A Sensitivity Study of WRF Model Simulations to Nudging Methods for A Yeongdong Heavy Snowfall Event

영동 대설 사례를 대상으로 한 WRF Simulation의 Nudging 방법에 따른 민감도 연구

  • Choi, Ji Won (Department of Atmospheric and Environmental Sciences Gangneung-Wonju National University) ;
  • Lee, Jae Gyoo (Department of Atmospheric and Environmental Sciences Gangneung-Wonju National University)
  • 최지원 (강릉원주대학교 대기환경과학과) ;
  • 이재규 (강릉원주대학교 대기환경과학과)
  • Received : 2014.11.19
  • Accepted : 2014.12.19
  • Published : 2015.03.31


To investigate the influences of the observational nudging and the analysis nudging on the WRF simulation for the heavy snowfall event in Yeongdong area on 26 February 2012, the sensitivity experiments in relation to nudging effects were conducted. We initially set the magnitude of nudging coefficient of $6.0{\times}10^{-4}s^{-1}$ to apply to the analysis nudging experiments and observational experiments. To select the optimized options for the observational nudging, the radius influence experiment was carried out with radii ranging from 10 to 25 km at 5 km intervals. Among the observational nudging experiments, the experiment, which was conducted with the option of the radius influence of 15 km and that of the nudging coefficient of $6.0{\times}10^{-4}s^{-1}$ (ONG exp.), showed a best result. As giving the nudging effect only directly on D1 and D2 brought about a better result for the analysis nudging, we set the analysis nudging experiment as above (ANG exp.). We compared and analyzed the results from the control experiment, ONG experiment, and ANG experiment to reveal nudging effects. It was found that the control experiment brought about a result that it overestimated its precipitation in comparison with the observation and failed to properly simulate the time zone of rainfall concentration. When either of the two nudging (observational and analysis nudging) was applied to the data assimilation, it brought about a better result than the control experiment. Especially the observational nudging led to a meaningful result for the wind field, while the analysis nudging had the best result for the precipitation distribution among the experiments.



Supported by : 기상청


  1. Anderson, T., and S. Nilson, 1990: Topographically induced convective snowbands over the Baltic Sea and their precipitation distribution. Amer. Meteor. Soc., 5, 299-312.
  2. Cho, K.-H., Y.-J. Cho, and T.-Y. Kwon, 2004: Characteristics of air mass related with precipitation events in Yeongdong region. Asia-Pac. J. Atmos. Sci., 40, 381-393 (in Korean with English abstract).
  3. Choi, H.-J., H. W. Lee, K.-H. Sung, and M.-J. Kim, 2009: The effect of atmospheric flow field according to the radius influence and nudging coefficient of the objective analysis on complex area. J. Environ. Sci., 18, 271-281 (in Korean with English abstract).
  4. Choi, J. H., Y. H. Lee, D. E. Chang, and C. H. Cho, 2002: The impact of surface data assimilation on shortrange prediction using AWS data. Atmosphere, 12, 377-380 (in Korean).
  5. Choi, W., J. G. Lee, and Y.-J. Kim, 2013: The impact of data assimilation on WRF simulation using surface data and radar data : Case study. Atmosphere, 23, 143-160 (in Korean with English abstract).
  6. Deng, A., and Coauthors, 2009: Update on WRF-ARW end-to-end multi-scale FDDA system. Proceedings of the 10th Annual WRF users' workshop, Boulder, CO., USA, NCAR, 1.9.
  7. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077-3107.
  8. Eiserloh, A. J., 2014: WRF-Model data assimilation studies of landfalling atmospheric rivers and orographic precipitation over Northern California. M. S. thesis, San Jose State University.
  9. Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF singlemoment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129-151.
  10. Hong, S.-Y., Y. Noah, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341.
  11. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170-181.<0170:TKCPAU>2.0.CO;2
  12. Kumar, R., M. C. Barth, G. G. Pfister, M. Naja, and G. P. Brasseur, 2014: WRF-Chem simulations of a typical pre-monsoon dust storm in northern India: influences on aerosol optical properties and radiation budget. Atmos. Chem. Phys., 14, 2431-2446.
  13. Lee, H. W., M.-J. Kim, D.-H. Kim, H.-G. Kim, and S.-H. Kim, 2009: Investigation of the assimilated surface wind characteristics for the evaluation of wind resources. J. Korean Environ. Sci. Soc., 25, 1-14 (in Korean with English abstract).
  14. Lee, J. G., and Y. J. Kim, 2008: A numerical simulation study using WRF of a heavy snowfall event in the Yeongdong coastal area in relation to the northeasterly. Atmosphere, 18, 339-354 (in Korean with English abstract).
  15. Lee, J. Y., 2006: Impact of nudging and resolution on the monthly weather prediction. M. S. thesis, Yonsei University (in Korean with English abstract).
  16. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated- k model for the longwave. J. Geophys. Res., 102, 16663-16682.
  17. Nam, H.-G., B.-G. Kim, S.-O. Han, C. K. Lee, and S.-S. Lee, 2014: Characteristics of easterly induced snowfall in Yeongdong and its relationship to air-sea temperature difference. Asia-Pac. J. Atmos. Sci., 50, 541-552.
  18. Ryu, C. M., and I. H. Cho, 2010: Sensitivity analysis of KWRF model using analysis nudging method in relate to forecasting precipitation. Proceedings of the 2010 Autumn Meeting of Korean Meteorological Society, 246-247 (in Korean).
  19. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Technical Note TN- 475+STR, 125 pp.
  20. Stauffer, D. R., and N. L. Seaman, 1990: Use of fourdimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250-1277.<1250:UOFDDA>2.0.CO;2
  21. Stauffer, D. R., and N. L. Seaman, 1991: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary boundary layer. Mon. Wea. Rev., 119, 734-754.<0734:UOFDDA>2.0.CO;2
  22. Yang, A.-R., 2012: A sensitivity study of WRF to the choice of observational data used in nudging. M. S. thesis, Gangneung-Wonju National University (in Korean with English abstract).
  23. Yoon, M. J., 2010: The sensitivity analysis of WRF model with various nudging methods and physics scheme. M. S. thesis, Anyang University (in Korean with English abstract).
  24. Yu, W., Y. Liu, and T. Warner, 2007: An evaluation of 3DVAR, nudging-based FDDA, and a hybrid scheme for summer convection forecasts using the WRFARW model. Proceedings of the 22nd Conference on Weather Analysis and Forecasting/18th Conference on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., P2.8.

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