Supervised Fine-Tuning (SFT) with Temporal Accumulation Loss to reduce exposure bias during autoregressive rollout.
Group-Relative Policy Optimization (GRPO) with AQI-aware rewards and curriculum rollout to improve operational reliability, especially by reducing false alarms.

The model is designed for long-horizon particulate matter forecasting up to 120 hours (5 days) ahead using real-time observations and regional CMAQ reanalysis for East Asia.

Intended Use

This model is intended for:

Research on long-horizon air-quality forecasting
Spatiotemporal forecasting over East Asia
Reliability-aware forecasting and AQI-oriented evaluation
Offline experiments and prototype operational systems

Primary Outputs

The model is mainly used to forecast:

PM2.5
PM10

These continuous predictions can also be mapped to AQI-style discrete categories such as Good, Moderate, Bad, and VeryBad.

Out-of-Scope Use

This model is not intended for:

Direct public warning issuance without local validation and expert review
Deployment outside the East Asia domain used in the paper
Use with input data that does not match the official preprocessing and directory structure
Clinical, legal, or regulatory decision-making without independent verification

Training Data

FAKER-Air is trained on the CMAQ-OBS Regional Air Quality Dataset for East Asia.

The released dataset combines:

Observation fields under data/obs
CMAQ reanalysis fields under data/cmaq

The dataset is built for East Asia at 27 km spatial resolution and 6-hour temporal intervals, combining grid-aligned observation fields with region-specific CMAQ reanalysis.

Model Inputs and Outputs

Inputs

The official pipeline expects preprocessed inputs matching the repository format.

In practice, inference uses:

Gridded OBS inputs (.npz)
CMAQ concentration fields (*_x_conc.npy)
CMAQ 2D meteorological fields (*_x_metcro2d.npy)
CMAQ 3D meteorological fields (*_x_metcro3d.npy)

Please follow the preprocessing and directory structure in the official repository before running evaluation or inference.

Outputs

The model predicts long-horizon particulate matter fields over the East Asia forecasting domain, especially:

PM2.5 concentration fields
PM10 concentration fields

These outputs can be further converted into AQI-style classes for operational analysis.

Training Procedure

The official training recipe uses a two-stage setup:

Stage 1: SFT with Temporal Accumulation Loss
Stage 2: GRPO with class-wise AQI rewards and curriculum rollout

This design is intended to combine strong spatiotemporal forecasting performance with improved operational reliability.

Evaluation

The paper evaluates long-horizon forecasting up to 120 hours ahead.

Selected Reported Results for PM2.5 at 120h

Binary F1: 56.72
False Alarm Rate (FAR): 17.32
4-class Accuracy: 45.16
4-class F1-macro: 41.90

Compared with the SFT baseline, the GRPO model is reported to reduce PM2.5 FAR from 32.86 to 17.32, while maintaining competitive F1.
For PM10, the paper reports a reduction in FAR from 18.44 to 10.81 in the 120h setting.

For full results, please refer to the paper and supplementary material.

Limitations

This checkpoint is region-specific and is designed for East Asia, not for global forecasting.
Performance depends on using the same preprocessing, variables, grid definition, and rollout setup as the official codebase.
The model is optimized for reliability-aware forecasting, so some operating points may trade raw point-wise accuracy for lower false alarm rates.
Performance may degrade under severe domain shift, missing local inputs, or deployment in regions outside the training domain.

How to Use

1) Download the checkpoint

python

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="2na-97/FAKER-Air",
    filename="FAKER-Air_GRPO",  # change this if you rename the checkpoint file
)

print(ckpt_path)

2) Prepare the dataset

Dataset:

2na-97/FAKER-Air

Code:

https://github.com/kaist-cvml/FAKER-Air/tree/main

Follow the repository instructions to place OBS and CMAQ files in the expected directories.

3) Run rollout evaluation / inference

bash

CUDA_VISIBLE_DEVICES=0 \
torchrun \
  --nproc_per_node=1 \
  --master_addr="127.0.0.1" \
  --master_port=29502 \
  test.py --batch 1 \
  --model aurora \
  --test-start-date 2023-01-01 \
  --test-end-date 2023-12-31 \
  --data-sources obs,cmaq \
  --checkpoint-path /path/to/FAKER-Air_GRPO \
  --npz-path ./data/obs_npz_27km \
  --cmaq-root ./data/cmaq_only_npy \
  --mode rollout \
  --rollout-hours 120 \
  --use_cmaq_pm_only

Repository Links

Code: https://github.com/kaist-cvml/FAKER-Air/tree/main
Dataset: https://huggingface.co/datasets/2na-97/FAKER-Air
Model: https://huggingface.co/2na-97/FAKER-Air

Citation

bibtex

@article{kang2026fakerair,
  title={Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization},
  author={Kang, Inha and Kim, Eunki and Ryu, Wonjeong and Shin, Jaeyo and Yu, Seungjun and Kang, Yoon-Hee and Jeong, Seongeun and Kim, Eunhye and Kim, Soontae and Shim, Hyunjung},
  journal={arXiv preprint arXiv:2511.22169},
  year={2026}
}
``