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

# Model Introduction

Pangu-Weather is a global medium-range weather forecast model proposed by Huawei Cloud, capable of rapidly predicting surface variables and multi-pressure-level upper-air variables.

Paper: Accurate medium-range global weather forecasting with 3D neural networks  
https://www.nature.com/articles/s41586-023-06185-3

# Model Description

Pangu-Weather is based on a 3D Earth-Specific Transformer architecture, trained on ERA5 data, and designed for short-to-medium-range weather forecasting.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Weather Forecast Training | Train Pangu-Weather using ERA5 HDF5 data |
| Local Quick Validation | Use synthetic data to verify data loading, model training & inference, and inference result visualization. |
| 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/Pangu-Weather --local_dir ./pangu_weather
cd pangu_weather
```

### 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 will save `model_bak.pth` under `data/checkpoints/`.

### Training Weights

This repository provides weights trained on ERA5 data from 1979 to 2025 in the `weights/` folder. At the 6-hour forecast lead time, these weights outperform the official open-source ONNX weights.

### Inference

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

Inference results will be saved to `result/output/`.

### Evaluation and Visualization

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

# 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 a reproduction of the original Pangu-Weather paper.