[MGI30-P08] A data assimilation library with Python for parallel computing
Keywords:data assimilation, particle filter, parallel computing, Python
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.