High-resolution modeling study of an isolated convective storm over Seoul Metropolitan area

  • Young-Hee LeeEmail author
  • Ki-Hong Min
Original Paper


The ability of a high-resolution (500 m) Weather Research and Forecasting (WRF) model to simulate an isolated convective precipitation event over Seoul metropolitan area on August 16, 2015 was investigated. To understand the effects of micro- and mesoscale forcing on the initiation of convective rainfall under large-scale conditions, we performed sensitivity tests using different initial times. Despite simulating the same case, the quantitative precipitation forecast and the timing of moist convection varied widely among the experiments. Mesoscale features such as outflow and low-level convergence are different in location and intensity among the experiments. When assimilation of surface observations and radar data was performed, the simulation reproduced the low-level convergence and, hence, the location and amount of rainfall reasonably well within the first 6 h of simulation period. The timing differences of convective rainfall among the experiments were examined in terms of the atmospheric boundary layer (ABL) growth. Rapid growth of the ABL enabled moist convection to occur early in the presence of an outflow. An overestimated maximum ABL height by the model also led to earlier collapse of the ABL as compared to observations, which contributed to a reduction of convective available potential energy over the urban area in late afternoon. The results of this study demonstrate that accurate simulation of ABL growth is important for predicting the timing and intensity of isolated convective storms.



This subject is supported by Korea Ministry of Environment (MOE) as “Water Management Research Program”. The ceilometer data used were provided by the Weather Information Service Engine Project.


  1. Ahrens CD (2009) Meteorology today. Brooks/Cole, DelmontGoogle Scholar
  2. Arya SP (2001) Introduction to micrometeorology. Academic Press, San DiegoGoogle Scholar
  3. Barker DM, Huang W, Guo YR, Bourgeois AJ, Xiao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon Weather Rev 132:897–914.;2 CrossRefGoogle Scholar
  4. Barker D, Huang XY, Liu Z, Auligné T, Zhang X, Rugg S, Ajjaji R, Bourgeois A, Bray J, Chen Y, Demirtas M, Guo YR, Henderson T, Huang W, Lin HC, Michalakes J, Rizvi S, Zhang X (2012) The weather research and forecasting (WRF) model’s community variational/ensemble data assimilation system: WRFDA. Bull Am Meteorol Soc 93:831–8843. CrossRefGoogle Scholar
  5. Davolio S, Buzzi A, Malguzzi P (2007) High resolution simulations of an intense convective precipitation event. Meteorol Atmos Phys 95:139–154. CrossRefGoogle Scholar
  6. Ducrocq V, Richard D, Lafore JP, Orain F (2002) Storm-scale Numerical rainfall prediction for five precipitating events over France: on the importance of the initial humidity field. Weather Forecasting 17:1236–1256.;2 CrossRefGoogle Scholar
  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.;2 CrossRefGoogle Scholar
  8. Duran DR, Wyen JA (2016) Thunderstorms do not get butterflies. Bull Am Meteorol Soc 97:237–243. CrossRefGoogle Scholar
  9. Eresmaa N, Karppinen A, Joffre SM, Rasanen J, Talvitie H. (2006) Mixing height determination by ceilometer. Atmos Chem Phys 6: 1485-1493.
  10. Findell KL, Eltahir EAB (2003a) Atmospheric controls on soil moisture-boundary layer interaction. Part I: framework development. J Hydrometeor 4:552–569.;2 CrossRefGoogle Scholar
  11. Findell KL, Eltahir EAB (2003b) Atmospheric controls on soil moisture-boundary layer interaction. Three-dimensional wind effects. J Geophys Res 108(D8):8385. CrossRefGoogle Scholar
  12. Gallus WA, Segal M (2001) Impact of improved initialization of mesoscale features on convective system rainfall in 10-km Eta simulation. Wea Forecasting 16:680–696.;2 CrossRefGoogle Scholar
  13. Gao J, Stensrud DJ (2012) Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification. J Atmos Sci 69:1054–1065. CrossRefGoogle Scholar
  14. Hanley KE, Kirchbaum DJ, Belcher SE, Roberts NM, Leoncini G (2011) Ensemble predictability of an isolated mountain thunderstorm in a high-resolution model. Q J Roy Meteorol Soc 137:2124–2137. CrossRefGoogle Scholar
  15. Hong SY, Lim J (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  16. Hong SY, Pan HL (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339.;2 CrossRefGoogle Scholar
  17. Hu M, Xue M, Brewster K (2006) 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth tornadic thunderstorms. Part I: cloud analysis and its impact. Mon Wea Rev 134:675–698CrossRefGoogle Scholar
  18. Juang JY, Porporato A, Stoy PC, Siqueira MS, Oishi AC, Detto M, Kim HS, Katul GG (2007) Hydrologic and atmospheric controls on initiation of convective precipitation events. Water Resour Res 43:W03421. CrossRefGoogle Scholar
  19. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models. The Kain-Fritcsh scheme. In: Emanuel KA and Raymond DJ (eds) The representation of cumulus convection in numerical models. Am Meteorol Soc, pp 165–170.
  20. Kusaka H, Kondo H, Kikegawa Y, Kimura F (2001) A simple single-layer urban canopy model for atmospheric models: comparison with multi-layer and slab models. Bound-layer Meteorol 101:329–358. CrossRefGoogle Scholar
  21. Lee JH, Lee HH, Choi Y, Kim HW, Lee DK (2010) Radar data assimilation for the simulation of mesoscale convective systems. Adv Atmos Sci 27(5):1025–1042. CrossRefGoogle Scholar
  22. Lorenz E (1969) The predictability of a flow which possesses many scales of motion. Tellus 21(3):289–330. CrossRefGoogle Scholar
  23. Min KH, Choo SH, Lee GW, Lee DH (2015) Evaluation of WRF cloud microphysics schemes using radar observations. Weather Forecasting 30(6):1571–1589. CrossRefGoogle Scholar
  24. Mitchell K (2005) The community Noah Land-Surface Model (LSM). Accessed 16 Jan 2014
  25. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated k-model for the long-wave. J Geophys Res 102(D14):16663–166682. CrossRefGoogle Scholar
  26. Munkel C, Eresmaa N, Rasanen J, Karppinen A (2007) Retrieval of mixing height and dust concentration with lidar ceilometer. Bound-Layer Meteorol 124:117–128. CrossRefGoogle Scholar
  27. NCEP (2015) NCEP GDAS/FNL 0.25 degree global tropospheric analysis and forecast grids. Accessed 10 Sep 2015
  28. NIMR (2014) Construction of input data for WRF-UCM using GIS and its operating method. NIMR-TN-2014-016, NIMRGoogle Scholar
  29. Park JY, Suh MS (2015) Improvement of MODIS land cover classification over the Asia-Oceania region. Korean J Remote Sens 31(2):51–64. CrossRefGoogle Scholar
  30. Park, Park SH, Chae JH, Choi MH, Song Y, Kang M, Roh JW (2017) High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan area, South Korea. Atmos Meas Tech 10:1575–1594. CrossRefGoogle Scholar
  31. Parrish DF, Derber JC (1992) The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon Weather Rev 120:1747–1763.;2 CrossRefGoogle Scholar
  32. Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void filling interpolation methods for SRTM data. Int J Geogr Inf Sci 21(9):983–1008. CrossRefGoogle Scholar
  33. Shin HH, Hong SY (2015) Representation of the subgrid-scale turbulent transport in convective boundary layers at Gray-zone resolutions. Mon Weather Rev 143:250–271. CrossRefGoogle Scholar
  34. Siqueira M, Katul G, Porporato A (2009) Soil moisture feedbacks on convection triggers: the role of soil-plant hydrodynamics. J Hydrometeor 10(1):96–112. CrossRefGoogle Scholar
  35. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the advance research WRF version 3. NCAR technical note, NCAR/TN-475+STRGoogle Scholar
  36. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, LondonCrossRefGoogle Scholar
  37. Xiao Q, Sun, J (2007) Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon Weather Rev 135:3381–3404.
  38. Sun J, Xue M, Wilson JW, Zawadzki I, Ballard SP, Onvlee-Hooimeyer J, Joe P, Barker DM, Li PW, Golding B, Xu M, Pinto J (2014) Use of NWP for nowcasting convective precipitation. Bull Am Meteorol Soc 95:409–426. CrossRefGoogle Scholar
  39. Tsaknakis G, Papayannis A, Kokkalis P, Amiridis V, Kambezidis HD, Mamouri RE, Georgoussis G, Avdikos G (2011) Inter-comparison of lidar and ceilometer retrievals for aerosol and planetary boundary layer profiling over Athens, Greece. Atmos Measure Tech 4:1261–1273.
  40. Walser A, Lüthi D, Schär C (2004) Predictability of precipitation in a cloud-resolving model. Mon Weather Rev 132:560–577.;2 CrossRefGoogle Scholar
  41. Wang H, Sun J, Fan S, Huang XY (2013) Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events. J Appl Meteorol Climatol 52:889–902. CrossRefGoogle Scholar
  42. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, San DiegoGoogle Scholar
  43. Yin J, Albertson JD, Rigby JR, Porporato A (2015) Land and atmospheric controls on initiation and intensity of moist convection: CAPE dynamics and LCL crossings. Water Resour Res 51:8476–8493. CrossRefGoogle Scholar
  44. Zhang F, Odins AM, Nielsen-Gammon JW (2006) Mesoscale predictability of an extreme warm-season precipitation event. Wea Forecasting 21:149–166. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Astronomy and Atmospheric SciencesKyungpook National UniversityDaeguSouth Korea

Personalised recommendations