---
datasets:
- OneScience/ERA5
frameworks:
- ""
language:
- en
license: mit
tags:
- OneScience
- Earth Science
- ERA5
- Medium-Range Weather Forecasting
- ViT
tasks: []
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">Stormer</span>
  </strong>
</p>


# Model Overview

Stormer was jointly developed by researchers at Argonne National Laboratory and the University of California, Los Angeles (UCLA). Its core paper was published at NeurIPS 2024, a leading conference in artificial intelligence.

Paper: *Scaling Transformer Neural Networks for Skillful and Reliable Medium-Range Weather Forecasting*

https://arxiv.org/abs/2312.03876

# Model Description

Stormer uses a standard Vision Transformer architecture and provides a streamlined deep learning model for medium-range weather forecasting.


# Use Cases

| Use Case | Description |
| :---: | :--- |
| Weather forecasting training | Train Stormer on ERA5 data in HDF5 format. |
| Quick local validation | Use synthetic data to validate data loading, model training and inference, and visualization of inference results. |
| ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the included scripts directly. |
| Multi-GPU training | Launch multi-process training with `torchrun`. |


# Usage

## 1. Using OneCode

Use the OneCode online environment for an intelligent, one-click AI4S development experience:

[Try one-click AI4S development with OneCode](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Setup

**Hardware Requirements**

- A GPU or DCU is recommended.
- A CPU can be used for import checks and connectivity validation with a minimal configuration, but full training and inference will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or later is recommended; alternatively, use the OneScience-recommended version compatible with your cluster.

### Download the Model Package

```bash
hf download --model OneScience-Group/Stormer --local-dir ./Stormer
cd Stormer
```

### Set Up the Runtime Environment

**DCU Environment**

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

**GPU Environment**

```bash
# 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
# Installation with uv is also supported.
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

### Training Data

The OneScience community provides ERA5 data for training. Because of file-size constraints, the repository currently contains a self-contained data slice. Download the data with the following command and ensure that the data path in `conf/config.yaml` is configured correctly:

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


### Training


Single GPU:

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

Multiple GPUs:

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

Training saves the `model_bak.pth` checkpoint under `data/checkpoints/`.

### Pre-trained Weights

This repository will provide weights trained on ERA5 reanalysis data in the `weights/` directory. The weight files are being prepared and will be uploaded soon.

### Inference

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

By default, inference loads `data/checkpoints/model_bak.pth`, and results are saved to `result/output/`.


### Evaluation and Visualization

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


# 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

- This repository is a reproduction of the original Stormer paper.