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
Pangu-Weather
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:
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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
modelscope download --model OneScience/Pangu-Weather --local_dir ./pangu_weather
cd pangu_weather
Install the Runtime Environment
DCU Environment
# 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
# 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:
modelscope download --dataset OneScience/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 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
python scripts/inference.py
Inference results will be saved to result/output/.
Evaluation and Visualization
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.