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OneScience-Group/
FourCastNet

Name: FourCastNet
Rating: 2.5 (8 reviews)

Short-to-Medium-Range Weather Forecast

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FourCastNet

Model Introduction

FourCastNet (Fourier Forecasting Neural Network) is a global weather forecast model based on AFNO (Adaptive Fourier Neural Operator), jointly developed by NVIDIA and multiple top-tier academic institutions.

Paper: FourCastNet: A Global Data-driven High-resolution Weather Forecasting Model

https://arxiv.org/abs/2202.11214

Model Description

FourCastNet is a global high-resolution weather forecast model based on the Adaptive Fourier Neural Operator, suitable for short-to-medium-range global weather forecast research.

Use Cases

Scenario	Description
Global Weather Forecast Research	Train a FourCastNet-style AFNO forecast model using annual ERA5 HDF5 data.
Local Quick Validation	Use synthetic data to verify data loading, training entry points, inference, and result scripts.
ModelScope / OneCode Execution	Download as a standalone model package, install dependencies, and run scripts directly.
Multi-GPU Training	Launch multi-process training via `torchrun`.

Usage Guide

1. OneCode Usage

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2. Manual Installation and Usage

Hardware Requirements

A GPU or DCU is recommended.
CPU can be used for import and small-scale connectivity verification; full training and inference will be slow.
DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching your cluster, is recommended.

Download the Model Package

bash

hf download OneScience-Group/FourCastNet --local-dir ./FourCastNet
cd FourCastNet

Install the Runtime Environment

DCU Environment

bash

# Please activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation is supported
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

GPU Environment

bash

# Please activate CONDA first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# uv installation is supported
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

Training Data Introduction

The OneScience community provides ERA5 data for training (due to file size limits, the current repository contains a slice of the full dataset). Users can download it with the command below and confirm that the data path in conf/config.yaml is set correctly:

hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data

Training

Single GPU:

python scripts/train.py

Multi-GPU:

torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py

Training outputs:

text

data/checkpoints/model_bak.pth
data/checkpoints/trloss.npy
data/checkpoints/valoss.npy

Training Weights

This repository provides weights trained on ERA5 reanalysis data in the weights/ folder. The weight files will be uploaded soon and are expected to be available in the near future.

Inference

Inference reads data/checkpoints/model_bak.pth by default:

python scripts/inference.py

Prediction results are output to:

result/output/

Evaluation and Visualization

python scripts/result.py

Output contents include:

result/rmse.npy
result/acc.npy
result/loss.png
Forecast comparison plots for specified dates and variables

OneScience Official Information

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation & License

This repository is an independent reproduction of FourCastNet, with the architecture design adapted from the original paper by Pathak et al. (2022).