---
license: apache-2.0
language:
- en
- zh
tags:
- OneScience
- Earth Science
- Weather Forecast
- Short-to-Medium-Range Weather Forecast
- ERA5
frameworks: PyTorch
datasets:
  - OneScience/ERA5
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">FourCastNet</span>
  </strong>
</p>

# 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

Experience intelligent one-click AI4S programming through the OneCode online environment:

[Click to Experience Intelligent One-Click AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 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
modelscope download --model OneScience/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:

```bash
modelscope download --dataset OneScience/ERA5 --local_dir ./data
```

### Training

Single GPU:

```bash
python scripts/train.py
```

Multi-GPU:

```bash
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:

```bash
python scripts/inference.py
```

Prediction results are output to:

```text
result/output/
```

### Evaluation and Visualization

```bash
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).