---
frameworks: PyTorch
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Precipitation Nowcasting
- Weather Forecast
- MRMS
tasks: []
datasets:
- OneScience/MRMS
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">NowcastNet_Earth</span>
  </strong>
</p>

# Model Introduction

NowcastNet is a large model for extreme-precipitation nowcasting proposed by a team from Tsinghua University. The research was published in the main edition of *Nature*.

Paper: Skilful nowcasting of extreme precipitation with NowcastNet

https://www.nature.com/articles/s41586-023-06184-4

# Model Description

NowcastNet combines data-driven deep learning with numerical methods based on physical equations in a unified framework. Two core networks work together to model precipitation processes at different spatial scales.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Short-term precipitation nowcasting training | Train NowcastNet with MRMS data. |
| Local quick validation | Use synthetic data to check data loading, model training and inference, and visualization of inference results. |
| ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. |
| Multi-GPU training | Use `torchrun` for data-parallel training across multiple GPUs or accelerators on one host. |

# 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/NowcastNet --local-dir ./NowcastNet
cd NowcastNet
```

### 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

Synthetic data is only used to check the data protocol and program flow; it does not represent real MRMS data or forecast quality:

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

### Training

Single GPU:

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

Multi-GPU:

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

Training weights are saved to `data/checkpoints/` by default.

### Training Weights

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

### Inference

Inference reads the training weights from `data/checkpoints/` by default:

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

### Evaluation and Visualization

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

# 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 reproduction of the original NowcastNet paper.