The Validation of AIRS Retrievals of Integrated Precipitable Water Vapor Using Measurements from a Network of Ground-Based GPS Receivers over the Contiguous United States

M. K. Rama Varma Raja I. M. Systems Group, Inc., Kensington, Maryland

Search for other papers by M. K. Rama Varma Raja in
Current site
Google Scholar
PubMed
Close
,
Seth I. Gutman NOAA/Earth System Research Laboratory/Global Systems Division, Boulder, Colorado

Search for other papers by Seth I. Gutman in
Current site
Google Scholar
PubMed
Close
,
James G. Yoe NOAA/NESDIS/ORA, World Weather Building, Camp Springs, Maryland

Search for other papers by James G. Yoe in
Current site
Google Scholar
PubMed
Close
,
Larry M. McMillin NOAA/NESDIS/ORA, World Weather Building, Camp Springs, Maryland

Search for other papers by Larry M. McMillin in
Current site
Google Scholar
PubMed
Close
, and
Jiang Zhao QSS Group, Inc., Lanham, Maryland

Search for other papers by Jiang Zhao in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A robust and easily implemented verification procedure based on the column-integrated precipitable water (IPW) vapor estimates derived from a network of ground-based global positioning system (GPS) receivers has been used to assess the quality of the Atmospheric Infrared Sounder (AIRS) IPW retrievals over the contiguous United States. For a period of six months from April to October 2004, excellent agreement has been realized between GPS-derived IPW estimates and those determined from AIRS, showing small monthly bias values ranging from 0.5 to 1.5 mm and root-mean-square (rms) differences of 4 mm or less. When the spatial (latitude–longitude) window for the GPS and AIRS matchup observations is reduced from the initial ½° by ½° to ¼° by ¼°, the rms differences are reduced. Analysis revealed that the observed IPW biases between the instruments are strongly correlated to the reported surface pressure differences between the GPS and AIRS observational points. Adjusting the AIRS IPW values to account for the surface pressure discrepancies resulted in significant reductions of the bias between GPS and AIRS. A similar reduction can be obtained by comparing only (GPS–AIRS) match-up pairs for which the corresponding surface pressure differences are 0.5 mb or less. The comparisons also revealed that the AIRS IPW tends to be relatively dry in moist atmospheres (when IPW values >40 mm) but wetter in dry cases (when IPW values <10 mm). This is consistent with the documented bias of satellite measurements toward the first guess used in retrieval algorithms. However, additional study is needed to verify whether the AIRS water vapor retrieval process is the source of the discrepancies. It is shown that the IPW bias and rms differences have a seasonal dependency, with a maximum in summer (bias ∼ 1.2 mm, rms ∼ 4.14 mm) and minimum in winter (bias < −0.5 mm, rms ∼ 3 mm).

Corresponding author address: James Yoe, NOAA/NESDIS/ORA, World Weather Building, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: james.g.yoe@noaa.gov

Abstract

A robust and easily implemented verification procedure based on the column-integrated precipitable water (IPW) vapor estimates derived from a network of ground-based global positioning system (GPS) receivers has been used to assess the quality of the Atmospheric Infrared Sounder (AIRS) IPW retrievals over the contiguous United States. For a period of six months from April to October 2004, excellent agreement has been realized between GPS-derived IPW estimates and those determined from AIRS, showing small monthly bias values ranging from 0.5 to 1.5 mm and root-mean-square (rms) differences of 4 mm or less. When the spatial (latitude–longitude) window for the GPS and AIRS matchup observations is reduced from the initial ½° by ½° to ¼° by ¼°, the rms differences are reduced. Analysis revealed that the observed IPW biases between the instruments are strongly correlated to the reported surface pressure differences between the GPS and AIRS observational points. Adjusting the AIRS IPW values to account for the surface pressure discrepancies resulted in significant reductions of the bias between GPS and AIRS. A similar reduction can be obtained by comparing only (GPS–AIRS) match-up pairs for which the corresponding surface pressure differences are 0.5 mb or less. The comparisons also revealed that the AIRS IPW tends to be relatively dry in moist atmospheres (when IPW values >40 mm) but wetter in dry cases (when IPW values <10 mm). This is consistent with the documented bias of satellite measurements toward the first guess used in retrieval algorithms. However, additional study is needed to verify whether the AIRS water vapor retrieval process is the source of the discrepancies. It is shown that the IPW bias and rms differences have a seasonal dependency, with a maximum in summer (bias ∼ 1.2 mm, rms ∼ 4.14 mm) and minimum in winter (bias < −0.5 mm, rms ∼ 3 mm).

Corresponding author address: James Yoe, NOAA/NESDIS/ORA, World Weather Building, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: james.g.yoe@noaa.gov

Save
  • Aumann, H. H., and Coauthors, 2003: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens., 41 , 253264.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bevis, M., Businger S. , Herring T. A. , Rocken C. , Anthes R. A. , and Ware R. H. , 1992: GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. J. Geophys. Res., 97 , 1578715801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bevis, M., Businger S. , Chiswell S. , Herring T. , and Anthes R. , 1994: GPS meteorology: Mapping zenith wet delays onto precipitable water. J. Appl. Meteor., 33 , 379386.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birkenheuer, D., and Gutman S. , 2005: A comparison of the GOES moisture-derived product and GPS-IPW during IHOP. J. Atmos. Oceanic Technol., 22 , 18401847.

    • Search Google Scholar
    • Export Citation
  • Divakarla, M. G., Barnet C. D. , Goldberg M. D. , McMillin L. M. , Maddy E. , Wolf W. , Zhou L. , and Liu X. , 2006: Validation of Atmospheric Infrared Sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts. J. Geophys. Res., 111 .D09S15, doi:10.1029/2005JD006116.

    • Search Google Scholar
    • Export Citation
  • Duan, J., and Coauthors, 1996: GPS meteorology: Direct estimation of the absolute value of precipitable water vapor. J. Appl. Meteor., 35 , 830838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Y., Bai Z. , Fang P. , and Williams A. , 2001: GPS water vapour experimental results from observations of the Australian Regional GPS Network (ARGN). Proc. A Spatial Odyssey: 42nd Australian Surveyors Congress, Brisbane, Australia, Queensland Government Natural Resources and Mines and Cosponsors. [Available online at http://www.isaust.org.au/innovation/2001-Spatial_Odyssey/pdf/feng.pdf.].

    • Search Google Scholar
    • Export Citation
  • Fetzer, E., and Coauthors, 2003: AIRS/AMSU/HSB validation. IEEE Trans. Geosci. Remote Sens., 41 , 418431.

  • Gutman, S. I., Sahm S. R. , Stewart J. Q. , Benjamin S. G. , Smith T. L. , and Schwartz B. E. , 2003: A new composite observing system strategy for ground-based GPS meteorology. Preprints, 12th Symp. on Meteorological Observations and Instrumentation, Long Beach, CA, Amer. Meteor. Soc., CD-ROM, 5.2.

  • Jeannet, P., Hoegger B. , and Levrat G. , 2002: Comparison of chilled mirror hygrometer and a carbon hygristor for radiosonde humidity measurements. Proc. Radiosonde Workshop, Hampton, VA, Center for Atmospheric Sciences, Hampton University, CD-ROM.

  • Kleespies, T. J., and McMillin L. M. , 1990: Retrieval of precipitable water from observations in the split window over varying surface temperatures. J. Appl. Meteor., 29 , 851862.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y-H., Schreiner W. S. , Wang J. , Rossiter D. L. , and Zhang Y. , 2005: Comparison of GPS radio occultation soundings with radiosondes. Geophys. Res. Lett., 32 .L05817, doi:10.1029/2004GL021443.

    • Search Google Scholar
    • Export Citation
  • Lambrigtsen, B. H., and Lee S. Y. , 2003: Coalignment and synchronization of the AIRS instrument suite. IEEE Trans. Geosci. Remote Sens., 41 , 343351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMillin, L. M., Crone L. J. , and Crosby D. S. , 1989: Adjusting satellite radiances by regression with an orthogonal transformation to a prior estimate. J. Appl. Meteor., 28 , 969975.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMillin, L. M., Zhao J. , Rama Varma Raja M. K. , Gutman S. I. , and Yoe J. G. , 2005: Validation of AIRS moisture products using three-way intercomparisons with radiosondes and GPS sensors. Preprints. Ninth Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), San Diego, CA, Amer. Meteor. Soc., CD-ROM, 1.3.

    • Search Google Scholar
    • Export Citation
  • McMillin, L. M., Zhao J. , Rama Varma Raja M. K. , Gutman S. I. , and Yoe J. G. , 2007: Radiosonde humidity corrections and potential Atmospheric Infrared Sounder moisture accuracy. J. Geophys. Res.,, 112 .D13S90, doi:10.1029/2005JD006109.

    • Search Google Scholar
    • Export Citation
  • Mikhail, E., 1976: Observations and Least Squares. IEP, 497 pp.

  • Miloshevich, L. M., Vömel H. , Oltmans S. J. , and Paukkunen A. , 2003: In situ validation of a correction for time-lag and bias errors in Vaisala RS80-H radiosonde humidity measurements. Proc. 13th ARM Science Team Meeting, Broomfield, CO, Atmospheric Radiation Measurement Program. [Available online at http://www.arm.gov/publications/proceedings/conf13/extended_abs/miloshevich-lm.pdf.].

    • Search Google Scholar
    • Export Citation
  • Miloshevich, L. M., Paukkunen A. , Vömel H. , and Oltmans S. J. , 2004: Development and validation of a time-lag correction for Vaisala radiosonde humidity measurements. J. Atmos. Oceanic Technol., 21 , 13051327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neill, A. E., 1996: Global mapping functions for the atmospheric delay at radio wavelengths. J. Geophys. Res., 101 , 32273246.

  • Olsen, E. T., Fishbein E. , Lee S. Y. , and Manning E. , 2005: AIRS/AMSU/HSB version 4.0 level 2 product levels and layers. Jet Propulsion Laboratory, California Institute of Technology, AIRS version 4.0 documentation for data users, 6 pp. [Available online at http://disc.gsfc.nasa.gov/AIRS/documentation/v4_docs/AIRS_L2_levels_and_layers.pdf.].

  • Parkinson, C. L., 2003: Aqua: An earth-observing satellite mission to examine water and other climate variables. IEEE Trans. Geosci. Remote Sens., 41 , 173183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Press, W. H., Teulkolsky S. A. , Vetterling W. T. , and Flannery B. P. , 1992: Numerical Recipes in FORTRAN 77: The Art of Scientific Computing. Cambridge University Press, 933 pp.

    • Search Google Scholar
    • Export Citation
  • Rama Varma Raja, M. K., Gutman S. I. , Yoe J. G. , McMillin L. M. , and Zhao J. , 2006: Comparison of column integrated water vapor measurements from Atmospheric Infrared Sounder (AIRS) and surface-based global positioning system receivers. Proc. 10th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), Atlanta, GA, Amer. Meteor. Soc., CD-ROM, 3.1.

    • Search Google Scholar
    • Export Citation
  • Revercomb, H. E., and Coauthors, 2003: The ARM program’s water vapor intensive observation periods. Bull. Amer. Meteor. Soc., 84 , 217236.

  • Rocken, C., Van Hove T. , Johnson J. , Solheim F. , Ware R. , Bevis M. , Chiswell S. , and Businger S. , 1995: GPS/STORM–GPS sensing of atmospheric water vapor for meteorology. J. Atmos. Oceanic Technol., 12 , 468478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodgers, C. D., 1990: Characterization and error analysis of profiles retrieved from remote sensing measurements. J. Geophys. Res., 95 , 55875595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy, B., Halverson J. B. , and Wang J. , 2004: The influence of radiosonde “age” on TRMM field campaign soundings humidity correction. J. Atmos. Oceanic Technol., 21 , 470480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saastamoinen, J., 1972: Introduction to practical computation of astronomical refraction. Bull. Geod., 106 , 383397.

  • Smith, T. L., Benjamin S. G. , Gutman S. I. , and Sahm S. , 2007: Short-range forecast impact from assimilation of GPS-IPW observations into the Rapid Update Cycle. Mon. Wea. Rev., 135 , 29142930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Susskind, J., Barnet C. D. , and Blaisdell J. M. , 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41 , 390409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Susskind, J., Barnet C. D. , Blaisdell J. M. , Iredell L. , Keita F. , Kouvaris L. , Molnar G. , and Chahine M. , 2006: Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover. J. Geophys. Res., 111 .D09S17, doi:10.1029/2005JD006272.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., Lesht B. M. , Clough S. A. , Liljegren J. C. , Revercomb H. E. , and Tobin D. C. , 2003: Dry bias variability in Vaisala RS80-H radiosondes: The ARM experience. J. Atmos. Oceanic Technol., 20 , 117132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., Cole H. L. , Carlson D. J. , Miller E. R. , Beierle K. , Paukkunen A. , and Laine T. K. , 2002: Corrections of humidity measurement errors from the Vaisala RS80 radiosonde —Application to TOGA COARE data. J. Atmos. Oceanic Technol., 19 , 9811002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., Carlson D. J. , Parsons D. B. , Hock T. F. , Lauritsen D. , Cole H. L. , Beierle K. , and Chamberlain E. , 2003: Performance of operational radiosonde humidity sensors in direct comparison with a chilled mirror dew-point hygrometer and its climate implication. Geophys. Res. Lett., 30 .1860, doi:10.1029/2003GL016985.

    • Search Google Scholar
    • Export Citation
  • Wang, J., Zhang L. , and Dai A. , 2005: Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications. J. Geophys. Res., 110 .D21101, doi:10.1029/2005JD006215.

    • Search Google Scholar
    • Export Citation
  • Wolfe, D. E., and Gutman S. I. , 2000: Developing an operational, surface-based, GPS, water vapor observing system for NOAA: Network design and results. J. Atmos. Oceanic Technol., 17 , 426440.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoe, J. G., Gutman S. I. , and Rama Varma Raja M. K. , 2003: GPS validation of AIRS water vapor. Optical Remote Sensing, OSA Tech. Digest OTuA4, Optical Society of America.

    • Search Google Scholar
    • Export Citation
  • Yoe, J. G., Cao C. , Wu X. , McMillin L. M. , Gutman S. I. , Birkenheuer D. L. , Rama Varma Raja M. K. , and Zhao J. , 2004: Calibration and validation of satellite sensors at NOAA/NESDIS/ORA: Summary of methods and recent results. Proc. Workshop on Intercomparison of Large Scale Optical and Infrared Sensors, Noordwijk, Netherlands, CEOS-IVOS. [Available online at http://earth.esa.int/workshops/ivos05/.].

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1445 1109 48
PDF Downloads 272 51 7