Dr. Ruoying Yin Profile

Dr. Ruoying Yin

at China Meteorological Administration

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Proceedings Article | 20 November 2024 Poster + Paper

Optimal cloud-clearing radiances for GIIRS onboard FengYun-4A satellite

Xinya Gong, Jun Li, Zhenglong Li, Ruoying Yin, Wei Han

Proceedings Volume 13193, 131930N (2024) https://doi.org/10.1117/12.3031422

KEYWORDS: Hyperspectral sensing, Weather forecasting, Clouds, Infrared imaging, Imaging systems, Satellites, Bias correction, Meteorological satellites, Infrared radiation, Spatial resolution

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Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's geostationary meteorological satellite of the Fengyun-4A satellite provides high-spectral-resolution infrared observations with high temporal resolution. Due to high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantages of thermodynamic information from GIIRS in all sky. Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias corrected optimal cloud-cleared (BCOCC) method is introduced to generate the GIIRS cloud-cleared radiances (CCRs) with the help of the collocated clear radiances of AGRI. A bias correction (BC) scheme is applied by adjusting radiance differences between AGRI and GIIRS based on field-of-view and scene temperature. This correction ensures radiometric consistency across multiple bands and minimizes errors in CCR generation. Comparing to the optimal cloud-cleared method without BC, BCOCC increases the data yields of successful GIIRS CCRs by three times and improves the accuracy of retrieved CCRs. Evaluations show the mean biases of CCRs are 0.09K, -0.06K, and 0.06K, when compared to AGRI IR bands B12, B13 and B14, respectively. The GIIRS CCRs can be assimilated as clear radiances in numerical weather prediction (NWP) models, leading to enhanced forecast accuracy, particularly in regions of cloud edges and broken clouds, such as during typhoon or hurricane formation. Future work will involve integrating these CCRs into operational NWP systems and quantifying the improvement in forecast skill, particularly for severe weather events.

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