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Precipitation Type Specific Radar Reflectivity-rain Rate Relationships for Warsaw, Poland

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Języki publikacji
EN
Abstrakty
EN
Implementation of weather radar precipitation estimates into hydrology, especially urban hydrology practice in Poland, requires the introduction of more precise radar reflectivity versus rain rate (Z-R) relationships accounting for drop size distribution (DSD) specific for different precipitation phases. We explored the development of precipitation type dependent Z-R relationship on the basis of approximately two years of DSD recordings at high temporal resolution of ten seconds. We divided the recorded data into four separate precipitation-type groups: rain, snow, rain-with-snow, and hail. The Z-R relationships for rain and rainwith-snow showed a strong resemblance to the well-known MarshallPalmer Z-R power-type relationship for rain. In the case of snowfall, we found that both the multiplication factor and the exponent coefficients in the Z-R formula have smaller values than for rain. In contrast, for hail precipitation these parameters are higher than for rain, especially the multiplication factor.
Czasopismo
Rocznik
Strony
1840--1857
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
  • Faculty of Environmental Engineering, Wroclaw University of Technology, Wrocław, Poland
  • HR – Hydroscience & Engineering, University of Iowa, Iowa City, USA
Bibliografia
  • Bech, J., V. Vidal, J.A. Ortiz, N. Pineda, and R. Veciana (2014), Real-time estimation of surface precipitation type merging weather radar and automated station observations. In: Abstracts 17th International Road Weather Conference, 30 January – 1 February 2014, Andorra.
  • Bringi, V.K., and V. Chandrasekar (2001), Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press.
  • Ciach, G.J., and W.F. Krajewski (1999), Radar-rain gauge comparisons under observational uncertainties, J. Appl. Meteorol. 38, 10, 1519-1525, DOI: 10.1175/1520-0450(1999)0382.0.CO;2.
  • Cyr, I. (2014), Estimation of Z-R relationship and comparative analysis of precipitation data from colocated rain-gauge, vertical radar and disdrometer, M.Sc. Thesis, Norwegian University of Science and Technology, Department of Hydraulic and Environmental Engineering, available from: http://www. diva-portal.org/smash/get/diva2:749367/FULLTEXT01.pdf.
  • Dotzek, N., and K.D. Beheng (2001), The influence of deep convective motions on the variability of Z-R relations, Atmos. Res. 59-60, 15-39, DOI: 10.1016/ S0169-8095(01)00107-7.
  • Gill, R.S., M.B. Soerensen, T. Boevith, J. Koistinen, M. Peura, D. Michelson, and R. Cremonini (2012), BALTRAD dual polarization hydrometeor classifier. In: Abstracts ERAD 2012 – 7th European Conference on Radar in Meteorology and Hydrology, 25-29 June 2012, Toulouse, France.
  • Gjertsen, U., and V. Ødegaard (2005), The water phase of precipitation – a comparison between observed, estimated and predicted values, Atmos. Res. 77, 218- 231, DOI: 10.1016/j.atmosres.2004.10.030.
  • Jakubiak, B., P. Licznar, and S.P. Malinowski (2014), Rainfall estimates from radar versus rain gauge measurements. Warsaw case study, Environ. Prot. Eng. 40, 2, 162-170, DOI: 10.5277/epe140212.
  • Joss, J.K., K. Schram, J.C. Thams, and A. Waldvogel (1970), On the quantitative determination of precipitation by radar, Wissenschaffliche Mineilungen, No. 63, Eidgenossisichen Komission Zum Studium der Hagelbildung und der Hagelawehr.
  • Lee, G., and I. Zawadzki (2005), Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation, J. Appl. Meteorol. 44, 2, 241-255, DOI: 10.1175/JAM2183.1
  • Licznar, P. (2009), Wstępne wyniki porównawczych testów polowych elektronicznego deszczomierza wagowego OTT Pluvio2 i disdrometru laserowego Parsivel, Instal. 7-8, 43-50 (in Polish).
  • Licznar, P., and K. Siekanowicz-Grochowina (2015), Application of laser disdrometer to weather radar image calibration in the example of Warsaw, Ochr. Sr. 37, 2, 11-16 (in Polish).
  • Licznar, P., C. De Michele, and W. Adamowski (2015), Precipitation variability within an urban monitoring network via microcanonical cascade generators, Hydrol. Earth Syst. Sc. 19, 1, 485-506, DOI: 10.5194/hess-19-485-2015.
  • Marshall, J.S., and W. McK. Palmer (1948), The distribution of raindrops with size, J. Meteorol. 5, 4, 165-166, DOI: 10.1175/1520-0469(1948)0052.0.CO;2.
  • Marshall, J.S., R.C. Langille, and W. McK. Palmer (1947), Measurement of rainfall by radar, J. Meteorol. 4, 186-192, DOI: 10.1175/1520-0469(1947)004 2.0.CO;2.
  • Ochou, A.D., E.-P. Zahiri, B. Bamba, and M. Koffi (2011), Understanding the variability of Z-R relationships caused by natural variations in raindrop size distributions (DSD): Implication of drop size and number, Atmos. Clim. Sci. 1, 147-164, DOI: 10.4236/acs.2011.13017.
  • OFCM (2005), Doppler Radar Meteorological Observations. Part B: Doppler Radar Theory and Meteorology, Federal Meteorological Handbook No. 11, FCM-H11B-2005, WSR-88D, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Washington DC.
  • OTT (2015), Present Weather Sensor OTT Parsivel 2. Operating instructions, Document No. 70.210.001.B.E, OTT Hydromet GmbH, available from: http:// www.tecnologiayambiente.com.ar/wp-content/uploads/Manual_Parsivel2. pdf (accessed: April 2015).
  • Szturc, J., K. Ośródka, and A. Jurczyk (2011), Quality index scheme for quantitative uncertainty characterization of radar-based precipitation, Meteorol. Appl. 18, 4, 407-420, DOI: 10.1002/met.230.
  • Vasiloff, S.V. (2002), Investigation of a WSR-88D Z-R relation for snowfall in northern UTAH. In: Abstracts 16th Conference on Hydrology AMS, 12-17 January 2002, Orlando, USA, 3.18.
  • Villarini, G., and W.F. Krajewski (2010), Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall, Surv. Geophys. 31, 1, 107-129, DOI: 10.1007/s10712-009-9079-x.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-648a542f-14ac-46d4-bc7b-8af3a9abf483
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