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

Fund Project:

1.National Key Research and Development Program of China under Grant 2018YFC15073022.National Natural Science Foundation of China under Grant 41975028


doi:  10.1007/s00376-021-1032-z

  • 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 slightly increases in the higher atmospheric layers, owing to the lack of temperature sounding channels. 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. With respect to the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g/kg. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 hPa and 300 hPa both in summer and winter in the RMSE sense.
  • [1] Myoung-Hwan AHN, Mee-Ja KIM, Chu-Yong CHUNG, Ae-Sook SUH, 2003: Operational Implementation of the ATOVS Processing Procedure in KMA and Its Validation, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 398-414.  doi: 10.1007/BF02690798
    [2] Peipei YU, Chunxiang SHI, Ling YANG, Shuai SHAN, 2020: A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 735-750.  doi: 10.1007/s00376-020-9249-9
    [3] YAO Zhigang, CHEN Hongbin, LIN Longfu, 2005: Retrieving Atmospheric Temperature Profiles from AMSU-A Data with Neural Networks, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 606-616.  doi: 10.1007/BF02918492
    [4] Yan Shaojin, Peng Yongqing, Wang Jianzhong, 1991: Determination of Kolmogorov Entropy of Chaotic Attractor Included in One-Dimensional Time Series of Meteorological Data, ADVANCES IN ATMOSPHERIC SCIENCES, 8, 243-250.  doi: 10.1007/BF02658098
    [5] ZHANG Lei, QIU Chongjian, HUANG Jianping, 2008: A Three-Dimensional Satellite Retrieval Method for Atmospheric Temperature and Moisture Profiles, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 897-904.  doi: 10.1007/s00376-008-0897-4
    [6] Xinrong WU, Shaoqing ZHANG, Zhengyu LIU, 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 193-207.  doi: 10.1007/s00376-015-5099-2
    [7] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2015: Use of Incremental Analysis Updates in 4D-Var Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1575-1582.  doi: 10.1007/s00376-015-5041-7
    [8] Kyu Rang KIM, Tae Heon KWON, Yeon-Hee KIM, Hae-Jung KOO, Byoung-Cheol CHOI, Chee-Young CHOI, 2009: Restoration of an Inner-City Stream and Its Impact on Air Temperature and Humidity Based on Long-Term Monitoring Data, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 283-292.  doi: 10.1007/s00376-009-0283-x
    [9] BIAN Jianchun, CHEN Hongbin, Holger VOMEL, DUAN Yunjun, XUAN Yuejian, LU Daren, 2011: Intercomparison of Humidity and Temperature Sensors: GTS1, Vaisala RS80, and CFH, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 139-146.  doi: 10.1007/s00376-010-9170-8
    [10] Dohyeong KIM, Myoung-Hwan AHN, Minjin CHOI, 2015: Inter-comparison of the Infrared Channels of the Meteorological Imager Onboard COMS and Hyperspectral IASI Data, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 0-.  doi: 10.1007/s00376-014-4124-1
    [11] Zhu Keyun, 2001: On the 4D Variational Data Assimilation with Constraint Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1131-1145.  doi: 10.1007/s00376-001-0028-y
    [12] Ji jinjun, 1989: Atmosphere-Ocean Coupling Schemes in a One-Dimensional Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 275-288.  doi: 10.1007/BF02661534
    [13] Fuqing ZHANG, Meng ZHANG, James A. HANSEN, 2009: Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1-8.  doi: 10.1007/s00376-009-0001-8
    [14] WANG Bin, LIU Juanjuan, WANG Shudong, CHENG Wei, LIU Juan, LIU Chengsi, Qingnong XIAO, Ying-Hwa KUO, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 715-727.  doi: 10.1007/s00376-009-9122-3
    [15] ZHANG Jie, Zhenglong LI, Jun LI, Jinglong LI, 2014: Ensemble Retrieval of Atmospheric Temperature Profiles from AIRS, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 559-569.  doi: 10.1007/s00376-013-3094-z
    [16] Liu Changsheng, 1988: REMOTE SENSING OF TEMPERATURE PROFILES IN THE BOUNDARY LAYER, ADVANCES IN ATMOSPHERIC SCIENCES, 5, 67-74.  doi: 10.1007/BF02657346
    [17] ZENG Zhihua, DUAN Yihong, LIANG Xudong, MA Leiming, Johnny Chung-leung CHAN, 2005: The Effect of Three-Dimensional Variational Data Assimilation of QuikSCAT Data on the Numerical Simulation of Typhoon Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 534-544.  doi: 10.1007/BF02918486
    [18] Wei Chong, Xue Yongkang, Zhu Xiaoming, Zou Shouxiang, 1984: DETERMINATION OF ATMOSPHERIC PRECIPITABLE WATER AND HUMIDITY PROFILES BY A GROUND-BASED 1.35 cm RADIOMETER, ADVANCES IN ATMOSPHERIC SCIENCES, 1, 119-139.  doi: 10.1007/BF03187623
    [19] JIN Ling, Fanyou KONG, LEI Hengchi*, and HU Zhaoxia, 2014: A Methodological Study on Using Weather Research and Forecasting (WRF) Model Outputs to Drive a One-Dimensional Cloud Model, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 230-240.  doi: 10.1007/s00376-013-2257-2
    [20] Wang Guiqin, 1990: Simulation of the Influence of Ion-Produced NOX and HOX Radicals on the Antarctic Ozone Depletion with a One-Dimensional Model, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 98-103.  doi: 10.1007/BF02919172

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Manuscript History

Manuscript received: 17 January 2021
Manuscript revised: 09 September 2021
Manuscript accepted: 11 October 2021
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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

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 slightly increases in the higher atmospheric layers, owing to the lack of temperature sounding channels. 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. With respect to the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g/kg. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 hPa and 300 hPa both in summer and winter in the RMSE sense.

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