• Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. E. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and Mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 13191343, https://doi.org/10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brusky, G., and R. Mamrosh, 2015: Future of AMDAR in the National Weather Service. Great Lakes Operational Meteorology Workshop, Grand Rapids, MI, NWS, https://www.weather.gov/media/grr/GLOM2015/Presentations/2_Brusky-Amdar3.pdf.

  • Dirksen, R. J., M. Sommer, F. J. Immler, D. F. Hurst, R. Kivi, and H. Vömel, 2014: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosondes. Atmos. Meas. Tech., 7, 44634490, https://doi.org/10.5194/amt-7-4463-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyre, J., and R. Reid, 2014: Cost-benefit studies of observing systems. Met Office Forecasting Research Tech. Rep. 593, 11 pp.

  • Fleming, R. J., 2000: Water vapor measurements from commercial aircraft: Progress and plans. Preprints, Fourth Symp. on Integrated Observing Systems, Long Beach, CA, Amer. Meteor. Soc., 30–33.

  • Fleming, R. J., and R. D. May, 2004: The 2nd Generation Water Vapor Sensing System and benefits of its use on commercial aircraft for air carriers and society. SpectraSensors Rep., 16 pp., www.eol.ucar.edu/system/files/spectrasensors.pdf.

  • Helms, D., A. Hoff, H. Smit, S. Taylor, and S. Carlberg, 2010: Advancements in the AMDAR humidity sensing. Technical Conf. on Meteorological and Environmental Instruments and Methods of Observations, Helsinki, Finland, WMO.

  • Hoover, B. T., D. A. Santek, A. Daloz, Y. Zhong, R. Dworak, R. A. Petersen, and A. Collard, 2017: Forecast impacts of assimilating aircraft WVSS-II water vapor mixing ratio observations in the Global Data Assimilation System (GDAS). Wea. Forecasting, 32, 16031611, https://doi.org/10.1175/WAF-D-16-0202.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • James, E. P., and S. G. Benjamin, 2017: Observation system experiments with the hourly updating rapid refresh model using GSI hybrid ensemble–variational data assimilation. Mon. Wea. Rev., 145, 28972918, https://doi.org/10.1175/MWR-D-16-0398.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jamison, B., and W. R. Moninger, 2002: An analysis of the temporal and spatial distribution of ACARS data in support of the TAMDAR program. Preprints, 10th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., J33–J36.

  • Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World map of the Köppen-Geiger climate classification updated. Meteor. Z., 15, 259263, https://doi.org/10.1127/0941-2948/2006/0130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mamrosh, R., J. Gillis, R. Petersen, and R. Baker, 2006: A comparison of WVSS-II and NWS radiosonde temperature and moisture data. 10th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Atlanta, GA, Amer. Meteor. Soc., 3.9, https://ams.confex.com/ams/pdfpapers/104889.pdf.

  • Moninger, W. R., R. D. Mamrosh, and P. M. Pauley, 2003: Automated meteorological reports from commercial aircraft. Bull. Amer. Meteor. Soc., 84, 203216, https://doi.org/10.1175/BAMS-84-2-203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., 2016: On the impact and benefits of AMDAR observations in operational forecasting. Part I: A review of the impact of automated aircraft wind and temperature reports. Bull. Amer. Meteor. Soc., 97, 585602, https://doi.org/10.1175/BAMS-D-14-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., W. Feltz, E. Olson, and S. Bedka, 2006a: Evaluation of the WVSSII moisture sensor using co-located in-situ and remotely sensed observations. 10th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Atlanta, GA, Amer. Meteor. Soc., P2.6, http://ams.confex.com/ams/Annual2006/techprogram/paper_102987.htm.

  • Petersen, R. A., W. Feltz, E. Olson, and S. Bedka, 2006b: Results of the November 2006 WVSS—Rawinsonde Intercomparison Study. University of Wisconsin–Madison Cooperative Institute for Meteorological Satellite Studies Rep., 42 pp., http://amdar.noaa.gov/docs/UW_WVSS-II_Nov2006_Assessment_FINAL.pdf.

  • Petersen, R. A., L. M. Cronce, W. F. Feltz, E. Olson, and D. Helms, 2011: Validation studies of WVSS-II moisture observations. 15th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Seattle, WA, Amer. Meteor. Soc., 209, https://ams.confex.com/ams/91Annual/webprogram/Paper184449.html.

  • Petersen, R. A., L. M. Cronce, R. Mamrosh, R. Baker, and P. Pauley, 2016: On the impact and future benefits of AMDAR observations in operational forecasting: Part II: Water vapor observations. Bull. Amer. Meteor. Soc., 97, 21172133, https://doi.org/10.1175/BAMS-D-14-00211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petty, G. W., 2008: Moist processes. A First Course in Atmospheric Thermodynamics. Sundog Publishing, 183–185.

  • Rahn, D. A., and C. J. Mitchell, 2016: Diurnal climatology of the boundary layer in Southern California using AMDAR temperature and wind profiles. J. Appl. Meteor. Climatol., 55, 11231137, https://doi.org/10.1175/JAMC-D-15-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, B., and S. G. Benjamin, 1995: A comparison of temperature and wind measurements from ACARS-equipped aircraft and rawinsondes. Wea. Forecasting, 10, 528544, https://doi.org/10.1175/1520-0434(1995)010<0528:ACOTAW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sonntag, D., 1994: Advancements in the field of hygrometry. Z. Meteor., 3, 5166, https://doi.org/10.1127/metz/3/1994/51.

  • SpectraSensors, 2010: Atmospheric Water Vapor Sensing System (WVSS) technical specifications. SpectraSensors Doc., 4 pp., www.spectrasensors.com/files/525/.

  • Sun, B., X. Calbet, A. Reale, S. Schroeder, M. Bali, R. Smith, and M. Pettey, 2021: Accuracy of Vaisala RS41 and RS92 upper tropospheric humidity compared to satellite hyperspectral infrared measurements. Remote Sens., 13, 173, https://doi.org/10.3390/rs13020173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vance, A. K., S. J. Abel, R. J. Cotton, and A. M. Woolley, 2015: Performance of WVSS-II hygrometers on the FAAM research aircraft. Atmos. Meas. Tech., 8, 16171625, https://doi.org/10.5194/amt-8-1617-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wexler, A., 1976: Vapor pressure formulation for water in range 0 to 100°C. J. Res. Natl. Bur. Stand., 80A, 775785, https://doi.org/10.6028/jres.080A.071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WMO, 2011: Instruments and observing methods: WMO intercomparison of high quality radiosonde systems. WMO Rep. 107, 249 pp.

  • WMO, 2014: WIGOS: WMO Integrated Global Observing System—The benefits of AMDAR data to meteorology and aviation. WMO Tech. Rep. 2014-01, 47 pp.

  • WMO, 2018: Guide to instruments and methods of observation. WMO Doc., 573 pp., https://library.wmo.int/doc_num.php?explnum_id=10179.

  • WMO, 2019: Instruments and observing methods: Tests, comparisons and operational performance of the Water Vapor Sensing Systems (WVSS-II). WMO Rep. 133, 45 pp., https://library.wmo.int/doc_num.php?explnum_id=9882.

All Time Past Year Past 30 Days
Abstract Views 31 31 31
Full Text Views 4 4 4
PDF Downloads 6 6 6

Examining the Compatibility of Aircraft Moisture Observations and Operational Radiosondes

View More View Less
  • 1 Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin
© Get Permissions
Restricted access

Abstract

The addition of moisture observations via the Water Vapor Sensing System (WVSS) from about 150 aircraft available operationally through the World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) program now provides highly reliable thermodynamic profiles of the troposphere. The nearly 900 profiles available daily provide greater temporal and spatial density than the operational radiosonde network over many parts of the United States. Previous studies comparing WVSS reports with specially collocated radiosondes have documented the quality and consistency of the WVSS observations. These studies, however, have been limited for short periods at a single location. This study expands on the earlier evaluations by using operational U.S. radiosondes from 2015 in a variety of locations, seasons, and climates. Comparison profiles at radiosonde sites were calculated in pressure layers and then interpolated to terrain-following sigma coordinates to account for the differences in elevations of comparison sites and provide a better means of integrating the higher vertical resolution of AMDAR observations taken in the boundary layer. Overall, systematic differences between the WVSS and radiosondes are smallest just above the surface, with the WVSS observations being slightly moister than the radiosondes aloft, with WVSS reports being moister during ascent than descent—possibly the result of small hysteresis effects. Standard deviations averaged 1.3 g kg−1 near the surface over the yearlong period. Differences varied by season and region. Overall, the results indicate that WVSS observations are compatible with radiosonde reports and can be used with high confidence to fill temporal and spatial data gaps.

Williams’s current affiliations: Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Skylar Williams, skylar.williams@noaa.gov

Abstract

The addition of moisture observations via the Water Vapor Sensing System (WVSS) from about 150 aircraft available operationally through the World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) program now provides highly reliable thermodynamic profiles of the troposphere. The nearly 900 profiles available daily provide greater temporal and spatial density than the operational radiosonde network over many parts of the United States. Previous studies comparing WVSS reports with specially collocated radiosondes have documented the quality and consistency of the WVSS observations. These studies, however, have been limited for short periods at a single location. This study expands on the earlier evaluations by using operational U.S. radiosondes from 2015 in a variety of locations, seasons, and climates. Comparison profiles at radiosonde sites were calculated in pressure layers and then interpolated to terrain-following sigma coordinates to account for the differences in elevations of comparison sites and provide a better means of integrating the higher vertical resolution of AMDAR observations taken in the boundary layer. Overall, systematic differences between the WVSS and radiosondes are smallest just above the surface, with the WVSS observations being slightly moister than the radiosondes aloft, with WVSS reports being moister during ascent than descent—possibly the result of small hysteresis effects. Standard deviations averaged 1.3 g kg−1 near the surface over the yearlong period. Differences varied by season and region. Overall, the results indicate that WVSS observations are compatible with radiosonde reports and can be used with high confidence to fill temporal and spatial data gaps.

Williams’s current affiliations: Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Skylar Williams, skylar.williams@noaa.gov
Save