日本地球惑星科学連合2019年大会

講演情報

[E] ポスター発表

セッション記号 M (領域外・複数領域) » M-GI 地球科学一般・情報地球科学

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

2019年5月29日(水) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:中野 慎也(情報・システム研究機構 統計数理研究所)、藤井 陽介(気象庁気象研究所)、宮崎 真一(京都大学理学研究科)、三好 建正(理化学研究所)

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

*中野 慎也1有吉 雄哉2樋口 知之1 (1.情報・システム研究機構 統計数理研究所、2.日本文理大学工学部)

キーワード: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.