OneScience-Group/OneForecast

<p align="center"> <strong> <span style="font-size: 30px;">OneForecast</span> </strong> </p>

Model Introduction

OneForecast was developed by the team of Prof. Xiaomeng Huang in the Department of Earth System Science at Tsinghua University, in collaboration with multiple institutions. The paper has been accepted by ICML 2025.

Paper: OneForecast: A Universal Framework for Global and Regional Weather Forecasting

https://arxiv.org/abs/2502.00338

Model Description

OneForecast is a universal framework for nested global-regional weather forecasting based on graph neural networks (GNNs). Its core goal is to address the challenges of existing AI weather models in balancing low-resolution global forecasts with high-resolution regional forecasts, as well as issues such as over-smoothing in extreme event forecasting.

Use Cases

Usage Guide

1. OneCode Usage

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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

hf download OneScience-Group/OneForecast --local-dir ./OneForecast
cd OneForecast

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:

hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data

Generate Synthetic Data

Synthetic data is only used to verify the data protocol and program flow; it does not represent scientific forecast quality:

python scripts/fake_data.py

Training

Single GPU:

python scripts/train.py

Training starts from random initialization by default and saves the model to data/checkpoint/model_bak.tar.

Multi-GPU:

torchrun --nproc-per-node=4 scripts/train.py

Fine-tuning

Fine-tuning starts from the training checkpoint data/checkpoint/model_bak.tar by default and saves the result to data/checkpoint/model_finetuned.tar:

python scripts/finetune.py

Training Weights

This repository provides weights trained on 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

Inference loads the training checkpoint data/checkpoint/model_bak.tar by default, uses the test-year data, and writes predictions to outputs/predictions/:

python scripts/inference.py --config conf/config.yaml

Result Visualization

python scripts/result.py

OneScience Official Information

Citation & License

• —This repository is a reproduction of the original OneForecast paper.