Inhomogeneity in Winter Precipitation Measurements

  • Daqing YangEmail author
  • Antonina Simonenko
  • Xiaobo He
Living reference work entry
Part of the Ecohydrology book series (ECOH)


Analyses of the long-term (1991–2010) intercomparison data quantify the consistency in winter precipitation observations by six identical Tretyakov gauges at the Valdai research station in Russia. Relative to the standard Tretyakov gauge, the mean catch ratios vary from 97% to 106% for dry snow, 94–104% for wet snow, 87–109% for blowing snow, 96–103% for mixed precipitation, to 98–101% for winter rain. The differences between the highest and lowest mean catches are about 10–11% for snow, 7% for mixed precipitation, and 3% for rain. On average, this difference is about 0.2 mm over the 12-h observation period. The catch difference for blowing snow is much higher, up to 22%, or average of 0.6 mm per observation. Comparisons of 12-h observations show a better consistency in gauge performance for the low snowfall events, and a large variation in gauge catch for the high snowfall cases. The differences in 12-h snow catches are mostly less than 2 mm among the 6 gauges. The difference in the 12-h observations is less than 1% for rain and 4% for mixed precipitation. Close linear relationships exist between the 12-h gauge observations for all precipitation types. The maximum differences in gauge snow catches increase very weakly with the wind speed, and higher differences are associated with the warmer temperatures from –5 °C to 0 °C. There is, however, no significant relationship between the max catch difference and mean wind speed or temperature over the 12-h period.


Tretyakov gauge Valdai station Precipitation Measurement Consistency 


  1. J. Adam, D.P. Lettenmaier, Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res. 108(D9), 4257 (2003). Scholar
  2. T. Bardsley, M.W. Williams, Overcollection of sold precipitation by a standard precipitation gauge, Niwot Ridge, Colorado. in Proceedings of Western Snow Conference, Canada, Western Snow Conference 1997, pp. 354–362Google Scholar
  3. E.G. Bogdanova, Investigation of precipitation measurement loss due to wind. Trans. Main. Geophys. Obs. 195, 40–62 (1966)Google Scholar
  4. Y. Ding, D. Yang, B. Ye, N. Wang, Effects of bias correction on precipitation trend over China. J. Geophys. Res. 112(D13), D13116 (2007). Scholar
  5. V.S. Golubev, On the problem of actual precipitation measurements at the observations site, in Proceeding of the International Workshop on the Correction of Precipitation measurements W MO/TD 104, 1985, pp. 61–64Google Scholar
  6. V.S. Golubev, Assessment of accuracy characteristics of the reference precipitation gauge with a double-fence shelter. in Final Report of the Fourth Session of the International Organizing Committee for the WMO Solid Precipitation Measurement Intercomparison (St.Moritz, WMO, 1989), pp. 34–41Google Scholar
  7. B.E. Goodison, Compatibility of Canadian snowfall and snowcover data. Water Resour. Res. 17, 893–900 (1981)CrossRefGoogle Scholar
  8. B.E. Goodison, S. Klemm, B. Sevruk, in WMO Solid Precipitation Measurement Intercomparison. TECO-1988 WMO/TD-No. 222, Leipzig, 1988, pp. 255–262Google Scholar
  9. B. Goodison, P.Y.T. Louie, D. Yang, WMO solid precipitation measurement intercomparison. Final Report, WMO/TD-No. 872, 1998, 212 ppGoogle Scholar
  10. P.Y. Groisman, V.V. Koknaeva, T.A. Belokrylova, T.R. Karl, Overcoming biases of precipitation measurement: a history of the USSR experience. Bull. Am. Meteorol. Soc. 72, 1725–1732 (1991)CrossRefGoogle Scholar
  11. P.Y. Groisman, E.L. Peck, R.G. Quayle, Intercomparison of recording and standard nonrecording U.S. gauges. J. Atmos. Ocean. Technol. 165, 602–609 (1999)CrossRefGoogle Scholar
  12. J. Kochendorfer, R. Nitu, M. Wolff, E. Mekis, R. Rasmussen, B. Baker, M.E. Earle, A. Reverdin, K. Wong, C.D. Smith, D. Yang, Y.-A. Roulet, S. Buisan, T. Laine, G. Lee, J.L.C. Aceituno, J. Alastrué, K. Isaksen, T. Meyers, R. Brækkan, S. Landolt, A. Jachcik, A. Poikonen, Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE. Hydrol. Earth Syst. Sci. 21, 3525–3542 (2017). Scholar
  13. D.R. Legates, T.A. Bogart, Estimating the proportion of monthly precipitation that falls in solid form. J. Hydrometeorol., 1209–1306 (2009).
  14. É. Mekis, L.A. Vincent, An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean 49(2), 163–177 (2011)CrossRefGoogle Scholar
  15. J.R. Metcalfe, B.E. Goodison, Correction Canadian winter precipitation data. in Eighth Symposium on Meteorological Observations and Instrumentation, Anaheim, 17–22 January 1993, AMS Boston, pp. 338–343Google Scholar
  16. V. Nešpor, B. Sevruk, Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. J. Atmos. Ocean. Technol. 16, 450–464 (1999)CrossRefGoogle Scholar
  17. R. Nitu, K. Wong, CIMO survey on national summaries of methods and instruments for solid precipitation measurement at automatic weather stations, WMO/TD-No. 1544, CIOM No. 102 2010, 57ppGoogle Scholar
  18. R. Rasmussen, Coauthors, How well are we measuring snow: the NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc. 93, 811–829. (2012). Scholar
  19. M. Sanderson, A comparison of Canadian and United States standard methods of measuring precipitation. J. Appl. Meteorol. 14(10), 1197–1199 (1975)CrossRefGoogle Scholar
  20. B. Sevruk, W.R. Hamon, International comparison of national precipitation gauges with a reference pit gauge. WMO Instrument and Observing Methods Report 17, 1984, 111 ppGoogle Scholar
  21. B. Sevruk, S. Klemm, Types of standard precipitation gauges, in Proceedings of International Workshop on Precipitation Measurement, (St.Moritz, WMO/IAHS/ETH, 1989), pp. 227–236Google Scholar
  22. I. Strangeways, Using Google Earth to evaluate GCOS weather station sites. Weather 64(1) (2009). 8ppCrossRefGoogle Scholar
  23. K. Sugiura, T. Ohata, D. Yang, Catch characteristics of precipitation in high-latitude regions with high winds. J. Hydrometeorol. 7, 984–994 (2006)CrossRefGoogle Scholar
  24. K. Sugiura, T. Ohata, D. Yang, Application of a snow particle counter to solid precipitation measurements under the Arctic condition. Cold Reg. Sci. Technol. 58(2009), 77–83 (2009)CrossRefGoogle Scholar
  25. X. Tian, A. Dai, D. Yang, Z. Xie, Effects of precipitation-bias corrections on surface hydrology over northern Latitudes. J. Geophys. Res. 112(D14), D14101 (2007). Scholar
  26. D. Yang, An improved precipitation climatology for the Arctic Ocean. Geophys. Res. Lett. 26(11), 1625–1628 (1999)CrossRefGoogle Scholar
  27. D. Yang, T. Ohata, A bias-corrected Siberian regional precipitation climatology. J. Hydrometeorol. 2, 122–139 (2001)CrossRefGoogle Scholar
  28. D. Yang, A. Simonenko, Comparison of winter precipitation measurements by Six Tretyakov gauges at the Valdai experimental site. Atmosphere-Ocean (2013). Scholar
  29. D. Yang, J.R. Metcalfe, B.E. Goodison, E. Mekis, An evaluation of double fence intercomparison reference (DFIR) gauge. in Proceedings of Eastern Snow Conference, 50th Meeting, Quebec City, 1993, pp. 105–111Google Scholar
  30. D. Yang, B.E. Goodison, J.R. Metcalfe, V.S. Golubev, E. Elomaa, T. Gunther, R. Bates, T. Pangburn, C. Hanson, D. Emerson, V. Copaciu, J. Milkovic, Accuracy of Tretyakov precipitation gauge: result of WMO intercomparison. Hydrol. Process. 9(8), 877–895 (1995)CrossRefGoogle Scholar
  31. D. Yang, B.E. Goodison, J.R. Metcalfe, V.S. Golubev, R. Bates, T. Pangburn, C.L. Hanson, Accuracy of NWS 8″ standard non-recording precipitation gauge: result and application of WMO intercomparison. J. Atmos. Ocean. Technol. 15, 54–68 (1998a)CrossRefGoogle Scholar
  32. D. Yang, B.E. Goodison, C.S. Benson, S. Ishida, Adjustment of daily precipitation at 10 climate stations in Alaska: application of World Meteorological Organization intercomparison results. Water Resour. Res. 34(2), 241–256 (1998b)CrossRefGoogle Scholar
  33. D. Yang, E. Elomaa, T. Gunther, V. Golubev, B.E. Goodison, B. Sevruk, H. Madsen, J. Milkovic, Wind-induced precipitation undercatch of the Hellmann gauges. Nord. Hydrol. 30, 57–80 (1999a)CrossRefGoogle Scholar
  34. D. Yang, S. Ishida, B.E. Goodison, T. Gunther, Bias correction of precipitation data for Greenland. J. Geophys. Res.-Atmos. 105(D6), 6171–6182 (1999b)CrossRefGoogle Scholar
  35. D. Yang, B.E. Goodison, J.R. Metcalfe, P. Louie, G. Leavesley, D. Emerson, V. Golubev, E. Elomaa, T. Gunther, C.L. Hanson, T. Pangburn, E. Kang, J. Milkovic, Quantification of precipitation measurement discontinuity induced by wind shields on national gauge. Water Resour. Res. 35(2), 491–508 (1999c)CrossRefGoogle Scholar
  36. D. Yang, D.L. Kane, L.D. Hinzman, B.E. Goodison, J.R. Metcalfe, P.Y.T. Louie, G.H. Leavesley, D.G. Emerson, C.L. Hanson, An evaluation of the Wyoming gauge system for snowfall measurement. Water Resour. Res. 36(9), 2665–2677 (2000)CrossRefGoogle Scholar
  37. D. Yang, B.E. Goodison, J.R. Metcalfe, P.Y.T. Louie, E. Elomaa, C.L. Hanson, V.S. Golubev, T. Gunther, J. Milkovic, M. Lapin, Compatibility evaluation of national precipitation gauge measurements. J. Geophys. Res.-Atmos. 16(D2), 1481–1491 (2001)CrossRefGoogle Scholar
  38. D. Yang, D. Kane, Z. Zhang, D. Legates, B. Goodison, Bias corrections of long-term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett. 32(19), L19501 (2005)CrossRefGoogle Scholar
  39. B. Ye, D. Yang, Y. Ding, T. Han, T. Koike, A bias-corrected precipitation climatology for China. J. Hydrometeorol. 5(6), 1147–1160 (2004)CrossRefGoogle Scholar
  40. B. Ye, D. Yang, L. Ma, Effect of precipitation bias-correction on water budget calculation in Upper Yellow River, China. Environ. Res. Lett. (2012). Scholar
  41. Y. Zhang, T. Ohata, D. Yang, G. Davaa, Bias correction of daily precipitation measurements for Mongolia. Hydrol. Process. 18, 2991–3005 (2004)CrossRefGoogle Scholar

Copyright information

© Crown 2019

Authors and Affiliations

  1. 1.Watershed Hydrology and Ecology Research Division, Water Science and TechnologyEnvironment and Climate Change CanadaVictoriaCanada
  2. 2.State Hydrologic InstituteSt. PetersburgRussia
  3. 3.State Key Laboratory of Cryosphere SciencesCold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of SciencesLanzhouChina

Personalised recommendations