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Modulus Checkpoints: FourCastNet V2 Small

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FourCastNet V2 (small) model checkpoint package. FourCastNet V2 is a deep learning model for weather prediction that uses Spherical Harmonics Neural Operators.
Latest Version
September 8, 2023
2.93 GB


This NGC asset is a FourCastNet v2 SFNO model checkpoint package. Model checkpoint package refers to the set of artifacts needed to run inference using pre-trained model which includes the model checkpoint, set of sample inputs, inference script.


Please refer to the reference paper to learn about the model architecture. In training this checkpoint, Spherical Fourier Neural Operator (SFNO) is applied to forecasting atmospheric dynamics, and demonstrates stable autoregressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change.


The model is trained on a 73-channel subset of the ERA5 reanalysis data on single levels and pressure levels that is pre-processed and stored into HDF5 files.

How to use?

A minimal inference script is provided in this model checkpoint package to get you started easily.

To run inference on this checkpoint, you can follow the below steps (all the files needed are included in the zip file):

  1. Launch Modulus docker container

    docker run --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --runtime nvidia -v ${PWD}:/examples -it
  2. Download and install earth2mip.

    git clone
    cd earth2mip && pip install .   
  3. Download this checkpoint zip file, and unzip it

    wget ''
  4. Run the simple inference

    cd fcnv2_sm/

You can also use the model checkpoint from this model checkpoint package to evaluate it with the earth2mip framework.


Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere