README.md

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
• Code: GitHub Repository

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

pip install huggingface_hub numpy

Download & Load Example

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

@article{kang2026fakerair,
  title={Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization},
  author={Kang, Inha and others},
  journal={arXiv preprint arXiv:2511.22169},
  year={2026}
}