---
license: other
language:
- en
- zh
tags:
- OneScience
- Earth Science
- Weather Forecasting
- Spherical Neural Operators
- ERA5
frameworks: PyTorch
datasets:
- OneScience/ERA5
---

<p align="center">
  <strong><span style="font-size: 30px;">Spherical Fourier Neural Operator</span></strong>
</p>

# Model Overview

SFNO learns dynamical system evolution on the sphere using spherical harmonic transforms, and can be applied to global weather forecasting and spherical shallow-water equation prediction.

Paper: *Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere*

https://proceedings.mlr.press/v202/bonev23a.html

# Model Description

This model package invokes NVIDIA's official `torch-harmonics` linear SFNO implementation and supports SHT on fake spherical fields, one parameter update step, checkpoint recovery, and short-term autoregressive rollout. It is an operator-level smoke package, not a reproduction of the paper's SWE/ERA5 experiments.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Spherical Operator Research | Verify SHT, spectral filtering, and inverse SHT. |
| Local Rapid Verification | Run through training and inference with fake spherical data. |
| ERA5 Weather Forecasting | Subsequently interface with 26- or 73-channel ERA5 data. |

# Usage

## 1. OneCode

[Click to experience intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation & Usage

**Hardware Requirements**

- CPU can run the current small configuration.
- GPU is recommended for full ERA5 training.

### Download the Model Package

```bash
hf download --model OneScience-Group/SFNO --local-dir ./SFNO
cd SFNO
```

### Set Up the Runtime Environment

**DCU Environment**

```bash
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
pip install torch-harmonics==0.8.0
```

**GPU Environment**

```bash
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
pip install torch-harmonics==0.8.0
```

This directory also retains `torch-harmonics==0.8.0` under `.deps/`.

### Data

The current scripts generate low-order smooth fake spherical fields in memory and split consecutive time frames into `T-1` input/target pairs; no external data download is required.

### Training

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

Training now performs multi-epoch pair Dataset training, time-sequential validation split, learning rate scheduling, and early stopping:

```bash
python scripts/train.py --epochs 10
python scripts/train.py --resume weight/training/latest.pth --epochs 20
```

### Inference

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

Output files:

```text
weight/model.pth
weight/training/latest.pth
weight/training/best.pth
weight/training/history.json
result/prediction.pt
result/target.pt
result/inference.json
```

### Result Inspection

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

The current test only verifies that the model executes. Randomly-initialized rollouts do not represent the paper's long-term stability results.

The result script generates `result/metrics.json` and `result/comparison.png`. The current RMSE does not incorporate spherical integration weights, and the ACC uses the sample's own spatial mean rather than a long-term training-set climatology; therefore the metrics are not comparable with those reported in the paper.

### Paper vs. Current Implementation I/O

| Item | Paper SWE / ERA5 | Current Smoke Configuration |
| --- | --- | --- |
| Input / Output | SWE 3 fields `256×512` / ERA5 26 or 73 channels | `[B,2,17,32]` smooth synthetic fields |
| Time Step | SWE 1 hour / ERA5 6 hours | Consecutive indices with no physical units |
| Architecture | SWE 4×256; weather model 8×384 | 2 blocks, embed dim 8 |
| Training | Single-step training followed by two-step autoregressive fine-tuning | Multi-epoch single-step pair training and validation; rollout used for inference analysis |
| Analysis | Spherically weighted relative error and climatological ACC | Unweighted smoke RMSE/ACC |

The complete execution flow is `train.py -> inference.py -> result.py`. Training generates z-scored `[T,C,Nlat,Nlon]` in memory and forms pairs from consecutive frames. The checkpoint `config` stores only the model configuration, while training parameters are stored separately under `train_config`, allowing inference to reconstruct the model architecture directly from the checkpoint. The model package is distributed without local training weights or `result/` artifacts. A production SWE/ERA5 mode further requires data loading, variable tables, formal train/validation splits, area-weighted loss, and the paper's two-stage training loop.

### Real Data

Real-data training requires ERA5 26/73-channel data, 6-hour temporal pairing, training-set statistics, and spherical grid resampling configuration.

# OneScience Official 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 & License

- Official Implementation: https://github.com/NVIDIA/torch-harmonics
- This directory is an independent runnable adaptation of SFNO; see `THIRD_PARTY.md` for third-party terms.