FRIDOM: A new modular Python based framework for geophysical fluid simulations

Numerical modeling frameworks are essential for advancing our understanding of oceanic and atmospheric processes. Traditional ocean models, often written in Fortran, offer high computational efficiency but can be challenging for young scientists due to their complexity and lack of GPU support.

We present the Framework for Idealized Ocean Models (FRIDOM), a Python-based and modular framework for fluid simulations. Inspired by machine learning frameworks like TensorFlow and PyTorch, FRIDOM’s flexible design supports applications beyond ocean-specific contexts or idealized setups. Realistic setups with complex topography and realistic forcings are already possible. Currently, it includes implementations of the 2D shallow water equations and 3D nonhydrostatic Boussinesq equations, compatible with both rectilinear Arakawa C-grids and spectral grids. The framework is designed for seamless integration of new governing equations, grid types, such as unstructured grids used in FESOM, and discretization methods.

FRIDOM also provides advanced flow decomposition tools, including Optimal Balance and Nonlinear Normal Mode Decomposition, for separating balanced and wave components in flow fields. Comprehensive documentation, tutorials, and examples ensure accessibility, making FRIDOM a powerful and user-friendly framework for fluid modeling and analysis, with the capability of performing high-resolution simulations to address important questions in geophysical fluid dynamics.