---
license: cc-by-4.0
task_categories:
  - other
tags:
  - cfd
  - openfoam
  - wind
  - terrain
  - atmospheric-boundary-layer
  - rans
  - structured-mesh
  - wind-energy
  - computational-fluid-dynamics
pretty_name: "Wind Flow Over Complex Terrain Dataset"
size_categories:
  - 1K<n<10K
---

# Wind Flow Over Complex Terrain Dataset

A large-scale dataset of steady-state RANS wind flow simulations over real-world complex terrain, designed for training machine learning models for wind resource assessment and atmospheric flow prediction.

## Overview

| Parameter | Value |
|---|---|
| Number of terrain locations | ~1000 |
| Wind directions per terrain | 2 (random) |
| Total simulation cases | ~10,000 |
| Cropped grid per case | ~298 × 298 × 64 |
| Horizontal resolution (AOI) | ~30 m |
| Vertical extent | ~500 m AGL |
| Flow variables | U (3-component), p, k, ε |
| Surface variables | DEM, roughness (z₀), height AGL |
| Reference wind speed | 10 m/s at 100 m height |
| Atmospheric stability | Neutral |
| Solver | OpenFOAM simpleFoam (RANS, k-ε) |
| Format | Zarr (xarray-compatible) |

## Quick Start

### Load a single case

```python
import xarray as xr

ds = xr.open_zarr("data/case_name.zarr")

# 3D wind field
Ux = ds['Ux'].values  # (ni, nj, nk) array, m/s
Uy = ds['Uy'].values
Uz = ds['Uz'].values

# Terrain
dem = ds['dem'].values        # (ni, nj), meters above sea level
z0 = ds['roughness'].values   # (ni, nj), aerodynamic roughness in meters

# Height above ground
h_agl = ds['h_agl'].values    # (ni, nj, nk), meters

# Metadata
print(ds.attrs['case_id'])
print(ds.attrs['rotation_deg'])   # wind direction
print(ds.attrs['converged'])      # simulation convergence flag
```

### Load from Hugging Face directly

```python
from huggingface_hub import hf_hub_download
import xarray as xr
import os

# Download a single case
local_path = hf_hub_download(
    repo_id="souravsud/wind-terrain-cfd",
    filename="data/case_name.zarr",
    repo_type="dataset",
    local_dir="./cache/"
)

ds = xr.open_zarr("./cache/data/case_name.zarr")
```

### PyTorch DataLoader

See `examples/02_dataloader_pytorch.py` for a ready-to-use `torch.utils.data.Dataset` class.

## Data Description

### Per-case Zarr store contents

| Variable | Shape | Units | Description |
|---|---|---|---|
| `X` | (ni, nj, nk) | m | UTM easting of cell centre |
| `Y` | (ni, nj, nk) | m | UTM northing of cell centre |
| `Z` | (ni, nj, nk) | m | Elevation above MSL |
| `Ux` | (ni, nj, nk) | m/s | Velocity x-component (UTM east) |
| `Uy` | (ni, nj, nk) | m/s | Velocity y-component (UTM north) |
| `Uz` | (ni, nj, nk) | m/s | Velocity z-component (vertical) |
| `p` | (ni, nj, nk) | m²/s² | Kinematic pressure (p/ρ) |
| `k` | (ni, nj, nk) | m²/s² | Turbulent kinetic energy |
| `epsilon` | (ni, nj, nk) | m²/s³ | Turbulent dissipation rate |
| `dem` | (ni, nj) | m | Ground elevation (MSL) |
| `roughness` | (ni, nj) | m | Aerodynamic roughness length z₀ |
| `h_agl` | (ni, nj, nk) | m | Height above ground level |

### Coordinate system

- **X, Y**: UTM coordinates. The EPSG code is stored in `ds.attrs['utm_epsg']`.
- **Z**: Absolute elevation above mean sea level (MSL), **not** height above ground.
- **h_agl**: Pre-computed height above ground: `h_agl[i,j,k] = Z[i,j,k] - dem[i,j]`.
- The mesh is **terrain-following** (curvilinear). At each (i,j) column, Z increases with k but follows the terrain surface. Horizontal coordinates vary slightly with k.

### Velocity scaling

All simulations use a **reference velocity of 10 m/s at 100 m height** under neutral atmospheric stability. Since the governing equations (incompressible RANS) are linear in velocity for neutral conditions, results can be scaled to any reference wind speed:

```python
V_ref_desired = 8.0  # m/s
scale = V_ref_desired / 10.0

U_scaled = U_dataset * scale
p_scaled = p_dataset * scale**2
k_scaled = k_dataset * scale**2
epsilon_scaled = epsilon_dataset * scale**3
```

This is a feature, not a limitation — it means the dataset effectively covers all wind speeds.

### Wind direction

Each case has a specific wind direction stored in `ds.attrs['rotation_deg']`. This is the angle (in degrees) by which the terrain was rotated to align the inlet boundary with the desired wind direction. The velocity components (Ux, Uy) are in the **rotated UTM frame** corresponding to that case.

### Convergence quality

Each case includes convergence information:
- `ds.attrs['converged']`: Boolean flag (True if all residuals < 10⁻³)
- `ds.attrs['residual_Ux']`, `ds.attrs['residual_p']`, etc.: Final residual per field
- `ds.attrs['iterations']`: Number of solver iterations

The `metadata/case_index.csv` file contains convergence data for all cases, allowing easy filtering.

## Dataset Structure

```
wind-terrain-cfd/
├── README.md                    # This file
├── data/
│   ├── case_0001.zarr/          # One Zarr store per simulation
│   ├── case_0002.zarr/
│   └── ...
├── metadata/
│   ├── case_index.csv           # Master index (lat, lon, wind_dir, converged, ...)
│   └── dataset_summary.json     # Aggregate statistics
└── examples/
    ├── 01_load_single_case.py
    ├── 02_dataloader_pytorch.py
    └── 03_velocity_scaling.py
```

## Generation Pipeline

The dataset was generated using an automated pipeline:

1. **Terrain fetching**: [terrain-fetcher](https://github.com/souravsud/terrain-fetcher) — downloads DEM (Copernicus GLO-30) and land cover (ESA WorldCover) data
2. **Mesh generation**: [terrain_following_mesh_generator](https://github.com/souravsud/terrain_following_mesh_generator) — structured terrain-following mesh for OpenFOAM
3. **Boundary conditions**: [ABL_BC_generator](https://github.com/souravsud/ABL_BC_generator) — neutral atmospheric boundary layer inlet profiles
4. **Job management**: [taskManager](https://github.com/souravsud/taskManager) — SLURM job submission and monitoring
5. **Orchestration**: [CFD-dataset](https://github.com/souravsud/CFD-dataset) — end-to-end pipeline coordination

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{sud2026windterrain,
  author = {Sud, Sourav},
  title = {Wind Flow Over Complex Terrain Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/souravsud/wind-terrain-cfd}
}
```

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.