---
license: mit
task_categories:
- time-series-forecasting
tags:
- climate
- code
pretty_name: FAKER-Air
size_categories:
- 10B<n<100B
---

# FAKER-Air Dataset

This repository contains the dataset used in **FAKER-Air**, consisting of ground-truth air quality observations interpolated onto a grid and CMAQ reanalysis data tailored for East Asia.

- **Paper**: [Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization](https://www.arxiv.org/abs/2511.22169)
- **Code**: [GitHub Repository](https://github.com/kaist-cvml/FAKER-Air)

## Dataset Structure

The data is organized into two main directories inside `data/`:

### 1. Observations (`data/obs`)
Ground-truth station data interpolated onto the CMAQ 27km grid.
- **Format**: `.npz` (Compressed NumPy archives)
- **Naming**: `YYYYMMDDHH_obs.npz` (e.g., `2016010100_obs.npz`)
- **Content**: Contains arrays for pollutant concentrations (PM2.5, PM10, etc.) on the grid.
- **Total Files**: ~74,000 files (Hourly data from 2016 to 2023+).

### 2. CMAQ Reanalysis (`data/cmaq`)
Physics-based model outputs (Community Multiscale Air Quality).
- **Format**: `.npy` and `.json`
- **Structure**: `YYYY/MM/DD/NIER_27_01/`
- **Files**:
    - `*_x_conc.npy`: Concentration fields.
    - `*_x_metcro2d.npy`: 2D Meteorological fields.
    - `*_x_metcro3d.npy`: 3D Meteorological fields.
    - `*_meta.json`: Metadata.

## How to Use

You can download specific parts of the dataset using the `huggingface_hub` Python library.

### Prerequisites
```bash
pip install huggingface_hub numpy
````

### Download & Load Example

```python
from huggingface_hub import snapshot_download
import numpy as np
import os

# 1. Download the dataset (It will cache data locally)
# To download only specific years or folders, use `allow_patterns`.
local_dir = snapshot_download(
    repo_id="2na-97/FAKER-Air",
    repo_type="dataset",
    allow_patterns=[
        "data/obs/2023*.npz",       # Example: Only download OBS for 2023
        "data/cmaq/2023/**"         # Example: Only download CMAQ for 2023
    ]
)

print(f"Data downloaded to: {local_dir}")

# 2. Load an OBS file
obs_path = os.path.join(local_dir, "data/obs/2023010100_obs.npz")
if os.path.exists(obs_path):
    data = np.load(obs_path)
    print("Keys in OBS:", data.files)
    # Example access: data['pm25']

# 3. Load a CMAQ file
cmaq_path = os.path.join(local_dir, "data/cmaq/2023/01/01/NIER_27_01/20230101_x_conc.npy")
if os.path.exists(cmaq_path):
    cmaq_data = np.load(cmaq_path)
    print("CMAQ Shape:", cmaq_data.shape)
```

## Citation

```bibtex
@misc{kang2026realtimelonghorizonair,
      title={Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization}, 
      author={Inha Kang and Eunki Kim and Wonjeong Ryu and Jaeyo Shin and Seungjun Yu and Yoon-Hee Kang and Seongeun Jeong and Eunhye Kim and Soontae Kim and Hyunjung Shim},
      year={2026},
      eprint={2511.22169},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.22169}, 
}
```