Quantifying wind-induced undercatch in the precipitation measurements at Ukrainian weather stations

Keywords: wind-induced undercatch, precipitation measurements, Tretyakov gauge, wind speed, precipitation map

Abstract

Literature overview. Precipitation measurements include random and systematic errors. Systematic errors increase in the following order: evaporation loss, wetting loss, and wind-induced undercatch (World Meteorological Organization, 2008). The last one occurs because of the aerodynamic blockage under the precipitation gauge collector (Baghapour et al. 2017; Sevruk & Nespor, 1994). Field experiments have shown that wind-induced undercatch reaches 14% for rain and 40% for snow for the Tretyakov wind-shielded gauge (Goodison et al., 1998).

In Ukraine, precipitation records omit wind-induced undercatch correction.

This study aims to calculate true precipitation values at Ukrainian weather stations, evaluate existing methodologies for precipitation measurements correction, and create the digital archive of corrected precipitation values based on sub-daily observations.

Material and methods. We used four methods to quantify wind-related errors for the Tretyakov gauge with wind shield proposed by Golubev (Konovalov et al., 2000), Bryazgin (Aleksandrov et al., 2005), Norway meteorological institute (Forland et al., 1996), and Yang (Yang et al., 1995). Sub-daily records were requested from Central Geophysical Observatory named after Boris Sreznevsky covering 207 stations between 1976 and 2019; 187 stations had more than 20 years’ period.

Results. For the Tretyakov gauge, annual wind-induced undercatch ranges from 5 to 9.5%, depending on correction methodology. The highest bias is observed for the solid precipitation – from 17.7 to 27.4%. The precipitation loss increases along with annual wind speed at the weather station (correlation coefficient r = 0.89).

Conclusions. We suggest that Golubev’s and Yang’s methodologies estimate precipitation wind-induced undercatch more accurately at stations where blizzards are often observed, we recommended using the Golubev’s methodology because it takes into account “false” precipitations.

The precipitation loss equals 0.2–4% according to the Golubev’s method at covered weather stations and reaches 13–19% at the bare mountain regions or seashore. Solid precipitation is more sensitive to the influence of wind – snow loss averages 17.3% according to the Golubev methodology or 21% according to the Yang methodology, while rain loss – 2.6% or 6.7%, respectively.

The obtained database with corrected precipitation comprises sub-daily and daily records from 207 Ukrainian stations between 1976 and 2019. It could be used for hydrological and climatological research.

Downloads

Download data is not yet available.

Author Biographies

Valeriy Osypov, Ukrainian Hydrometeorological Institute

PhD (Geography), Senior Researcher

Andrii Bonchkovskyi, Ukrainian Hydrometeorological Institute

Junior Researcher

Andrii Oreshchenko, Ukrainian Hydrometeorological Institute

PhD (Geography), Senior Researcher

Dmytro Oshurok, Ukrainian Hydrometeorological Institute

PhD (Geography), Senior Researcher

Natalia Osadcha, Ukrainian Hydrometeorological Institute

Doctor (Geography)

References

Bogdanova, E. G., Golubev, V. S., Il’in, B. M., & Dragomilova, I. V. (2002). New model for correction of measured precipitation and its use in Russian polar regions. Meteorology and Hydrology, 10, 68–94 [in Russian].

State hydrometeorological survice. (2011). Guidelines for hydrometeorological stations and posts. Issue 3. Part 1. Meteorological observations at stations. 280 [in Ukrainian].

Sevruk, Boris, & Nespor, V. (1994). The Effect of Dimensions and Shape of Precipitation Gauges on the Wind-Induced Error. In Global Precipitations and Climate Change (pp. 231–246). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-79268-7_14

Golubev, V. S. (1979). Research of the double fence cover influnce on precipitation measurement of Tretyakov gauge. Trudy GGI, 258, 91–101 [in Russian].

Alekseev, G. A. (1975). Methods for estimating random errors of hydrometeorological information. Leningrad: Hy-drometeoizdat, 96 [in Russian].

World Meteorological Organization. (2008). Guide to Hydrological Practices, Volume I: Hydrology – From Meas-urement to Hydrological Information. 296.

Smirnova, N. S. (Ed.). (1990). Scientific and applied reference book on the climate of the USSR. Series 3. Long-term data. Parts 1-6. Issue 10. Ukrainian SSR. Book 1. Leningrad: Hydrometeoizdat, 605 [in Russian].

Baghapour, B., Wei, C., & Sullivan, P. E. (2017). Numerical simulation of wind-induced turbulence over precipita-tion gauges. Atmospheric Research, 189, 82–98. https://doi.org/10.1016/j.atmosres.2017.01.016

Goodison, B. E., Louie, P. Y. T., & Yang, D. (1998). WMO Solid precipitation measurement intercomparison: Final report. 212.

Bogdanova, E. G. (1968). Accounting the wind error in measured precipitation when calculating their average long-term values (norms). Trudy GGO, 215, 45–56 [in Russian].

Golubev, V. S. (1973). Methodology for correcting sub-daily and monthly values of atmospheric precipitation and the results of its verification. Trudy GGI, 207, 11–27 [in Russian].

Konovalov, D., Golubev, V. S., Bogdanova, E. G., & Ylin, B. M. (2000). A full model for correction of precipitation measurements data and a method and algorithm for estimation of systematic error components. In Papers present-ed at the WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observa-tion (TECO-2000). WMO/TD- No. 1028; IOM Report - No. 74 (pp. 136–139) [in Russian].

Bogdanova, E. G., Il’in, B. M., & Dragomilova, I. V. (2003). Experience in using an improved methodology for ad-justing daily precipitation amounts in various climatic conditions. Trudy GGO, 551, 23–50 [in Russian].

State hydrological institute. (1973). Guide for water balance stations. Leningrad: Hydrometeoizdat, 306 [in Rus-sian].

Sevruk, B., & Hamon, W. R. (1984). International Comparison of National Precipitation Gauges with a Reference Pit Gauge (WMO/TD-No. 38). Geneva, 139.

Forland, E. J., Allerup, P., Dahlstrom, B., Elomaa, E., Jonsson, T., Madsen, H., … Vejen, F. (1996). Manual for oper-ational correction of Nordic precipitation data (Report NR. 24/96). Oslo, 66.

Yang, D., Goodison, B. E., Metcalfe, J. R., Golubev, V. S., Elomaa, E., Gunther, T., … Milkovic, J. (1995). Accuracy of Tretyakov precipitation gauge: Result of WMO intercomparison. Hydrological Processes, 9(8), 877–895. https://doi.org/10.1002/hyp.3360090805

Aleksandrov, Y. I., Bryazgin, N. N., Førland, E. J., Radionov, V. F., & Svyashchennikov, P. N. (2005). Seasonal, inter-annual and long-term variability of precipitation and snow depth in the region of the Barents and Kara seas. Po-lar Research, 24(1–2), 69–85. https://doi.org/10.3402/polar.v24i1.6254

Kosovets, O., & Shvets, N. (2011). History and physical-geographical description of meteorological stations of Ukraine (climatological reference book). Kyiv, 462 [in Ukrainian].

Grebin’, V. V. (2010). Modern streamflow regime of rivers in Ukraine (landscape-hydrology analysis) [in Ukraini-an]. Kyiv: Nika-Centr, 316 [in Ukrainian].

Olenev, A. M. (1987). Impact of macrorelief on climate and landscape complexes. Sverdlovsk, 86 [in Russian].

Mkrtchian, O., & Shuber, P. (2013). Interpolation of meteodata on precipitation and other climatic variables by regression-kriging. Visnyk Lviv Univ. Ser. Geogr., 42, 258–264 [in Ukrainian].

Mkrtchian, O., & Shuber, P. (2011). A method for geospatial and mapping of climatic characteristics from mete-ostation observation data. Visnyk Lviv Univ. Ser. Geogr., 39, 245–253 [in Ukrainian].

Barabash, M. B., Pahaluk, O. E., & Tatarchuk, O. G. (2007). Precipitation. Year. In National atlas of Ukraine (p. 435). Kyiv, 435 [in Ukrainian].

Lipinskiy, B. M., Dyachuk, V. A., & Babichenko, B. M. (Eds.). (2003). Climate in Ukraine. Kyiv: Vyd-vo Raevskogo, 343 [in Ukrainian].

Barabash, M. B., Korzh, T. V., & Tatarchuk, O. G. (2004). Investigation of changes and fluctuations in precipitation at the turn of the 20th and 21st centuries in the context of global warming. Nauk. Pr. UkrNDGMI, 253, 92–102 [in Ukrainian].

Yang, D., & Simonenko, A. (2014). Comparison of Winter Precipitation Measurements by Six Tretyakov Gauges at the Valdai Experimental Site. Atmosphere-Ocean, 52(1), 39–53. https://doi.org/10.1080/07055900.2013.865156

Karl, T. R., Quayle, R. G., & Groisman, P. Y. (1993). Detecting Climate Variations and Change: New Challenges for Observing and Data Management Systems. Journal of Climate, 6(8), 1481–1494. https://doi.org/10.1175/1520-0442(1993)006<1481:DCVACN>2.0.CO;2

Legates, D. R. (1995). Global and terrestrial precipitation: A comparative assessment of existing climatologies. International Journal of Climatology, 15(3), 237–258. https://doi.org/10.1002/joc.3370150302

World Meteorological Organization. (2018). Guide to Instruments and Methods of Observation. 1385.

World Meteorological Organization. (2018). WMO Solid Precipitation Intercomparison Experiment (SPICE). In-struments and Observing Methods. Report No. 131. 1445. Retrieved from https://library.wmo.int/index.php?lvl=notice_display&id=20742#.X5lglIgzZhE

Published
2021-12-01
Cited
How to Cite
Osypov, V., Bonchkovskyi, A., Oreshchenko, A., Oshurok, D., & Osadcha, N. (2021). Quantifying wind-induced undercatch in the precipitation measurements at Ukrainian weather stations. Visnyk of V. N. Karazin Kharkiv National University, Series "Geology. Geography. Ecology&quot;, (55), 204-215. https://doi.org/10.26565/2410-7360-2021-55-15