@article{ZHOU2026, 
author = {Kang ZHOU and Wei HAN and Ruoying YIN and Jinhua YU and Zhiqiu GAO},
title = {PCRF: An Independent Cloud Detection Framework for FY-4B GIIRS Hyperspectral Infrared Sounder Using PCA Reconstruction and Random Forest},
year = {2026},
journal = {Journal of Meteorological Research},
volume = {40},
number = {5},
pages = {1532-1549},
keywords = {hyperspectral infrared sounder, cloud detection, Fengyun-4B (FY-4B), Geostationary Interferometric Infrared Sounder (GIIRS), Principal Component Analysis (PCA), Random Forest (RF)},
url = {https://www.sciopen.com/article/10.1007/s13351-026-5274-7},
doi = {10.1007/s13351-026-5274-7},
abstract = {Accurate cloud detection is essential for the assimilation of hyperspectral infrared observations from the Geostationary Interferometric Infrared Sounder (GIIRS) aboard Fengyun-4B (FY-4B). This study introduces a novel, independent cloud detection framework termed PCRF, which integrates Principal Component Analysis (PCA)–based reconstruction of clear-sky spectra with an optimized random forest (RF) classifier. In contrast to operational methods that depend on external imager products [e.g., Advanced Geostationary Radiation Imager (AGRI)], PCRF exploits the intrinsic spectral characteristics of GIIRS longwave channels to identify cloud-contaminated pixels through reconstruction error analysis. Fourier-based spectral features are further incorporated to better represent cloud microphysical properties. When validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud masks, PCRF achieves overall accuracies of 91.3% over ocean and 86.7% over land, outperforming the operational AGRI cloud product by 2.1% and 6.6%, respectively. An independent validation using July 2024 data confirms the model’s temporal stability and spatial generalizability. Spatial comparisons reveal that PCRF delineates sharper cloud boundaries and more effectively detects fragmented clear-sky regions, particularly over complex surfaces. These results highlight PCRF’s potential as a robust cloud detection solution that can operate without external imager cloud products during routine application, offering direct benefits for numerical weather prediction (NWP) data assimilation and satellite product quality control.}
}