ensemble variational methods and its application for the prediction of geomagnetic secular variation

*Shin ya Nakano1, Takuto Minami2, Futoshi Takahashi3, Masaki Matsushima4, Hisayoshi Shimizu5, Hiroaki TOH6 (1.The Institute of Statistical Mathematics, 2.Graduate School of Science, Kobe University, 3.Faculty of Science, Kyushu University, 4.Department of Earth and Planetary Sciences, School of Science, Tokyo Institute of Technology, 5.Earthquake Research Institute, the University of Tokyo, 6.Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University)

Keywords:Data assimilation, Ensemble variational method, Geomagnetic field, Geomagnetic secular variation

The 4-dimensional ensemble variational method (4DEnVar) is a data assimilation method which is easy to implement as a post-process. The iterative version of the 4DEnVAR, which is sometimes referred to as the iterative ensemble smoother, is also regarded as a useful tool for nonlinear data assimilation problems. Since these methods are derived based on a linear approximation of a dynamical system model, the behavior of the algorithm under ths situations with large uncertainties is not trivial. In this study, the accuracy of the algorithm was evaluated after considering second and higher order terms of the Taylor expansion of a system model. The sufficient conditions for finding a local maximum of the objective function are then explored, and the behavior of the algorithm under the situation with nonlinearity is discussed. The applications for the prediction of geomagnetic secular variation based on the above approach will also be introduced.