Analysis of Z-R relationship equations varying by rain cluster for rainfall estimation using Sattahip radar
Article Sidebar
Main Article Content
Abstract
Weather radar measures the reflectivity of radar waves when they interact with raindrops. This radar reflectivity varies according to the size and distribution pattern of the raindrops. When using radar reflectivity data to estimate rainfall, this data is converted to rainfall intensity (R, (mm/h)) using the Z-R relationship equation (Z=aRb). This study collected data from 510 rainfall events between February 2, 2018, and August 31, 2020, comprising hourly rainfall from 110 automatic rain gauges and radar reflectivity within a 240 km radius of the Sattahip radar. The data was analyzed to determine the most appropriate rainfall estimation using various Z-R relationship equations: Z-R relationships that vary according to rain clusters based on radar reflectivity, Z-R relationships that vary according to rain clusters based on rainfall intensity measured by automatic rain gauges, climatological Z-R equation, Z=300R1.4, and Z=200R1.6. Each of these rainfall estimations was then compared to the rainfall intensity from automatic rain gauges to find the statistical values of RMSE (Root Mean Squared Error), MSE (Mean Squared Error), and MAE (Mean Absolute Error). The results indicated that the rainfall estimation method using Z-R relationships that vary according to rain clusters based on rainfall intensity provided the most accurate rainfall estimation for the Sattahip radar. This was determined by examining the statistical values of RMSE, MSE, and MAE, which were closest to zero for both calibration and verification rainfall events, when compared to rainfall estimation methods using Z-R relationships that vary according to rain clusters based on radar reflectivity, climatological Z-R equation, Z=300R1.4, and Z=200R1.6 respectively.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Morin E, Gabella M. Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes. J Geophys Res. 2007;112:D20108. https://doi.org/10.1029/2006JD008206.
Marshall JS, Palmer WMK. The distribution of raindrops with size. Journal of Meteorology. 1984;5(4):165-6. https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.
Mapiam P, Sriwongsitanon N. Climatological Z-R relationship for radar rainfall estimation in the upper Ping river basin. ScienceAsia. 2008;34:215-22. https://doi.org/10.2306/scienceasia1513-1874.2008.34.215.
Hanchoowong R, Weesakul U, Chumchean S. Bias correction of radar rainfall estimates based on a geostatistical technique. ScienceAsia. 2012;38:373-85. https://doi.org/10.2306/scienceasia1513-1874.2012.38.373.
Chantraket P, Detyothin C, Pankaew S, Kirtsaeng S. An operational weather radar-based calibration of Z–R relationship over Central Region of Thailand. Int J Eng. 2016;2:92–100.
Ayat H, Kaviaanpour MR, Moazami S, Hong Y, Ghaemi E. Calibration of weather radar using region probability matching method (RPMM). Theor Appl Climatol. 2018;134:165-76. https://doi.org/10.1007/s00704-017-2266-7.
Ramli S, Tahir W. Radar hydrology: new Z/R relationships for quantitative precipitation estimation in Klang River Basin, Malaysia. International Journal of Environmental Science and Development. 2011;2(3):537-41. https://doi.org/10.1109/CHUSER.2011.6163790.
Michelson D, Einfalt T, Holleman I, Gjertsen U, Friedrich K, Haase G, Lindskog M, Sztuc J. Weather radar data quality in Europe: quality control and characterization. COST 717 Working Document WDF_20_200204_1. 2004.
Hydro & meteo GmbH & Co. KG. SCOUT Documentation Version 3.32, Hydro & meteo GmbH & Co. KG., Germany. 2016.
Battan LJ. Radar observation of the atmosphere, The University of Chicago Press. 1973;324.
Chumchean S. Improved estimation of radar rainfall for use in hydrological modelling. Ph.D. Thesis, University of New South Wales, Sydney, Australia. 2004.
Delrieu G, Andrieu H, Creutin JD. Quantification of path-integrated attenuation for X and C-band weather radar systems operating in Mediterranean heavy rainfall. J Appl Meteor. 2000;39(6):840-50. https://doi.org/10.1175/1520-0450(2000)039<0840:QOPIAF>2.0.CO;2.
Futon RA, Breidenbach JP, Seo DJ, Miller DA, O’Brannon T. The WSD–88D rainfall algorithm. Weather Forecasting. 1998;13:377-95. https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2.
Doelling IG, Joss J, Riedl J. Systematic variations of Z-R relationships from drop size distributions measured in northern Germany during seven years. Atmospheric Research. 1998;47-48:635-49. https://doi.org/10.1016/S0169-8095(98)00043-X.
Steiner M, Smith JA. Reflectivity, rain rate, and kinetic energy flux relationships based on raindrop spectra. American Meteorological Society. 2000;39:1923-40. https://doi.org/10.1175/1520-0450(2000)039<1923:RRRAKE>2.0.CO;2.
Hagen M, Yuter, SE. Relations between radar reflectivity, liquid water content, and rainfall rate during the MAP-SOP. Atmospheric Sciences. 2003;128:477-94. https://doi.org/10.1256/qj.02.23.
Mapiam P, Sriwongsitanon N, Chumchean S, Sharma A. Effects of rain gauge temporal resolution on the specification of a ZR relationship. J Atmos Oceanic Technol. 2009;26:1302-14. https://doi.org/10.1175/2009JTECHA1161.1.
Mapiam P, Sharma A, Sriwongsitanon N. Defining the Z–R relationship using gauge rainfall with coarse temporal resolution: implications for flood forecasting. J Hydrol Eng. 2014;19(8):04114003. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000616.
Seed A, Sirivarden L, Sun X, Jordan P, Elliot J. On the calibration of Australian weather radars. Technical report 02/7. 2002;40.
Woodley W, Herndon AA. (1970). Raingage evaluation of the Miami reflectivity-rainfall rate relation. Journal of Applied Meteorology. 1970;9(2):258-64. https://doi.org/10.1175/1520-0450(1970)009<0258:AREOTM>2.0.CO;2.
