README.md

---
frameworks: PyTorch
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Climate Simulation
- Probabilistic Forecasting
- FV3GFS
- Spherical DYffusion
tasks: []
datasets:
- FV3GFS
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">Spherical DYffusion</span>
  </strong>
</p>

# Model Introduction

Spherical DYffusion was proposed by Salva Ruhling Cachay and collaborators for probabilistic simulation of a global climate model.

Paper: Probabilistic Emulation of a Global Climate Model with Spherical DYffusion

https://arxiv.org/abs/2406.14798

# Model Description

The original method models spherical dynamics with an SFNO and uses the DYffusion interpolator and forecaster in a two-stage training procedure for probabilistic ensemble simulation. This repository contains a compact local implementation that preserves the project's tensor and data contracts for smoke testing; it is not a full paper-scale SFNO/DYffusion implementation.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Local pipeline validation | Use synthetic 37-channel global-grid data to check training, inference, and visualization. |
| FV3GFS protocol checks | Validate NetCDF variables, spatial dimensions, and consecutive time frames. |
| ModelScope / OneCode execution | Download the standalone model package and run the compact local pipeline. |
| 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**

- 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/Spherical_DYffusion --local-dir ./Spherical_DYffusion
cd Spherical_DYffusion
```

### 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
```

### Generate Synthetic Data

Generate a deterministic NetCDF FV3GFS-contract fixture at `data/data/synthetic_fv3gfs.nc`:

```bash
python scripts/fake_data.py
```

The fixture contains 37 protocol variables, including surface pressure and temperature, eight vertical levels of temperature, total water, and wind components, plus `DSWRFtoa`, `HGTsfc`, and `ocean_fraction`. It is intended only for protocol checks. The local training pipeline creates its own learnable `data/data/virtual_fv3gfs.npz` fixture.

### Training

Single GPU:

```bash
python scripts/train.py
```

Multi-GPU:

```bash
torchrun --nproc_per_node=8 scripts/train.py
```

Training starts from random initialization and saves `data/checkpoint/model_bak.pt` and `data/checkpoint/last.pt`.

The complete local smoke workflow can also be run with:

```bash
python scripts/local_pipeline.py all
```

### Training Weights

This repository provides a `weight/` directory for FV3GFS-compatible checkpoints. The weight files will be uploaded soon and are expected to be available in the near future.

### Inference

Inference reads `data/checkpoint/model_bak.pt` and writes `output/inference/prediction.npz`:

```bash
python scripts/inference.py
```

### Evaluation and Visualization

```bash
python scripts/result.py
```

The script computes per-variable and overall diagnostics and writes `output/visualization/diagnostic_dashboard.png` and `output/visualization/variable_metrics.png`, with machine-readable summaries under `output/metrics/`.

# 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 compact local reproduction of the original Spherical DYffusion paper and does not claim to reproduce the paper-scale training setup or metrics.