• Allard, J., , Keim B. D. , , Chassereau J. E. , , and Sathiaraj D. , 2009: Spuriously induced precipitation trends in the Southeast United States. Theor. Appl. Climatol., 96, 173177, doi:10.1007/s00704-008-0021-9.

    • Search Google Scholar
    • Export Citation
  • Baker, C. B., , Larson L. , , May E. , , Bogin H. , , and Collins B. , 2005: Final report: Operational testing of various precipitation sensors in support of the United States Climate Reference Network (USCRN). NOAA Tech. Note. USCRN-05-2, 69 pp. [Available online at http://www1.ncdc.noaa.gov/pub/data/uscrn/documentation/program/technotes/TN05002PrecipTesting.pdf.]

  • Bell, J. E., and Coauthors, 2013: U.S. Climate Reference Network soil moisture and temperature observations. J. Hydrometeor., 14, 977988, doi:10.1175/JHM-D-12-0146.1.

    • Search Google Scholar
    • Export Citation
  • Daly, C., , Gibson W. P. , , Taylor G. H. , , Doggett M. K. , , and Smith J. I. , 2007: Observer bias in daily precipitation measurements at United States Cooperative Network stations. Bull. Amer. Meteor. Soc., 88, 899912, doi:10.1175/BAMS-88-6-899.

    • Search Google Scholar
    • Export Citation
  • Devine, K. A., , and Mekis E. , 2008: Field accuracy of Canadian rain measurements. Atmos.–Ocean, 46, 213227, doi:10.3137/ao.460202.

  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 424498, doi:10.1175/BAMS-D-12-00170.1.

    • Search Google Scholar
    • Export Citation
  • Diem, J. E., , and Mote T. L. , 2005: Interepochal changes in summer precipitation in the southeastern United States: Evidence of possible urban effects near Atlanta, Georgia. J. Appl. Meteor., 44, 717730, doi:10.1175/JAM2221.1.

    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., 2008: Using vibrating-wire technology for precipitation measurements. Precipitation: Advances in Measurement, Estimation, and Prediction, S. C. Michaelides, Ed., Springer, 33–58.

  • Easterling, D. R., , Karl T. R. , , Lawrimore J. H. , , and Del Greco S. A. , 1999: United States Historical Climatology Network daily temperature, precipitation and snow data for 1871–1997. ORNL/CDIAC-118, DNP-070, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, 82 pp.

  • Fiebrich, C. A., 2009: History of surface weather observations in the United States. Earth-Sci. Rev., 93, 7784, doi:10.1016/j.earscirev.2009.01.001.

    • Search Google Scholar
    • Export Citation
  • Fiebrich, C. A., , and Crawford K. C. , 2009: Automation: A step toward improving the quality of daily temperature data produced by climate observing networks. J. Atmos. Oceanic Technol., 26, 12461260, doi:10.1175/2009JTECHA1241.1.

    • Search Google Scholar
    • Export Citation
  • Gall, R., , Young K. , , Schotland R. , , and Schmitz J. , 1992: The recent maximum temperature anomalies in Tucson: Are they real or an instrumental problem? J. Climate, 5, 657665, doi:10.1175/1520-0442(1992)005<0657:TRMTAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Golubev, V. S., , Groisman P. Ya. , , and Quayle R. G. , 1992: An evaluation of the United States standard 8-in. nonrecording raingage at the Valdai Polygon, Russia. J. Atmos. Oceanic Technol., 9, 624629, doi:10.1175/1520-0426(1992)009<0624:AEOTUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Goodison, B. E., , Louie P. Y. T. , , and Yang D. , 1998: WMO solid precipitation measurement intercomparison. WMO Instruments and Observing Methods Rep. 67, WMO/TD-872, 212 pp.

  • Gordon, J. D., 2003: Evaluation of candidate rain gages for upgrading precipitation measurement tools for the National Atmospheric Deposition Program. Water-Resources Investigations Rep. USGS/WRIR 02-4302, 31 pp.

  • Groisman, P. Ya., , and Legates D. R. , 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc., 75, 215227, doi:10.1175/1520-0477(1994)075<0215:TAOUSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Ya., , Peck E. L. , , and Quayle R. G. , 1999: Intercomparison of recording and standard nonrecording U.S. gauges. J. Atmos. Oceanic Technol., 16, 602609, doi:10.1175/1520-0426(1999)016<0602:IORASN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Groot, A., , and Carlson D. W. , 1996: Influence of shelter on night temperatures, frost damage, and bud break of white spruce seedlings. Can. J. For. Res., 26, 15311538, doi:10.1139/x26-172.

    • Search Google Scholar
    • Export Citation
  • Guttman, N. B., , and Baker C. B. , 1996: Exploratory analysis of the difference between temperature observations recorded by ASOS and conventional methods. Bull. Amer. Meteor. Soc., 77, 28652873, doi:10.1175/1520-0477(1996)077<2865:EAOTDB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Harrison, R. G., 2011: Lag-time effects on naturally ventilated large thermometer screen. Quart. J. Roy. Meteor. Soc., 137, 402408, doi:10.1002/qj.745.

    • Search Google Scholar
    • Export Citation
  • Hubbard, K. G., , Lin X. , , and Walter-Shea E. A. , 2001: The effectiveness of the ASOS, MMTS, GILL, and CRS air temperature radiation shields. J. Atmos. Oceanic Technol., 18, 851864, doi:10.1175/1520-0426(2001)018<0851:TEOTAM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hubbard, K. G., , Lin X. , , Baker C. B. , , and Sun B. , 2004: Air temperature comparison between the MMTS and the USCRN temperature systems. J. Atmos. Oceanic Technol., 21, 15901597, doi:10.1175/1520-0426(2004)021<1590:ATCBTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 1995: Critical issues for long-term climate monitoring. Climatic Change, 31, 185221, doi:10.1007/BF01095146.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., , Melillo J. M. , , Peterson T. C. , , and Hassol S. J. , Eds., 2009: Global Climate Change Impacts in the United States.Cambridge University Press, 188 pp.

  • Lin, X., , and Hubbard K. G. , 2004: Sensor and electronic biases/errors in air temperature measurements in common weather station networks. J. Atmos. Oceanic Technol., 21, 10251032, doi:10.1175/1520-0426(2004)021<1025:SAEEIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Melillo, J. M., , Richmond T. C. , , and Yohe G. W. , Eds., 2014: Climate change impacts in the United States: The third national climate assessment. U.S. Global Change Research Program, 841 pp., doi:10.7930/J0Z31WJ2.

  • Menne, M. J., , and Williams C. N. Jr., 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 17001717, doi:10.1175/2008JCLI2263.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., , Williams C. N. Jr., , and Vose R. S. , 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 9931007, doi:10.1175/2008BAMS2613.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., , Williams C. N. Jr., , and Palecki M. A. , 2010: On the reliability of the U.S. surface temperature record. J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

    • Search Google Scholar
    • Export Citation
  • Nakamura, R., , and Mahrt L. , 2005: Air temperature measurement errors in naturally ventilated radiation shields. J. Atmos. Oceanic Technol., 22, 10461058, doi:10.1175/JTECH1762.1.

    • Search Google Scholar
    • Export Citation
  • National Research Council, 1998: Future of the National Weather Service Cooperative Observer Network.National Academies Press, 65 pp.

  • National Weather Service, 2014a: Cooperative station observations. National Weather Service Manual 10-1315, Surface Observing Program, Operations and Services, 138 pp.

  • National Weather Service, cited 2014b: What is the Coop program? [Available online at http://www.nws.noaa.gov/om/coop/what-is-coop.html.]

  • Nitu, R., , and Wong K. , 2010: CIMO survey on national summaries of methods and instruments for solid precipitation measurement at automatic weather stations. Instruments and Observing Methods Rep. 102, WMO/TD-1544, 57 pp.

  • Peterson, T. C., , and Vose R. S. , 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc., 78, 28372849, doi:10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and Coauthors, 1998: Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol., 18, 14931517, doi:10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T.

    • Search Google Scholar
    • Export Citation
  • Pielke, R., and Coauthors, 2007: Documentation of uncertainties and biases associated with surface temperature measurement sites and for climate change assessment. Bull. Amer. Meteor. Soc., 88, 913928, doi:10.1175/BAMS-88-6-913.

    • Search Google Scholar
    • Export Citation
  • Quayle, R. G., , Easterling D. R. , , Karl T. R. , , and Hughes P. Y. , 1991: Effects of recent thermometer changes in the Cooperative Station Network. Bull. Amer. Meteor. Soc., 72, 17181723, doi:10.1175/1520-0477(1991)072<1718:EORTCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow? The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, doi:10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Sieck, L. C., , Burges S. J. , , and Steiner M. , 2007: Challenges in obtaining reliable measurements of point rainfall. Water Resour. Res., 43, W01420, doi:10.1029/2005WR004519.

    • Search Google Scholar
    • Export Citation
  • Sensor Instruments Co., Inc., 2000: Operating manual: NIMBUS PL digital thermometer. 28 pp.

  • Sevruk, B., , Ondrás M. , , and Chvía B. , 2009: The WMO precipitation measurement intercomparisons. Atmos. Res., 92, 376380, doi:10.1016/j.atmosres.2009.01.016.

    • Search Google Scholar
    • Export Citation
  • Sun, B., , Bruce C. B. , , Karl T. R. , , and Gifford M. D. , 2005: A comparative study of ASOS and USCRN temperature measurements. J. Atmos. Oceanic Technol., 22, 679686, doi:10.1175/JTECH1752.1.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., , Bashor P. G. , , and McDowell V. L. , 2010: Comparison of rain gauge measurements in the mid-Atlantic region. J. Hydrometeor., 11, 553565, doi:10.1175/2009JHM1137.1.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., , Roche R. J. , , and Bashor P. G. , 2014: An experimental study of spatial variability of rainfall. J. Hydrometeor., 15, 801811, doi:10.1175/JHM-D-13-031.1.

    • Search Google Scholar
    • Export Citation
  • WMO, 2008: Measurement of precipitation. Guide to meteorological instruments and methods of observation, WMO-8, 7th ed. 1.6-1–1.6-17.

  • Wu, H., , Hubbard K. G. , , and You J. , 2005: Some concerns when using data from the cooperative weather station networks: A Nebraska case study. J. Atmos. Oceanic Technol., 22, 592602, doi:10.1175/JTECH1733.1.

    • Search Google Scholar
    • Export Citation
  • Yao, W., , and Zhong S. , 2009: Nocturnal temperature inversions in a small, enclosed basin and their relationship to ambient atmospheric conditions. Meteor. Atmos. Phys., 103, 195210, doi:10.1007/s00703-008-0341-4.

    • Search Google Scholar
    • Export Citation
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Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and Precipitation Comparison

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  • 1 Cooperative Institute for Climate and Satellites–North Carolina, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina
  • 2 NOAA/National Climatic Data Center, Asheville, North Carolina
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Abstract

The U.S. Cooperative Observer Program (COOP) network was formed in the early 1890s to provide daily observations of temperature and precipitation. However, manual observations from naturally aspirated temperature sensors and unshielded precipitation gauges often led to uncertainties in atmospheric measurements. Advancements in observational technology (ventilated temperature sensors, well-shielded precipitation gauges) and measurement techniques (automation and redundant sensors), which improve observation quality, were adopted by NOAA’s National Climatic Data Center (NCDC) into the establishment of the U.S. Climate Reference Network (USCRN). USCRN was designed to provide high-quality and continuous observations to monitor long-term temperature and precipitation trends, and to provide an independent reference to compare to other networks. The purpose of this study is to evaluate how diverse technological and operational choices between the USCRN and COOP programs impact temperature and precipitation observations. Naturally aspirated COOP sensors generally had warmer (+0.48°C) daily maximum and cooler (−0.36°C) minimum temperatures than USCRN, with considerable variability among stations. For precipitation, COOP reported slightly more precipitation overall (1.5%) with network differences varying seasonally. COOP gauges were sensitive to wind biases (no shielding), which are enhanced over winter when COOP observed (10.7%) less precipitation than USCRN. Conversely, wetting factor and gauge evaporation, which dominate in summer, were sources of bias for USCRN, leading to wetter COOP observations over warmer months. Inconsistencies in COOP observations (e.g., multiday observations, time shifts, recording errors) complicated network comparisons and led to unique bias profiles that evolved over time with changes in instrumentation and primary observer.

Corresponding author address: Ronald D. Leeper, CICS-NC, National Climatic Data Center, 151 Patton Avenue, Asheville, NC 28801. E-mail: ronald.leeper@noaa.gov

Abstract

The U.S. Cooperative Observer Program (COOP) network was formed in the early 1890s to provide daily observations of temperature and precipitation. However, manual observations from naturally aspirated temperature sensors and unshielded precipitation gauges often led to uncertainties in atmospheric measurements. Advancements in observational technology (ventilated temperature sensors, well-shielded precipitation gauges) and measurement techniques (automation and redundant sensors), which improve observation quality, were adopted by NOAA’s National Climatic Data Center (NCDC) into the establishment of the U.S. Climate Reference Network (USCRN). USCRN was designed to provide high-quality and continuous observations to monitor long-term temperature and precipitation trends, and to provide an independent reference to compare to other networks. The purpose of this study is to evaluate how diverse technological and operational choices between the USCRN and COOP programs impact temperature and precipitation observations. Naturally aspirated COOP sensors generally had warmer (+0.48°C) daily maximum and cooler (−0.36°C) minimum temperatures than USCRN, with considerable variability among stations. For precipitation, COOP reported slightly more precipitation overall (1.5%) with network differences varying seasonally. COOP gauges were sensitive to wind biases (no shielding), which are enhanced over winter when COOP observed (10.7%) less precipitation than USCRN. Conversely, wetting factor and gauge evaporation, which dominate in summer, were sources of bias for USCRN, leading to wetter COOP observations over warmer months. Inconsistencies in COOP observations (e.g., multiday observations, time shifts, recording errors) complicated network comparisons and led to unique bias profiles that evolved over time with changes in instrumentation and primary observer.

Corresponding author address: Ronald D. Leeper, CICS-NC, National Climatic Data Center, 151 Patton Avenue, Asheville, NC 28801. E-mail: ronald.leeper@noaa.gov
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