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Korean Meteorological Society (한국기상학회)
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Merging Radar Rainfalls of Single and Dual-polarization Radar to Improve the Accuracy of Quantitative Precipitation Estimation

정량적 강우강도 정확도 향상을 위한 단일편파와 이중편파레이더 강수량 합성

  • Lee, Jae-Kyoung (Korea Meteorological Administration Weather Radar Center) ;
  • Kim, Ji-Hyeon (Korea Meteorological Administration Weather Radar Center) ;
  • Park, Hye-Sook (Korea Meteorological Administration Weather Radar Center) ;
  • Suk, Mi-Kyung (Korea Meteorological Administration Weather Radar Center)
  • Received : 2014.05.13
  • Accepted : 2014.08.27
  • Published : 2014.09.30
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Abstract

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

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References

  1. Bates, J. M., and C. W. J. Granger, 1969: The combination of forecasts. Oper. Res. Quart., 20, 451-468. https://doi.org/10.1057/jors.1969.103
  2. Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric doppler weather radar: Principal and applications. Cambridge University Press, p. 636.
  3. Cifelli, R., V. Chandraselar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge, 2011: Dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technolo., 28, 352-364. https://doi.org/10.1175/2010JTECHA1488.1
  4. Clemen, R. T., A. H. Murphy, and R. L. Winker, 1995: Screening probability forecasts: contrasts between choosing and combining. Int. J. Forecasting, 11, 133- 146. https://doi.org/10.1016/0169-2070(94)02007-C
  5. Dolan, B., and S. Rutledge, 2009: A theory-based hydrometeor identification algorithm for X-band polarimetric radars. J. Atmos. Oceanic Technol., 26, 2071- 2088. https://doi.org/10.1175/2009JTECHA1208.1
  6. Georgakakos, K. P., D. J. Seo, H. Gupta, J. Schaake, and M. B. Butts, 2004: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. J. Hydrol., 298, 222-241. https://doi.org/10.1016/j.jhydrol.2004.03.037
  7. Goudenhoofdt, E., and L. Delobbe, 2009: Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrol. Earth Syst. Sc., 13, 195- 203. https://doi.org/10.5194/hess-13-195-2009
  8. Granger, C. W. J., and P. Newbold, 1977: Forecasting Economic Time Series. Academic Press, New York, U. S. A.
  9. Heo, S.-I., M. Kang, K.-Y. Nam, H.-J. Ko, C.-K. Lee, C.-K. Lee, J.-C. Choi, and Y.-J. Choi, 2012: Case study of hydrometeor classification using dual-polarization radar. Proceeding of Spring Meeting of Korean Meteorological Society, 50.
  10. Jeong, D.-I., and Y.-O. Kim, 2009: Combining single-value streamflow forecasts - A review and guidelines for selecting techniques. J. Hydrol., 377, 284-299. https://doi.org/10.1016/j.jhydrol.2009.08.028
  11. Kang, M., K.-Y. Nam, J.-C. Choi, and Y.-J. Choi, 2011: Rainfall estimation case study with the NIMR X-POL radar. Proceeding of Annual Meeting of Korea Water Resources Association 2011, 358-359.
  12. Kang, M., K.-Y. Nam, S.-I. Heo, J.-C. Choi, and Y.-J. Choi, 2012: Rainfall estimation and hydrometeor classification with the NIMR X-POL radar. Proceeding of the Annual Meeting of Korea Water Resources Association 2012, 277.
  13. Kim, M. K., I. S. Kang, C. K. Park, and K. M. Kim, 2004: Superensemble prediction of resional precipitation over Korea. Int. J. Climatol., 24, 777-790. https://doi.org/10.1002/joc.1029
  14. Korea Meteorological Administration, 2012: Radar rainfall estimation comparison and verification joint experiment report, Weather Radar Center and Meteorological Advancement Council, Korea Meteorological Administration.
  15. Krishnamurti, T. N., C. M. Kishtawal, T. E. Larow, D. R. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285, 1548-1550. https://doi.org/10.1126/science.285.5433.1548
  16. Kwon, S.-H., G. Lee, Y.-H. Cho, Y.-A. Oh, and C.-K. Lee, 2011: Rainfall estimation from an operational S-band dual-polarization radar measurement. Proceeding of the Autumn Meeting of Korea Meteorological Society, 2011, 182-183.
  17. Marshall, J. S., R. C. Langille, and W. McK. Palmer, 1947: Measurement of rainfall by radar. J. Meteor., 4, 186- 192. https://doi.org/10.1175/1520-0469(1947)004<0186:MORBR>2.0.CO;2
  18. Martens, B., P. Cabus, I. De Jongh, and N. E. C. Verhoest, 2013: Merging weather radar observations with ground-based measurements of rainfall using an adaptive multiquadric surface fitting algorithm. J. Hydrol., 500, 84-96. https://doi.org/10.1016/j.jhydrol.2013.07.011
  19. Marzano, F. S., 2007: Supervised fuzzy-logic classification of hydrometeors using C-band weather radar. Geosci. Remote Sens., 45, 3784-3799. https://doi.org/10.1109/TGRS.2007.903399
  20. McLeod, A. I., D. J. Noakes, K. W. Hipel, and R. M. Thompstone, 1987: Combining hydrologic forecast. J. Water Res. Plann. Manage., 113, 29-41. https://doi.org/10.1061/(ASCE)0733-9496(1987)113:1(29)
  21. Oh, Y.-A., G. Lee, S.-H. Jung, Y.-H. Cho, and K.-Y. Nam, 2010: Quantitative rainfall estimation with the NIMR X-POL radar. Proceeding of the Autumn Meeting of Korea Meteorological Society, 2011, 412-413.
  22. Pettazzi, A., and S. Salson, 2012: Combining radar and rain gauges rainfall estimates using conditional merging: a case study. Proceeding of the ERAD 2012 - The 7th European Conference on Radar in Meteorology and Hydrology, 2012.
  23. Rajagopalan, R., U. Lall, and S. E. Zebiak, 2002: Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Mon. Wea. Rev., 130, 1792-1811. https://doi.org/10.1175/1520-0493(2002)130<1792:CCFTRA>2.0.CO;2
  24. Vulpiani, G., S. Giangrande, and F. S. Marzano, 2009: Estimation from polarimetric S-band radar measurement: Validation of a neural network approach. J. Appl. Meteor. Climatol., 48, 2022-2036. https://doi.org/10.1175/2009JAMC2172.1

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