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Volume 29 Issue 2

Mar.  2012

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2012 >  29(2): 263-273

Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

  • 1.

    School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-747, Korea, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA;School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-!747, Korea;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA


doi: 10.1007/s00376-011-1119-z

  • This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.
  • [1] 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
    [2] Pucai WANG, N. F. ELANSKY, Yu. M. TIMOFEEV, Gengchen WANG, G. S. GOLITSYN, M. V. MAKAROVA, V. S. RAKITIN, Yu. SHTABKIN, A. I. SKOROKHOD, E. I. GRECHKO, E.V. FOKEEVA, A. N. SAFRONOV, Liang RAN, Ting WANG, 2018: Long-Term Trends of Carbon Monoxide Total Columnar Amount in Urban Areas and Background Regions: Ground- and Satellite-based Spectroscopic Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 785-795.  doi: 10.1007/s00376-017-6327-8
    [3] Bozhen LI, Gen ZHANG, Lingjun XIA, Ping KONG, Mingjin ZHAN, Rui SU, 2020: Spatial and Temporal Distributions of Atmospheric CO2 in East China Based on Data from Three Satellites, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1323-1337.  doi: 10.1007/s00376-020-0123-6
    [4] ZHENG Jing, Jun LI, Timothy J. SCHMIT, Jinlong LI, Zhiquan LIU, 2015: The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike (2008) and Irene (2011), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 319-335.  doi: 10.1007/s00376-014-3162-z
    [5] WU Xuebao, LI Jun, ZHANG Wenjian, WANG Fang, 2005: Atmospheric Profile Retrieval with AIRS Data and Validation at the ARM CART Site, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 647-654.  doi: 10.1007/BF02918708
    [6] JIANG Yuan, LIU Liping, 2014: A Test Pattern Identification Algorithm and Its Application to CINRAD/SA(B) Data, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 331-343.  doi: 10.1007/s00376-013-2315-9
    [7] Fang Yuan, Zijiang Zhou, LIAO Jie, 2024: A New method for deriving the high-vertical-resolution Wind Vector data from L-band radar sounding system in China, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3163-5
    [8] LIAO Jie, WANG Bin, LI Qingxiang, 2014: A New Method for Quality Control of Chinese Rawinsonde Wind Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1293-1304.  doi: 10.1007/s00376-014-4030-6
    [9] Yanni Qu, Mitchell D. Goldberg, Murty Divakarla, 2001: Ozone Profile Retrieval from Satellite Observation Using High Spectral Resolution Infrared Sounding Instrument, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 959-971.
    [10] YAO Zhigang, Jun LI, ZHAO Zengliang, 2015: Synergistic Use of AIRS and MODIS for Dust Top Height Retrieval over Land, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 470-476.  doi: 10.1007/s00376-014-4046-y
    [11] Chengcheng NI, Guoping LI, Xiaozhen XIONG, 2017: Analysis of a Vortex Precipitation Event over Southwest China Using AIRS and In Situ Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 559-570.  doi: 10.1007/s00376-016-5262-4
    [12] MA Shuqing, CHEN Hongbin, WANG Gai, PAN Yi, LI Qiang, 2004: A Miniature Robotic Plane Meteorological Sounding System, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 890-896.  doi: 10.1007/BF02915591
    [13] Li Jun, Huang Hung-Lung, 1994: Optimal Use of High Resolution Infrared Sounder Channels in Atmospheric Profile Retrieval, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 271-276.  doi: 10.1007/BF02658145
    [14] YAO Zhigang, Jun LI, Jinlong LI, 2012: Sunglint Impact on Atmospheric Soundings from Hyperspectral Resolution Infrared Radiances, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 455-463.  doi: 10.1007/s00376-011-1013-8
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    • Manuscript History

    Manuscript received: 10 March 2012
    Manuscript revised: 10 March 2012
    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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    Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

    • 1. School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-747, Korea, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA;School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-!747, Korea;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA
    • Manuscript received: 2012-03-10
    • Manuscript revised: 2012-03-10

    Abstract: This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.

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