Wang et al. (2025) Generalized Variational Retrieval of Full Field-of-View Cloud Fraction and Precipitable Water Vapor from FY-4A/GIIRS Observations

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-11
• Authors: Gen Wang, Song Ye, Bing Xu, Xiefei Zhi, Qiao Liu, Yang Liu, Yue Pan, Chuanyu Fan, Tiening Zhang, Feng Xie
• DOI: 10.3390/rs17223687

Research Groups

• Satellite remote sensing and atmospheric science research groups
• Numerical weather prediction research groups

Short Summary

This study proposes a generalized variational retrieval framework to estimate full field-of-view (FOV) cloud fraction and precipitable water vapor from Fengyun-4A/Geostationary Interferometric Infrared Sounder (FY-4A/GIIRS) observations, demonstrating improved brightness temperature simulations in cloudy regions and better indication of high-impact weather events.

Objective

• To develop and evaluate a generalized variational retrieval framework for effectively utilizing full field-of-view (FOV) observations from satellite infrared sounders to estimate cloud fraction and precipitable water vapor (PWV) for high-impact weather applications.

Study Configuration

• Spatial Scale: Full field-of-view (FOV) observations from a geostationary satellite (FY-4A/GIIRS), covering regional to continental scales.
• Temporal Scale: High-frequency observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020).

Methodology and Data

• Models used:
  • Generalized variational retrieval framework
  • Radiative transfer model
  • Minimum Residual Method (MRM)
• Data sources:
  • Geostationary Interferometric Infrared Sounder (GIIRS) observations from Fengyun-4A (FY-4A) satellite.
  • ERA5 Total Column Water Vapour (TCWV) data (for comparison).

Main Results

• A three-step channel selection strategy based on information entropy was designed for FY-4A/GIIRS.
• A constrained generalized variational retrieval method, coupled with a cloud cost function, was established.
• Incorporating cloud parameters as auxiliary inputs to the radiative transfer model improved the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduced brightness temperature biases.
• Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information showed closer agreement.
• The derived PWV, at certain FOVs, more effectively indicated the occurrence of high-impact weather events.

Contributions

• Proposes a novel generalized variational retrieval framework for estimating full FOV cloud fraction and precipitable water vapor from hyperspectral infrared sounder data (FY-4A/GIIRS).
• Introduces a three-step channel selection strategy based on information entropy specifically for FY-4A/GIIRS.
• Demonstrates significant improvements in brightness temperature simulations and bias reduction in cloud-affected regions by integrating retrieved cloud parameters.
• Provides PWV data that more effectively indicates high-impact weather events compared to existing reanalysis products.
• Establishes a robust basis for future assimilation and operational utilization of infrared data in numerical weather prediction models over cloud-affected regions.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Wang2025Generalized,
  author = {Wang, Gen and Ye, Song and Xu, Bing and Zhi, Xiefei and Liu, Qiao and Liu, Yang and Pan, Yue and Fan, Chuanyu and Zhang, Tiening and Xie, Feng},
  title = {Generalized Variational Retrieval of Full Field-of-View Cloud Fraction and Precipitable Water Vapor from FY-4A/GIIRS Observations},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17223687},
  url = {https://doi.org/10.3390/rs17223687}
}