pyPDAF - A Python interface to Parallel Data Assimilation FrameworkΒΆ

pyPDAF is a Python interface to the Parallel Data Assimilation Framwork (PDAF) written in Fortran. The latest pyPDAF supports PDAF-V3.0.

As an interface to PDAF, pyPDAF supports all PDAF functionalities. You can use pyPDAF to construct a parallel ensemble data assimilation system purely in Python. The pyPDAF is designed as a framework that defines the workflow of given DA algorithms. Considering the versatility of the software, information on the model and observations are passed to the DA algorithms through user-supplied functions. With pyPDAF, all user-supplied functions can be implemented in Python. We expect that the coding of the user-supplied functions will be easier and more flexible than in Fortran due to the rich Python ecosystem.

pyPDAF can be used with two modes:

  • online mode: DA is performed without interrupting the model program. Here, the model code is extended by calling PDAF functions to generate a single program. In online mode, the filtering gets the model state and distributes the analysis to model by in-memory exchange. This is the recommended mode for better efficiency.

  • offline mode: DA is performed after the model program is finished. Here, a separate program is generated to perform DA. In offline mode, the filtering reads the model state from disk and writes the analysis to disk.

The potential applications of pyPDAF include:

  • online DA systems with Python models, e.g., machine-learning models

  • offline DA systems

This is a great tool for researchers who want to test and develop new DA systems. Compared to Fortran systems, the efficiency is decreased mainly from user-supplied functions and overhead for array conversions between Fortran and Python. The core DA algorithms are as efficient as in PDAF. Note that, for computational intensive user-supplied functions, the efficiency can be improved by using just-in-time compilation tools such as numba.

To get started, we highly recommend to start from the Jupyter notebook example for a serial ensemble DA system using a simple wave model.

We also provide more structured offline and online examples. One can adapt these examples based on their needs

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