---
title: A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation
canonical_url: "https://www.modelscope.cn/models/ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation"
md_url: "https://www.modelscope.cn/models/ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation.md"
repository: ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation
chinese_name: "用于卫星观测资料同化的通用深度学习框架"
last_updated: 2025-09-22
license: apache-2.0
downloads: 71
stars: 19
---

# A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation

> A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation - ai4s 在 ModelScope 开源的模型。SCRIPT A General Deep Learning Framework for Satellite Data Assimilation

ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation
- **License**: apache-2.0
- **Downloads**: 71
- **Stars**: 19
- **Last updated**: 2025-09-22

Source: https://www.modelscope.cn/models/ai4s/A_General_Deep_Learning_Framework_for_Satellite_Data_Assimilation

---

# SCRIPT
A General Deep Learning Framework for Satellite Data Assimilation

# Overview
Data assimilation is an indispensable component of modern Numerical Weather Prediction (NWP) systems, playing a crucial role in generating analysis fields that significantly affect forecast performance. However, building an efficient DA system faces major challenges, particularly in establishing complex relationships between the background field and massive multi-source observational data within limited operational time windows. Here, we present FuXi-DA — a general deep learning–based data assimilation framework designed for assimilating satellite observations. By assimilating data from the Advanced Geosynchronous Radiation Imager (AGRI) aboard Fengyun-4B, FuXi-DA consistently reduces analysis errors and significantly improves forecast performance. Moreover, FuXi-DA has been validated against atmospheric physical theories, demonstrating its consistency and reliability.

# Installation
We recommend installing SCRIPT in a virtual environment. For Conda, use the following command to create and activate a dedicated environment:
```bash
conda create -n script python=3.8 && source activate script
```
Next, install the required dependencies:
```
torch, xarray, dask, netCDF4, zarr
```

# Tutorial
Run SCRIPT with a single command
To run SCRIPT with one command, use:
```bash
cd fuxi-da/code_inference
bash run_inference.sh
```
The assimilation results are saved in `fuxi-da/output`.

# reference
```
@article{xu2025fuxi,
  title={Fuxi-da: A generalized deep learning data assimilation framework for assimilating satellite observations},
  author={Xu, Xiaoze and Sun, Xiuyu and Han, Wei and Zhong, Xiaohui and Chen, Lei and Gao, Zhiqiu and Li, Hao},
  journal={npj Climate and Atmospheric Science},
  volume={8},
  number={1},
  pages={156},
  year={2025},
  publisher={Nature Publishing Group UK London}
}
```