README.md

## FuXi


This is the official repository for the FuXi paper.

[FuXi: A cascade machine learning forecasting system for 15-day global weather forecast
](https://arxiv.org/abs/2306.12873)

by Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Yuan Qi, Hao Li


## What's New
  - Release of the ONNX model and inference code.
  - Addition of new sample data (20210101).



## Installation

The Google Drive folder contains the FuXi model, code, and sample input data, all of which are essential resources for this study. Access to these resources is restricted by a password-protected link. To obtain the password, users must complete the provided [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSfjwZLf6PmxRvRhIPMQ1WRLJ98iLxOq_0dXb87N8CFNPyYAGg/viewform?usp=sharing). For inquiries regarding the password, please contact Professor Li Hao at the email address: lihao_lh@fudan.edu.cn.

The downloaded files shall be organized as the following hierarchy:

```plain
├── root
│   ├── data
│   │    ├── 20180101
│   │    │       ├── input.nc
│   │    │       ├── output.nc
│   │    │       
│   │    ├── 20210101
│   │            ├── input.nc
│   │            ├── output.nc
│   │            ├── target.nc
│   │   
│   ├── model
│   |   ├── buffer.st
│   |   ├── fuxi_short.st
│   |   ├── fuxi_medium.st
│   |   ├── fuxi_long.st
│   |   ├── onnx
│   |        ├── short
│   |        ├── short.onnx
│   |        ├── medium
│   |        ├── medium.onnx
│   |        ├── long
│   |        ├── long.onnx
|   |   
│   ├── fuxi.py
│   ├── fuxi_demo.ipynb
│   ├── infernece_fuxi.py

```

1. Install xarray.

```
 conda install -c conda-forge xarray dask netCDF4 bottleneck
```

2. Install onnxruntime

```
pip install -r requirement.txt
```

3. (Optional) Install PyTorch and CUDA for inference with `pth` model


## Demo

### Inferece with onnx (recommend)

```python 
python inference_fuxi.py --model model/onnx --input data/20210101/input.nc
```

### Inferece with pytorch  

 The `fuxi_demo.ipynb` consists of multiple sections: 
  1. Construct the Fuxi model. 
  2. Load weights and buffers. 
  3. Load the preprocessed input. 
  4. Run inference for 15-day forecasting. 
  5. Save the results. 
  6. Visualization.


## Data preparation 

The `input.nc` file contains preprocessed data from the origin ERA5 files. The file has a shape of (2, 70, 721, 1440), where the first dimension represents two time steps. The second dimension represents all variable and level combinations, named in the following exact order:

```plain
'Z50', 'Z100', 'Z150', 'Z200', 'Z250', 'Z300', 'Z400', 'Z500', 'Z600', 'Z700', 'Z850', 'Z925', 'Z1000', 
'T50', 'T100', 'T150', 'T200', 'T250', 'T300', 'T400', 'T500', 'T600', 'T700', 'T850', 'T925', 'T1000', 
'U50', 'U100', 'U150', 'U200', 'U250', 'U300', 'U400', 'U500', 'U600', 'U700', 'U850', 'U925', 'U1000', 
'V50', 'V100', 'V150', 'V200', 'V250', 'V300', 'V400', 'V500', 'V600', 'V700', 'V850', 'V925', 'V1000', 
'R50', 'R100', 'R150', 'R200', 'R250', 'R300', 'R400', 'R500', 'R600', 'R700', 'R850', 'R925', 'R1000', 
'T2M', 'U10', 'V10', 'MSL', 'TP'
```

The last five variables ('T2M', 'U10', 'V10', 'MSL', 'TP') are surface variables, while the remaining variables represent atmosphere variables with numbers representing pressure levels.


**_NOTE:_**

- The variable 'Z' represents geopotential and not geopotential height.
- The variable 'TP' represents total precipitation accumulated over a period of 6 hours.