README.md

---
datasets:
- OneScience/ERA5
frameworks:
- PyTorch
language:
- en
- zh
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecast
- Regional Forecast
- Diffusion Model
- ERA5
- HRRR
tasks: []
---

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

# Model Introduction

StormCast is a generative regional weather forecasting model proposed by NVIDIA, targeting high-resolution nowcasting of mesoscale convective weather.

Paper: StormCast: A Machine Learning Method for Meso-β-Scale Convection-resolving Weather Forecasting

https://arxiv.org/abs/2408.10958

# Model Description

StormCast constrains the evolution of regional states with large-scale weather backgrounds, and uses a generative diffusion approach to supplement the fine-scale structures that deterministic forecasts struggle to represent.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Two-Stage Weather Forecast Training | Train a deterministic regression model and a conditional residual diffusion model in sequence. |
| Local Quick Validation | Use synthetic data to verify data loading, model training, inference, and inference result visualization. |
| ModelScope / OneCode Execution | Download as a standalone model package, install dependencies, and run scripts directly. |
| Multi-GPU Training | Launch multi-process training via `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**

- Training and inference require a GPU or DCU recognized by PyTorch; CPU can be used to generate synthetic data and verify configuration, but cannot run the current training and inference scripts.
- Multi-GPU training uses the NCCL backend. Please make sure the device driver, communication libraries, and PyTorch version are compatible.
- 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/StormCast --local-dir ./StormCast
cd StormCast
```

### 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 ERA5 data for training (due to file size limits, the current repository contains a slice of the full dataset). Users can download it with the command below and confirm that the data path in `conf/config.yaml` is set correctly:

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

### Generate Synthetic Data for Pipeline Validation

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

Synthetic data is only used to verify the data protocol and program flow; it does not represent the model's scientific forecasting capability.

### Training

Single GPU:

```bash
# Train the deterministic regression model; weights are saved to data/checkpoint/regression/model_bak.pt by default
python scripts/train.py --stage regression
# Train the residual diffusion model; weights are saved to data/checkpoint/diffusion/model_bak.pt by default
python scripts/train.py --stage diffusion
```

### Multi-GPU

```bash
# Train the deterministic regression model
torchrun --nproc_per_node=2 scripts/train.py --stage regression
# Train the residual diffusion model
torchrun --nproc_per_node=2 scripts/train.py --stage diffusion
```

### Training Weights

This repository provides weights trained on ERA5 reanalysis data in the `weights/` folder. The weight files will be uploaded soon and are expected to be available in the near future.

### Inference

Run autoregressive prediction with the default configuration and the two sets of weights saved during training:

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

### Evaluation and Visualization

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

Plots are saved to `outputs/inference/plots/` by default.

# 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

- This repository is a reproduction of the original StormCast paper.