This is an official implementation of the paper Hybrid Neural Representations for Spherical Data (HNeR-S) accepted at ICML 2024.
Dataset preparation code (src/datasets/generation/download.py
) is originally from NNCompression github.
Before, running the below code, one shoudl fill out the KEY
value in download.py
. The value can be obtained by following the process at CDS API website.
Run the following codes to download weather datasets.
# Resolution 0.25
python src/datasets/generation/download.py --variable=geopotential --mode=single --level_type=pressure --years=2000 --resolution=0.25 --month=01 --day=01 --time=00:00 --pressure_level=500 --custom_fn=data.nc --output_dir=dataset/spatial_0_25/era5_geopotential
python src/datasets/generation/download.py --variable=temperature --mode=single --level_type=pressure --years=2000 --resolution=0.25 --month=01 --day=01 --time=00:00 --pressure_level=500 --custom_fn=data.nc --output_dir=dataset/spatial_0_25/era5_temperature
Run the following codes to download weather datasets.
# Geopotential
python src/datasets/generation/download.py --variable=geopotential --mode=separate --level_type=pressure --years=2000 --resolution=1.00 --pressure_level=500 --custom_fn=data.nc --output_dir=dataset/temporal/era5_geopotential
# Temperature
python src/datasets/generation/download.py --variable=temperature --mode=separate --level_type=pressure --years=2000 --resolution=1.00 --pressure_level=500 --custom_fn=data.nc --output_dir=dataset/temporal/era5_temperature
- dataset_dir (str)
- downscale_factor (int)
- seed (int)
- model (str)
[healpix, equirect]
# Model : HEALPix
# Task : Spatial super resolution
# Dataset : Geopotential
# Downscale factor : x2
# Seed : 0
CUDA_VISIBLE_DEVICES=4 python src/main_superres.py \
--dataset_dir dataset/spatial_0_25/era5_geopotential \
--downscale_factor 2 \
--seed 0 \
--n_levels 9 \
--n_features_per_level 2 \
--input_dim 2 \
--batch_size 4096 \
--model healpix \
--normalize \
--skip