README.md

---
frameworks: PyTorch
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecast
- Short-to-Medium-Range Weather Forecast
- ERA5
- FourCastNet
- SFNO
tasks: []
datasets:
- OneScience/ERA5
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">FourCastNet_v2</span>
  </strong>
</p>

# Model Introduction

FourCastNet v2 is a global weather forecast model based on the Spherical Fourier Neural Operator (SFNO), proposed by NVIDIA and its collaborators.

Paper: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

https://arxiv.org/abs/2306.03838

# Model Description

The key architectural change from v1 is replacing the Adaptive Fourier Neural Operator (AFNO) with the Spherical Fourier Neural Operator (SFNO).

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Global weather forecast training | Train an SFNO-style FourCastNet v2 model with 73-channel ERA5 HDF5 data. |
| Local quick validation | Use synthetic ERA5 files to check the training, inference, and result-visualization pipeline. |
| ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. |
| Multi-GPU training | Launch PyTorch DDP with `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
hf download OneScience-Group/FourCastNet_v2 --local-dir ./FourCastNet_v2
cd FourCastNet_v2
```

### 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 an ERA5 data slice that can be downloaded as follows:

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

Real HDF5 annual files must contain `fields`, variable attributes, `time_step`, `global_means`, and `global_stds`. When real data is unavailable, first generate synthetic files for pipeline validation:

```bash
python scripts/fake_data.py
```

### Training

Single GPU:

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

Multi-GPU:

```bash
torchrun --nproc_per_node=8 scripts/train.py
```

The default checkpoint is saved to `data/checkpoint/one_step/model_bak.pt`.

### Fine-tuning

Single GPU:

```bash
python scripts/train.py --stage finetune
```

Multi-GPU:

```bash
torchrun --nproc_per_node=8 scripts/train.py --stage finetune
```

The checkpoint is saved to `data/checkpoint/<stage>/model_bak.pt` by default.

### Training Weights

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

### Inference

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

Prediction results are written to `result/output/` by default.

### Evaluation and Visualization

```bash
python scripts/result.py
```

The default output includes latitude-weighted RMSE/ACC metrics and `result/figures/t2m_forecast.png`.

# Official OneScience Resources

| 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 and License

- The SFNO numerical implementation of FourCastNet v2 follows the design of NVIDIA Earth2MIP and related official implementations. The upstream code and model licenses and copyright notices must be retained.