Japan Geoscience Union Meeting 2019

Presentation information

[E] Poster

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

[M-GI30] Data assimilation: A fundamental approach in geosciences

Wed. May 29, 2019 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

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)

[MGI30-P08] A data assimilation library with Python for parallel computing

*Shin ya Nakano1, Yuya Ariyoshi2, Tomoyuki Higuchi1 (1.The Institute of Statistical Mathematics, 2.Faculty of Engineering, Nippon Bunri University)

Keywords:data assimilation, particle filter, parallel computing, Python

Parallel computing is essential to reduce the computational time in the ensemble-based data assimilation. However, it requires skills in parallel programming. It is sometimes a hard task to attain high computational efficiency with ensemble-based data assimilation. In particular, the particle filter (PF) algorithm, which is applicable to nonlinear and/or non-Gaussian problems, contains a procedure difficult to parallelize.
It is thus challenging to achieve high computational efficiency with the PF even for experienced users.

We have developed a Python library named P-cubed (Python Parallelized Particle Filter Library), that enables us to use parallel-ready PF algorithms with high parallel efficiency. Now we are also planning to attach a module of other data assimilation algorithms such as the ensemble Kalman filter to this library. In this presentation, we introduce the parallelized PF algorithms which are already available in P-cubed and explain the design and characteristics of the library. Future prospects of this library will also be discussed.