---
frameworks: PyTorch
tasks: []
tags:
  - OneScience
  - Earth Science
  - Weather Forecast
  - ERA5
  - Deterministic Forecast
language:
  - zh
  - en
license: apache-2.0
datasets:
  - OneScience/ERA5
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">AIFS_Single_v1</span>
  </strong>
</p>

# 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](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 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

```bash
modelscope download --model OneScience/AIFS_Single_v1 --local_dir ./AIFS_Single_v1
cd AIFS_Single_v1
```

### Install the Runtime Environment

**DCU**
```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**
```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
modelscope download --dataset OneScience/ERA5 --local_dir ./data
```

### Training

```bash
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

```bash
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

```bash
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.