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One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

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Abstract

A physical retrieval approach based on the one-dimensional variational (1D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4A GIIRS (geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center (NSMC) during the periods from December 2019 to January 2020 (winter) and from July 2020 to August 2020 (summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error (RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there. Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg−1. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 hPa and 300 hPa both in summer and winter in terms of RMSE.

摘要

本文基于FY-4A的干涉式大气垂直探测仪(GIIRS)红外高光谱资料,利用一维变分原理(1D-Var)的物理反演算法同时反演了晴空和部分云条件下的大气温度和湿度廓线。利用中国区域探空站点的无线电探空观测资料和国家卫星气象中心(NSMC)发布的L2级业务产品,验证了2019年12月至2020年1月(冬天)和2020年7月至2020年8月(夏天)温度和湿度廓线的反演精度。结果表明:与探空资料相比,晴空条件下,对流层各垂直气压层上(近地面除外),一维变分算法反演的温度均方根误差(RMSE)值在2 K以内。由于高层大气缺乏温度探测通道,平流层反演的温度RMSE值略有增加。部分云条件下,一维变分算法的反演精度优于L2级业务产品,而且利用一维变分反演算法可以反演整层大气的温度廓线,而L2级业务产品仅反演云顶以上高度的温度廓线。对于湿度廓线,无论冬季还是夏季,对流层整层平均的湿度RMSE值均在2 g kg-1内。与ERA5再分析资料相比,无论是冬季还是夏季,一维变分算法的反演精度在800-300 hPa气压层高度范围内都有所改进。

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Acknowledgements

The authors would like to thank the editor and reviewers for their helpful comments on the manuscript. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC1507302, in part by the National Natural Science Foundation of China under Grant 41975028.

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Authors and Affiliations

  1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Qiumeng Xue & Li Guan

  2. Department of Mathematics, University of Wisconsin-Madison, Madison, 53706, USA

    Xiaoning Shi

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  1. Qiumeng Xue
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  2. Li Guan
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  3. Xiaoning Shi
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Correspondence to Li Guan.

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Article Highlights

• The 1D-Var physical retrieval algorithm is utilized to retrieve the atmospheric profiles under both clear-sky and partly cloudy conditions.

• The 1D-Var-retrieved atmospheric profiles can be produced at each vertical level while the NSMC level-2 operational products obtain temperature values only at altitudes above the cloud top and no humidity retrievals.

• The accuracy of the 1D-Var-retrieved temperature profiles is greatly improved compared with the accuracies of the NSMC operational level-2 products.

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Xue, Q., Guan, L. & Shi, X. One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS. Adv. Atmos. Sci. 39, 471–486 (2022). https://doi.org/10.1007/s00376-021-1032-z

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  • DOI: https://doi.org/10.1007/s00376-021-1032-z

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