Modelpublic

OneScience-Group/Pangu_Weather

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face

0likes11downloads

Model Card

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:

Click to Experience Intelligent One-Click AI4S Programming

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/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

hf download --repo-type dataset OneScience-Group/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.