OneScience/FengWu

FengWu

Model Introduction

FengWu is a global medium-range weather forecast foundation model jointly released by the Shanghai Artificial Intelligence Laboratory and multiple universities. It has been adopted by organizations such as the Hong Kong Observatory for operational weather forecasting.

Paper: FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

https://arxiv.org/abs/2304.02948

Model Description

The FengWu model is built on a multi-modal and multi-task deep learning approach, without relying on traditional physical equations. It is trained entirely on ERA5 reanalysis data.

Use Cases

Scenario	Description
Weather Forecast Training	Train FengWu 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

modelscope download --model OneScience/FengWu --local_dir ./FengWu
cd FengWu

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 39 years of ERA5 reanalysis data in the weights/ folder. The weight files will be uploaded soon and are expected to be available in the near future.

Inference

python scripts/inference.py

Inference results will be saved to result/output/.

Evaluation and Visualization

python scripts/result.py

You can specify a date and variable at the end of result.py for visualization.

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 FengWu paper.