metadata

license: mit
language:
  - en
  - zh
metrics:
  - accuracy
tags:
  - climate

FourCastNet: a global data-driven high-resolution weather model

This is a global data-driven high-resolution weather model implemented, trained and open sourced by High-Flyer AI. It is the first AI weather model, which can compare with the ECMWF Integrated Forecasting System (IFS).

Typhoon track prediction:

Water vapour prediction:

For more cases about FourCastNet prediction, please have a look at HF-Earth, a daily updated demo released by High-Flyer AI.

Inference

You can load the weights backbone.pt and precipitation.pt to generate weather predictions, as shown in the following pseudocode. The complete code is released at ./infer2img.py.

import xarray as xr
import cartopy.crs as ccrs
from afnonet import AFNONet   # download the code from https://github.com/HFAiLab/FourCastNet/blob/master/model/afnonet.py

backbone_model = AFNONet(img_size=[720, 1440], in_chans=20, out_chans=20, norm_layer=partial(nn.LayerNorm, eps=1e-6))
backbone_model.load('./backbone.pt')
precip_model = AFNONet(img_size=[720, 1440], in_chans=20, out_chans=1, norm_layer=partial(nn.LayerNorm, eps=1e-6))
precip_model.load('./precipitation.pt')

input_x = get_data('2023-01-01 00:00:00')

pred_x = backbone_model(input_x)    # input Xt, output Xt+1
pred_p = precip_model(pred_x)       # input Xt+1, output Pt+1

plot_data = xr.Dataset([pred_x, pred_p])
ax = plt.axes(projection=ccrs.PlateCarree())
plot_data.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, add_labels=False, rasterized=True)
ax.coastlines(resolution='110m')
plt.savefig('img.png')

FourCastNet can predict 7 surface variables, plus 5 atmospheric variables at each of 3 or 4 pressure levels, for 21 variables total. The details of these variables follow the paper.

Description of Files

backbone.pt

the weights of backbone model, 191MB, which is trained on 20 atmospheric variables from 1979-01 to 2022-12.

precipitation.pt

the weights of precipitation model, 187MB, which is trained on the variable total_precipitation from 1979-01 to 2022-12.

infer2img.py

Case code: load the above two weights to generate images of global weather prediction.