| --- |
| frameworks: PyTorch |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - OneScience |
| - Earth Science |
| - Weather Forecast |
| - Global Weather Forecast |
| - ERA5 |
| - Neural ODE |
| tasks: [] |
| datasets: |
| - OneScience/ERA5 |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">ClimODE</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| ClimODE is a weather forecasting model proposed in 2024 by researchers from Aalto University and collaborating institutions. |
|
|
| Paper: ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs |
|
|
| https://arxiv.org/abs/2404.10024 |
|
|
| # Model Description |
|
|
| ClimODE is a physics-informed neural ordinary differential equation model for global, monthly-scale, and regional climate and weather forecasting. It represents atmospheric evolution as a continuous-time dynamical system and incorporates a transport-based physical inductive bias into the neural ODE. |
|
|
| # Use Cases |
|
|
| | Scenario | Description | |
| | :---: | :--- | |
| | Global weather forecasting | Train or evaluate ClimODE on ERA5 data following this project's five-variable protocol. | |
| | Local quick validation | Use synthetic ERA5 HDF5 data to check data loading, training, inference, evaluation, and visualization. | |
| | ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. | |
| | Multi-GPU training | Launch PyTorch DistributedDataParallel 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** |
|
|
| - Training and inference require a GPU or DCU recognized by PyTorch. CPU can be used to generate synthetic data and inspect configuration, but cannot run the current training and inference scripts. |
| - Multi-GPU training uses the NCCL backend. Ensure that the device driver, communication libraries, and PyTorch version are compatible. |
| - 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/ClimODE --local-dir ./ClimODE |
| cd ClimODE |
| ``` |
|
|
| ### 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 OneScience community provides an ERA5 data slice for training. Download it and confirm that the paths in `conf/config.yaml` point to the downloaded data: |
|
|
| ```bash |
| hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data |
| ``` |
|
|
| ClimODE reads the five variables `z`, `t`, `t2m`, `u10`, and `v10`, regridding the source `721x1440` fields to the model's `32x64` grid. The source variable mapping is defined in `conf/config.yaml`. |
|
|
| ### Generate Synthetic Data |
|
|
| When real ERA5 data is unavailable, generate a full-resolution synthetic fixture for pipeline validation: |
|
|
| ```bash |
| python scripts/fake_data.py |
| ``` |
|
|
| Synthetic data does not represent ERA5 and cannot reproduce the paper's metrics. |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| Multi-GPU: |
|
|
| ```bash |
| torchrun --nproc_per_node=8 scripts/train.py |
| ``` |
|
|
| The default checkpoint is saved to `data/checkpoints/model_bak.pth`. |
|
|
| ### Fine-tuning |
|
|
| To fine-tune from an existing checkpoint, pass an explicit checkpoint and mode: |
|
|
| ```bash |
| python scripts/train.py --mode finetune --checkpoint data/checkpoints/model_bak.pth |
| ``` |
|
|
| An official pretrained checkpoint can be selected explicitly with `--use-pretrained --pretrained-checkpoint <path>`. |
|
|
| ### Training Weights |
|
|
| This repository provides a `weight/` directory for ClimODE checkpoints. The weight files will be uploaded soon and are expected to be available in the near future. |
|
|
| ### Inference |
|
|
| Inference reads `data/checkpoints/model_bak.pth` by default. If it is unavailable, pass `--checkpoint` explicitly: |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| Predictions, uncertainty estimates, and targets are written to `result/output/`. |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| The script computes latitude-weighted RMSE, ACC, and CRPS, and writes per-variable figures to `result/output/figures/`. |
|
|
| # 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 OneScience adaptation of the ClimODE paper and is not the official Aalto-QuML release. |
|
|
