| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - Earth Science |
| - Earth System Model |
| - Coupled Climate |
| - Long Simulation |
| - U-Net |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"><strong><span style="font-size: 30px;">DLESyM</span></strong></p> |
|
|
| # Model Introduction |
|
|
| DLESyM asynchronously couples deep-learning atmosphere and ocean modules for long free-running climate simulations and diagnostic precipitation. |
|
|
| Paper: A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate |
| https://arxiv.org/abs/2409.16247 |
|
|
| # Model Description |
|
|
| The model was proposed by an atmospheric-science and machine-learning research team. It was trained with 1983–2017 ERA5 fields, ISCCP OLR, and SST. Coupled DLWP, DLOM, and precipitation modules support current-climate simulation and internal-variability analysis. |
|
|
| # Use Cases |
|
|
| | Use Case | Description | |
| | :---: | :--- | |
| | Long climate simulation | Run stable atmosphere-ocean rollouts. | |
| | Climate variability | Analyze ENSO, monsoons, and annular modes. | |
| | Precipitation diagnosis | Diagnose accumulated precipitation from atmospheric states. | |
| | ModelScope/OneCode execution | Validate data, training, inference, climate metrics, and visualization. | |
| | Multi-GPU training | Start multi-process training through `torchrun`. | |
|
|
| # Usage Instructions |
|
|
| Use a GPU or DCU when available; CPU supports the default smoke configuration. |
|
|
| ```bash |
| hf download OneScience-Group/DLESyM --local-dir ./DLESyM |
| cd DLESyM |
| ``` |
|
|
| ### Environment Dependencies |
|
|
| **Hardware Requirements** |
|
|
| - A GPU or DCU is recommended. |
| - A CPU can be used for connectivity validation with the default small-sample configuration. |
| - DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first. |
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Activate DTK and Conda first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
|
|
| ```bash |
| # 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 |
| pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ```bash |
| python scripts/fake_data.py |
| python scripts/train.py |
| torchrun --standalone --nproc_per_node=2 scripts/train.py |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
| Training jointly optimizes atmosphere, ocean, and precipitation modules. Inference runs four coupled cycles and evaluation reports finite drift diagnostics. |
| ## Trained Weights |
| No weights are bundled under `weight/`. The authors provide configurations and weights at https://github.com/AtmosSci-DLESM/DLESyM. |
| # Citation and License |
| This repository is an independent engineering reproduction of the public DLESyM specifications. |
|
|
| The original preprint, official code, model weights, and related data remain subject to their respective licenses and terms. |
|
|
