OneScience-Group/NowcastNet_Earth

NowcastNet_Earth

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

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

hf download OneScience-Group/NowcastNet --local-dir ./NowcastNet
cd NowcastNet

Install the Runtime Environment

DCU Environment

# 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

# 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:

python scripts/fake_data.py

Training

Single GPU:

python scripts/train.py

Multi-GPU:

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:

python scripts/inference.py

Evaluation and Visualization

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.