fuxi

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
+
+
+## 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
+│   │   ├── input.nc
+│   │   ├── output.nc
+│   ├── model
+│   |   ├── buffer.st
+│   |   ├── fuxi_short.st
+│   |   ├── fuxi_medium.st
+│   |   ├── fuxi_long.st
+│   ├── fuxi.py
+│   ├── fuxi_demo.ipynb
+
+```
+
+1. Install PyTorch and CUDA. Go to the official PyTorch website (https://pytorch.org/) and follow the installation instructions for Linux to install PyTorch.
+
+
+2. Install xarray, netCDF4, and cartopy.
+
+```
+conda install xarray netCDF4 cartopy -c conda-forge
+```
+
+3. Finally, install any additional packages listed in the "requirement.txt" file using the following pip command:
+
+```
+pip install -r requirement.txt
+```
+
+
+## Demo
+
+ 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.
+
+
+### Input 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.
+
+

requirements.txt

+einops==0.6.1
+pandas==1.5.3
+matplotlib==3.7.1
+safetensors==0.3.1