README.md

---
frameworks: PyTorch
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecast
- Subseasonal Forecast
- Coupled Atmosphere-Ocean-Land
- ERA5
tasks: []
datasets:
- OneScience/ERA5
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">FengWu-W2S</span>
  </strong>
</p>

# Model Introduction

FengWu-W2S (FengWu Weather-to-Subseasonal) is a seamless global weather-to-subseasonal forecasting model extending FengWu.

Paper: FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

https://arxiv.org/abs/2411.10191

# Model Description

The model uses a six-hour time step for autoregressive forecasts of up to 42 days. Coupled atmospheric, ocean, and land branches, together with diversity perturbations, are used to improve extended-range forecast skill. This repository contains a compact implementation of the coupled interfaces for reproducible workflow checks.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Weather-to-subseasonal forecast research | Train a 78-channel coupled model with six-hourly global atmospheric, ocean, and land fields. |
| Local quick validation | Use small-grid synthetic HDF5 data to check training, fine-tuning, inference, and forecast 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**

- 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/FengWu-W2S --local-dir ./FengWu-W2S
cd FengWu-W2S
```

### 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 data path in `conf/config.yaml` is correct:

```bash
hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data
```

Real files must contain all 78 channels listed in `conf/config.yaml`, including atmospheric, ocean, and land variables, with six-hour time spacing. The configured group indices define the coupled branches.

### Generate Synthetic Data

The default configuration describes a full `721x1440` grid. For a practical local smoke test, generate a small fixture explicitly:

```bash
python scripts/fake_data.py --height 32 --width 64 --timesteps 12
```

Synthetic HDF5 files are for workflow validation only and do not represent the ERA5 reanalysis or the paper's forecast quality.

### Training

Single GPU:

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

Multi-GPU:

```bash
torchrun --nproc_per_node=2 scripts/train.py
```

For a short smoke run, reduce the workload while keeping the generated data and model grid consistent:

```bash
python scripts/train.py --max-epoch 1 --batch-size 1 --num-workers 0 --rollout-steps 1
```

The default checkpoint is saved to `data/checkpoints/model_bak.pth`.

### Fine-tuning

Resume from the base checkpoint with the lower fine-tuning learning rate:

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

### Training Weights

This repository provides a `weight/` directory for FengWu-W2S 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 and writes forecasts grouped by initialization year to `result/output/<year>/`, together with `result/output/index.json`:

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

Use `--steps` and `--limit` to bound a local smoke test; `--stochastic` enables perturbation sampling.

### Evaluation and Visualization

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

The script computes per-channel RMSE, normalized RMSE, and anomaly ACC, and generates forecast-comparison, lead-time skill, channel-ranking, and training-loss 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

- Paper: https://arxiv.org/abs/2411.10191
- This directory is an independent reproduction of the paper method and does not represent official code, weights, or training results released by the authors.