This is the official repository for the FuXi-S2S paper.
A machine learning model that outperforms conventional global subseasonal forecast models
by Lei Chen, Xiaohui Zhong, Jie Wu, Deliang Chen, Shangping Xie, Qingchen Chao, Chensen Lin, Zixin Hu, Bo Lu, Hao Li, Yuan Qi
The FuXi-S2S model and sample input data used in this study are now publicly available via Zenodo:
The downloaded files shall be organized as the following hierarchy:
├── root
│ ├── data
│ │ ├── mask.nc
│ │ ├── sample
│ │ ├── geopotential.nc
│ │ ├── temperature.nc
│ │ ├── ......
│ │ ├── total_precipitation.nc
| |
│ ├── model
│ | ├── fuxi_s2s.onnx
| |
│ ├── inference.py
│ ├── data_util.py
- Install xarray
conda install -c conda-forge xarray dask netCDF4 bottleneck- Install pytorch
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidiapython inference.py \
--model model/fuxi_s2s.onnx \
--input data/input.nc \
--total_step 42 \
--total_member 11 \
--save_dir output;The input.nc file contains preprocessed data from the origin ERA5 files. The file has a shape of (2, 76, 121, 240), where the first dimension represents two time steps. The second dimension represents all variable and level combinations, named in the following exact order:
['z1000', 'z925', 'z850', 'z700', 'z600', 'z500', 'z400', 'z300',
'z250', 'z200', 'z150', 'z100', 'z50', 't1000', 't925', 't850',
't700', 't600', 't500', 't400', 't300', 't250', 't200', 't150',
't100', 't50', 'u1000', 'u925', 'u850', 'u700', 'u600', 'u500',
'u400', 'u300', 'u250', 'u200', 'u150', 'u100', 'u50', 'v1000',
'v925', 'v850', 'v700', 'v600', 'v500', 'v400', 'v300', 'v250',
'v200', 'v150', 'v100', 'v50', 'q1000', 'q925', 'q850', 'q700',
'q600', 'q500', 'q400', 'q300', 'q250', 'q200', 'q150', 'q100',
'q50', 't2m', 'd2m', 'sst', 'ttr', '10u', '10v', '100u', '100v',
'msl', 'tcwv', 'tp']The last 11 variables: ('t2m', 'd2m', 'sst', 'ttr', '10u', '10v', '100u', '100v', 'msl', 'tcwv', 'tp') are surface variables, while the remaining variables represent atmosphere variables with numbers representing pressure levels.