---
license: apache-2.0
language:
- en
tags:
- OneScience
- Earth Science
- WeatherBench2
- Weather Benchmark
- Probabilistic Evaluation
frameworks: PyTorch
---

<p align="center"><strong><span style="font-size: 30px;">WeatherBench2</span></strong></p>

# Model Introduction

WeatherBench2 is an evaluation benchmark for the next generation of data-driven global weather models. It covers deterministic, ensemble-probabilistic, bias, and spectral diagnostics.

Paper: WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models  
https://arxiv.org/abs/2308.15560

# Model Description

The benchmark was proposed by teams from Google Research, Google DeepMind, and ECMWF. It uses 2020 global forecasts from ERA5, IFS, and multiple data-driven systems. It supports deterministic, probabilistic, bias, and spatial-scale evaluation of global forecasts from one to fourteen days.

# Use Cases

| Use Case | Description |
| :---: | :--- |
| Deterministic evaluation | Compute RMSE, ACC, bias, and SEEPS. |
| Probabilistic evaluation | Compute CRPS and spread-skill ratio. |
| Ensemble diagnosis | Compare ensemble means, spread, and skill. |
| ModelScope/OneCode execution | Validate data, training, inference, evaluation, and visualization. |
| Multi-GPU training | Validate a compact baseline through `torchrun`. |

# Usage Instructions

```bash
hf download OneScience-Group/WeatherBench2 --local-dir ./WeatherBench2
cd WeatherBench2
```

### 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
```

### Training Data

WeatherBench2 evaluates 2020 global forecasts from ERA5, IFS, and data-driven systems on a common 1.5-degree grid. Synthetic data retain eight headline variables and the ensemble dimension while reducing times and grid size.

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

### Training

For single-process training, use:

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

For multi-process training, use:

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

Training results are saved to:

```text
result/checkpoints/weatherbench2.pt
result/training/metrics.json
```

### Trained Weights

No weights are bundled under `weight/`. WeatherBench2 is a benchmark rather than a single pretrained model, so there is no unified official weight artifact.

### Inference

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

Inference generates an ensemble with shape `[8,8,8,24,48]`. Results are saved to:

```text
result/output/predictions.npz
```

### Evaluation and Visualization

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

Evaluation reports RMSE, CRPS, and spread-skill ratio and creates a spatial error map. Results are saved to:

```text
result/evaluation/metrics.json
result/evaluation/comparison.png
```

# 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 WeatherBench2 specifications.

The WeatherBench2 evaluation code, ERA5, IFS, and forecast data from participating systems remain subject to the licenses and data-use terms of their respective source projects.