frameworks: PyTorch
tasks: []
tags:
- OneScience
- Earth Science
- Weather Forecast
- ERA5
- Deterministic Forecast
language:
- zh
- en
license: apache-2.0
datasets:
- OneScience/ERA5
AIFS_Single_v1
Model Introduction
AIFS Single v1.1 is the deterministic version of the AI-powered weather forecasting system developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).
Paper: AIFS — ECMWF's data-driven forecasting system, arXiv:2406.01465
https://arxiv.org/abs/2406.01465
Model Description
AIFS is built on a Graph Neural Network (GNN), pre-trained on ERA5 data and fine-tuned with NWP operational analysis data.
Use Cases
| Scenario | Description |
|---|---|
| Weather Forecast Training | Train AIFS from scratch 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. |
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 is recommended.
Download the Model Package
modelscope download --model OneScience/AIFS_Single_v1 --local_dir ./AIFS_Single_v1
cd AIFS_Single_v1
Install the Runtime Environment
DCU
# 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
# 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
python scripts/train.py
Training weights are saved to weights/model_bak.ckpt, and the normalization file computed before training is saved to weights/era5_stats.npz.
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
python scripts/inference.py
The number of forecast steps is controlled by test_lead_time in conf/config.yaml (in hours; default 24 = 1 day).
Inference results will be saved to the output directory.
Evaluation and Visualization
python scripts/result.py
Computes ACC / RMSE metrics and generates plots. Metrics are saved to metrics/ and figures are saved to plots/.
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 AIFS Single v1.1 paper.