--- license: apache-2.0 language: - en tags: - OneScience - Earth Science - Seasonal Prediction - ACE2 - Ensemble Forecasting - NAO frameworks: PyTorch ---

ACE2-Seasonal

# Model Introduction ACE2-Seasonal reproduces the global seasonal hindcast experiment using ACE2-ERA5. Six-hour autoregression and persistent SST and sea-ice anomalies form a 64-member lagged ensemble. Paper: Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data https://doi.org/10.1038/s41612-025-01198-3 # Model Description The study was conducted by teams at the Met Office, University of Exeter, and Ai2. ACE2 was trained with ERA5 atmospheric reanalysis, while seasonal experiments use ERA5 initial states and persistent ocean-boundary anomalies. It supports global DJF prediction and NAO, ENSO-teleconnection, and ensemble spread-skill analysis. # Use Cases | Use Case | Description | | :---: | :--- | | Seasonal ensembles | Construct a 64-member lagged ensemble. | | NAO prediction | Evaluate ensemble-mean NAO correlation and spread. | | Boundary forcing | Persist initialized SST and sea-ice anomalies. | | ModelScope/OneCode execution | Validate structured data, training, inference, seasonal metrics, and visualization. | | Multi-GPU training | Start multi-process training through `torchrun`. | # Usage Instructions ```bash hf download OneScience-Group/ACE2-Seasonal --local-dir ./ACE2-Seasonal cd ACE2-Seasonal ``` ### 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 --nproc_per_node=2 --nnodes=1 --master_addr="localhost" --master_port=29500 scripts/train.py python scripts/inference.py python scripts/result.py ``` Synthetic data retain the global-grid protocol, boundary variables, six-hour order, and 64-member ensemble. The six-hour state-transition target completes backpropagation and parameter updates in verified single-process and two-process DDP runs. Inference restores the checkpoint, performs 12 engineering forecast steps, and produces a finite `[64,12,8,16,16]` ensemble while retaining the incomplete-coverage marker. Training results are saved to `result/checkpoints/ace2_seasonal.pt`; ensemble predictions and seasonal metrics are saved under `result/output/` and `result/evaluation/`. # Official OneScience 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 and License This repository is an independent engineering reproduction of the public ACE2-Seasonal specifications. The original paper is licensed under CC BY 4.0; the paper, ACE2 code, weights, and ERA5 data remain subject to their respective licenses and terms.