---
license: cc-by-4.0
task_categories:
  - other
task_ids: []
tags:
  - climate
  - downscaling
  - super-resolution
  - weather
  - geospatial
  - zarr
pretty_name: AORC Texas Climate Downscaling Benchmark
size_categories:
  - 10B<n<100B
language: []
multilinguality: monolingual
---

# AORC Texas Climate Downscaling Benchmark

A benchmark dataset for statistical climate downscaling (super-resolution) based on NOAA AORC 1 km reanalysis data over Texas (2021–2024). The dataset supports training and evaluating models that reconstruct high-resolution (1 km) meteorological fields from coarsened low-resolution inputs at five scaling factors (2×, 4×, 8×, 16×, 32×).

## Dataset Description

| Field | Value |
|---|---|
| **Source** | NOAA Analysis of Record for Calibration (AORC) v1.1 |
| **Spatial domain** | Texas (two sub-regions: SW and NE), 1 km grid |
| **Temporal range** | 2021–2024, hourly |
| **Patch size** | 512 × 512 pixels at 1 km resolution |
| **Variables** | 2 m air temperature, near-surface specific humidity, precipitation rate |
| **Format** | Zarr v2, float32, chunks (32, 512, 512) |
| **Total size** | ~43 GB |

## Variables

| Short name | Long name | Physical unit | Normalization |
|---|---|---|---|
| `temp` | 2 m air temperature | K | z-score: `(T − 290.58) / 10.01` |
| `humidity` | Near-surface specific humidity | kg kg⁻¹ | z-score: `(q − 6.58×10⁻³) / 4.16×10⁻³` |
| `precip` | Precipitation rate | mm h⁻¹ | log1p then z-score: `(log1p(p) − 0.01637) / 0.1322` |

Normalization statistics were computed from the **training split** only and are stored in `stats.json`.  
Values stored in the zarr arrays are **already normalized** (approximately zero-mean, unit-variance).

To recover physical values:
```python
import numpy as np, json, zarr

with open("stats.json") as f:
    stats = json.load(f)

z = zarr.open("processed/train/temp.zarr", "r")
y_norm = z[0]  # (512, 512), normalized

# Temperature (K)
temp_K = y_norm * stats["temp"]["std"] + stats["temp"]["mean"]

# Humidity (kg/kg)
hum = y_norm * stats["humidity"]["std"] + stats["humidity"]["mean"]

# Precipitation (mm/h) — undo z-score then undo log1p
precip_log = y_norm * stats["precip"]["std"] + stats["precip"]["mean"]
precip_mmh = np.expm1(precip_log)
```

## Degradation Operator

Low-resolution inputs are generated from the high-resolution targets using a **Gaussian blur → strided subsampling → bicubic upsample** operator:

```python
from scipy.ndimage import gaussian_filter, zoom

def degrade(y_hr, scale):
    """y_hr: (512,512) normalized HR field → (512,512) degraded LR-upsampled field."""
    sigma  = 0.5 * scale
    y_blur = gaussian_filter(y_hr, sigma=sigma)
    x_lr   = y_blur[::scale, ::scale]          # (512//scale, 512//scale)
    x_up   = zoom(x_lr, scale, order=3)[:512, :512]  # bicubic upsample back to 512×512
    return x_up
```

Supported scaling factors: **2×, 4×, 8×, 16×, 32×**.

## Splits

| Split | Region | Years | Frames (per variable) | Purpose |
|---|---|---|---|---|
| `train` | SW Texas | 2021–2022 | ~29 334 | Training |
| `val` | SW Texas | 2023 | 8 760 | Validation / model selection |
| `test_temporal` | SW Texas | 2024 | 8 784 | Temporal generalization |
| `test_spatial` | NE Texas | 2021–2022 | 17 520 | Spatial generalization |
| `test_ood` | NE Texas | 2024 | 8 784 | Spatio-temporal OOD |

The train/val/test_temporal splits share the SW Texas domain (in-distribution spatially) while test_spatial and test_ood use an unseen NE Texas domain. test_ood is the hardest split: unseen region **and** unseen year.

## Directory Structure

```
processed/
├── stats.json                  ← normalization statistics (training split)
├── train/
│   ├── temp.zarr/              ← (29334, 512, 512) float32 z-score normalized
│   ├── precip.zarr/            ← (29451, 512, 512) float32 log1p + z-score normalized
│   └── humidity.zarr/          ← (29316, 512, 512) float32 z-score normalized
├── val/
│   └── ...                     ← same structure, (8760, 512, 512)
├── test_temporal/
│   └── ...                     ← (8784, 512, 512)
├── test_spatial/
│   └── ...                     ← (17520, 512, 512)
└── test_ood/
    └── ...                     ← (8784, 512, 512)
```

## Usage Example

```python
import zarr, numpy as np
from scipy.ndimage import gaussian_filter, zoom

# Load a batch of HR temperature patches from training split
z = zarr.open("processed/train/temp.zarr", mode="r")
y_batch = z[:32]  # (32, 512, 512) normalized

# Generate 8× LR input on the fly
scale = 8
y_lr_batch = np.stack([
    zoom(gaussian_filter(y, 0.5 * scale)[::scale, ::scale], scale, order=3)[:512, :512]
    for y in y_batch
])  # (32, 512, 512) — bicubic upsampled LR
```

## Responsible AI

**Intended use:** Climate downscaling research benchmark. Designed for training and evaluating statistical downscaling / super-resolution models for meteorological fields.

**Limitations:**
- Geographic scope limited to **Texas, USA**; models may not generalize to other regions.
- Temporal scope **2021–2024** only; does not include extreme historical events before this window.
- Based on **reanalysis** (model-derived gridded product), not direct observations; inherits AORC v1.1 biases.
- Spatial resolution is 1 km; sub-kilometer dynamics are not represented.

**Sensitive attributes:** None. No personally identifiable information; purely gridded geophysical fields.

**Prohibited uses:** None formally. We discourage use as a sole basis for operational weather forecasting or safety-critical decisions without independent validation.

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{ovanger2025aorc,
  author    = {Ovanger, Oscar},
  title     = {{AORC Texas Climate Downscaling Benchmark}},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/oovanger/aorc-texas-downscaling}
}
```

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)