All‐sky <scp>FY4B</scp> ‐GIIRS infrared hyperspectral satellite data assimilation in the <scp>CMA‐GFS</scp> 4DVar system. Part I: Implementation

Abstract Although significant advancements have been made in all‐sky microwave assimilation, the extension to all‐sky assimilation of infrared hyperspectral satellite data in a global forecast system remains a key challenge, limited by several factors, including uncertainties in forward operators, strong interchannel correlation in cloudy regions, and the substantial background bias in hydrometeors. This two‐part study focuses on developing all‐sky infrared hyperspectral data assimilation capability for the China Meteorology Administration Global Forecast System (CMA‐GFS) four‐dimensional variational (4DVar) system. We aim to mitigate non‐Gaussian effects in first‐guess departures and to accurately extract water vapor information from the observation minus background () values. Specifically, we select 50 water vapor channels from the FY4B geostationary interferometric infrared sounder for assimilation. An iterative constrained bias correction method is developed to address biases per channel and per detector. To promote a Gaussian distribution of under all‐sky conditions, we introduce an asymmetric cloud proxy observation error model to tackle the issue of asymmetrical sampling bias. Additionally, we implement a statistically based background hydrometeor bias‐correction (cloud BC) method to mitigate the systematic bias resulting from the insufficient representation of clouds in the CMA‐GFS model, thereby enhancing the accuracy of the extracted water vapor information. Furthermore, a scene‐dependent inflation scheme for observation errors is developed to effectively address strong interchannel correlations in all‐sky conditions. Collectively, these procedures contribute to a more Gaussian distribution with reduced hydrometeor bias. Remarkably, nearly 60% of all‐sky observations pass the QC threshold of 3 K, a rate significantly higher than the 3.6% observed for clear‐sky cases. Part II of this study provides a comprehensive assessment of the assimilation effects within the CMA‐GFS 4DVar system using the methods described herein.

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Publication Details

Journal
Quarterly Journal of the Royal Meteorological Society
Published
2026-04-24
DOI
https://doi.org/10.1002/qj.70185
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.00

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article

All‐sky FY4B ‐GIIRS infrared hyperspectral satellite data assimilation in the CMA‐GFS 4DVar system. Part I: Implementation

Senyi Kong, Wei Han, Lei Bi, Ruoying Yin
Quarterly Journal of the Royal Meteorological Society
Meteorological Phenomena and Simulations
article

All‐sky FY4B ‐GIIRS infrared hyperspectral satellite data assimilation in the CMA‐GFS 4DVar system. Part I: Implementation

Senyi Kong, Wei Han, Lei Bi, Ruoying Yin
article en

Abstract

Abstract Although significant advancements have been made in all‐sky microwave assimilation, the extension to all‐sky assimilation of infrared hyperspectral satellite data in a global forecast system remains a key challenge, limited by several factors, including uncertainties in forward operators, strong interchannel correlation in cloudy regions, and the substantial background bias in hydrometeors. This two‐part study focuses on developing all‐sky infrared hyperspectral data assimilation capability for the China Meteorology Administration Global Forecast System (CMA‐GFS) four‐dimensional variational (4DVar) system. We aim to mitigate non‐Gaussian effects in first‐guess departures and to accurately extract water vapor information from the observation minus background () values. Specifically, we select 50 water vapor channels from the FY4B geostationary interferometric infrared sounder for assimilation. An iterative constrained bias correction method is developed to address biases per channel and per detector. To promote a Gaussian distribution of under all‐sky conditions, we introduce an asymmetric cloud proxy observation error model to tackle the issue of asymmetrical sampling bias. Additionally, we implement a statistically based background hydrometeor bias‐correction (cloud BC) method to mitigate the systematic bias resulting from the insufficient representation of clouds in the CMA‐GFS model, thereby enhancing the accuracy of the extracted water vapor information. Furthermore, a scene‐dependent inflation scheme for observation errors is developed to effectively address strong interchannel correlations in all‐sky conditions. Collectively, these procedures contribute to a more Gaussian distribution with reduced hydrometeor bias. Remarkably, nearly 60% of all‐sky observations pass the QC threshold of 3 K, a rate significantly higher than the 3.6% observed for clear‐sky cases. Part II of this study provides a comprehensive assessment of the assimilation effects within the CMA‐GFS 4DVar system using the methods described herein.

Quarterly Journal of the Royal Meteorological Society
China Meteorological Administration (CN), Chinese Academy of Meteorological Sciences (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 5%
Meteorological Phenomena and Simulations
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