| --- |
| frameworks: PyTorch |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - OneScience |
| - Earth Science |
| - Climate Simulation |
| - Global Atmospheric State Simulation |
| - SFNO |
| - FV3GFS |
| tasks: [] |
| datasets: |
| - FV3GFS |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">Ai2_Climate_Emulator</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| The AI2 Climate Emulator (ACE) is a global atmospheric state emulator proposed by the Allen Institute for AI (AI2). |
|
|
| Paper: ACE: A fast, scalable foundation model for the atmosphere |
|
|
| https://arxiv.org/abs/2310.02074 |
|
|
| # Model Description |
|
|
| This project implements the spherical Fourier neural operator (SFNO) forward graph with PyTorch and `torch_harmonics`. It takes the atmospheric state and external forcings at the current six-hour time step as input, predicts the state at the next time step, and can generate multi-step climate or weather fields autoregressively. |
|
|
| # Use Cases |
|
|
| | Scenario | Description | |
| | :---: | :--- | |
| | Global atmospheric state simulation | Train a one-step ACE model with FV3GFS data following the 40/44-channel protocol. | |
| | Local quick validation | Generate synthetic NPZ files with `scripts/fake_data.py` to check the training, inference, and result-visualization pipeline. | |
| | ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. | |
| | Multi-GPU training | Launch PyTorch DDP with `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](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, or the OneScience recommended version matching your cluster, is recommended. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/Ai2_Climate_Emulator --local-dir ./Ai2_Climate_Emulator |
| cd Ai2_Climate_Emulator |
| ``` |
|
|
| ### 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 ACE paper uses an ensemble of 11 FV3GFS initial conditions: 10 members for training and one member for validation. The simulations are written at six-hour intervals and regridded to a Gaussian latitude-longitude grid. The original FV3GFS files and NOAA `fregrid` are not included in this model package; users must prepare and convert them to the NPZ format required by the project: |
|
|
| ```text |
| inputs: [N, 40, H, W] |
| targets: [N, 44, H, W] |
| ``` |
|
|
| When real data is unavailable, generate synthetic data for pipeline validation: |
|
|
| ```bash |
| python scripts/fake_data.py |
| ``` |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| Multi-GPU: |
|
|
| ```bash |
| torchrun --nproc_per_node=8 scripts/train.py |
| ``` |
|
|
| Training checkpoints are written to `data/checkpoint/model_bak.pt` by default. |
|
|
| ### Training Weights |
|
|
| This repository provides weights trained on FV3GFS data in the `weight/` folder. The weight files will be uploaded soon and are expected to be available in the near future. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| Inference results are saved to `output/infer/rollout.npz` by default. |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| Area-weighted RMSE, global mean bias, and PNG figures are written to `output/pic/` by default. |
|
|
| # Official OneScience Resources |
|
|
| | 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 and License |
|
|
| - This repository is a reproduction of the ACE model. |
|
|
