| --- |
| license: mit |
| task_categories: |
| - image-to-image |
| tags: |
| - climate |
| - downscaling |
| - super-resolution |
| - weather |
| - meteorology |
| - deep-learning |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Climate Downscaling Dataset (S2S) |
|
|
| A dataset for climate data spatial downscaling from low resolution (16×16) to high resolution (64×64). |
|
|
| ## Dataset Description |
|
|
| This dataset contains climate variables for training deep learning models to perform statistical downscaling. The data is preprocessed and ready for use with PyTorch-based frameworks. |
|
|
| ### Data Format |
|
|
| The dataset consists of PyTorch tensor files (`.pt`) with the following structure: |
|
|
| | Key | Shape | Description | |
| |-----|-------|-------------| |
| | `LR_input` | `[C, T, 16, 16]` | Low-resolution input (C=7 channels, T=time steps) | |
| | `HR_target` | `[C, T, 64, 64]` | High-resolution target | |
| | `HR_topo` | `[2, 64, 64]` | Topographic data (elevation and slope) | |
|
|
| ### Files |
|
|
| | File | Size | Description | |
| |------|------|-------------| |
| | `dict_s2s_train.pt` | 8.6 GB | Training set | |
| | `dict_s2s_test.pt` | 1.8 GB | Test set | |
| | `dict_s2s_val.pt` | 873 MB | Validation set | |
| | `HR_topo.nc` | 58 KB | Topographic data (NetCDF format) | |
|
|
| ### Input Channels |
|
|
| The 7 input channels in `LR_input` typically include: |
| - Temperature (t2m) |
| - Geopotential height |
| - U-wind component |
| - V-wind component |
| - Relative humidity |
| - Surface pressure |
| - Other meteorological variables |
|
|
| ### Scale Factor |
|
|
| - **Input resolution**: 16×16 |
| - **Output resolution**: 64×64 |
| - **Scale factor**: 4x |
|
|
| ## Usage |
|
|
| ### Loading the Data |
|
|
| ```python |
| import torch |
| |
| # Load training data |
| train_data = torch.load('dict_s2s_train.pt') |
| |
| lr_input = train_data['LR_input'] # [C, T, 16, 16] |
| hr_target = train_data['HR_target'] # [C, T, 64, 64] |
| hr_topo = train_data['HR_topo'] # [2, 64, 64] |
| |
| # Transpose to [T, C, H, W] for batch processing |
| lr_input = lr_input.permute(1, 0, 2, 3) |
| hr_target = hr_target.permute(1, 0, 2, 3) |
| |
| print(f"Number of samples: {lr_input.shape[0]}") |
| print(f"Input shape: {lr_input.shape}") |
| print(f"Target shape: {hr_target.shape}") |
| ``` |
|
|
| ### With Hugging Face Datasets |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| # Download specific file |
| train_path = hf_hub_download( |
| repo_id="YOUR_USERNAME/climate-downscaling-s2s", |
| filename="dict_s2s_train.pt", |
| repo_type="dataset" |
| ) |
| |
| train_data = torch.load(train_path) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @dataset{climate_downscaling_s2s, |
| title={Climate Downscaling Dataset for Deep Learning}, |
| year={2025}, |
| url={https://huggingface.co/datasets/YOUR_USERNAME/climate-downscaling-s2s} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the MIT License. |
|
|
