Meteorology and Atmospheric Physics

, Volume 131, Issue 3, pp 329–350 | Cite as

Influences of Appalachian orography on heavy rainfall and rainfall variability associated with the passage of hurricane Isabel by ensemble simulations

  • Guy OldakerIV
  • Liping LiuEmail author
  • Yuh-Lang Lin
Original Paper


This study focuses on the heavy rainfall event associated with hurricane Isabel’s (2003) passage over the Appalachian mountains of the eastern United States. Specifically, an ensemble consisting of two groups of simulations using the Weather Research and Forecasting model (WRF), with and without topography, is performed to investigate the orographic influences on heavy rainfall and rainfall variability. In general, the simulated ensemble mean with full terrain is able to reproduce the key observed 24-h rainfall amount and distribution, while the flat-terrain mean lacks in this respect. In fact, 30-h rainfall amounts are reduced by 75% with the removal of topography. Rainfall variability is also significantly increased with the presence of orography. Further analysis shows that the complex interaction between the hurricane and terrain along with contributions from varied microphysics, cumulus parametrization, and planetary boundary layer schemes have a pronounced effect on rainfall and rainfall variability. This study follows closely with a previous study, but for a different TC case of Isabel (2003). It is an important sensitivity test for a different TC in a very different environment. This study reveals that the rainfall variability behaves similarly, even with different settings of the environment.



This study is supported by the National Science Foundation Awards AGS-1265783, OCI-1126543, and CNS-1429464.


  1. Fang X, Kuo Y-H, Wang A (2011) The impacts of Taiwan topography on the predictability of Typhoon Morakot’s record-breaking rainfall: a high-resolution ensemble simulation. Weather Forecast 26:613–633CrossRefGoogle Scholar
  2. Ferrier BS (1994) A double-moment multiple phase four-class bulk ice scheme. Part I: Description. J Atmos Sci 51:249–280CrossRefGoogle Scholar
  3. Hong S-Y, Dudhia J, Chen S-H (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  4. Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341CrossRefGoogle Scholar
  5. Houtekamer PL (1993) Global and local skill forecasts. Mon Weather Rev 121:1834–1846CrossRefGoogle Scholar
  6. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon Weather Rev 122:927–945CrossRefGoogle Scholar
  7. Janjic ZI (2000) Comments on “Development and evaluation of a convection scheme for use in climate models”. J Atmos Sci 57:3686CrossRefGoogle Scholar
  8. Janjic ZI (2002) Nonsingular Implementation of the Mellor–Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note 437Google Scholar
  9. Jolliffe IT, Stephenson DB (2011) Forecast verification: a practitioner’s guide in atmospheric science, 2nd edn. Wiley, Hoboken, NJCrossRefGoogle Scholar
  10. Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  11. Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  12. Medina MA, Reidand JC, Carpenter RH (2004) Physiography of North Carolina. North Carolina Geological Survey, Division of Land Resources. Accessed 29 Dec 2007Google Scholar
  13. Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Sp Phys 20:851–875CrossRefGoogle Scholar
  14. Morrison H, Thompson G, Tatarskii V (2009) Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one- and two-moment schemes. Mon Weather Rev 137:991–1007CrossRefGoogle Scholar
  15. NOAA (2005) Hurricane Isabel assessment: review of hurricane evacuation study products and other aspects of the National Hurricane Mitigation and Preparedness Program (NHMPP) in the context of the Hurricane Isabel responseGoogle Scholar
  16. NWS (2003) Service assessment: Hurricane Isabel September 18–19, 2003. National Weather Service, NOAA, Silver SpringGoogle Scholar
  17. Rostom R, Lin Y-L (2015) Control parameters for track continuity of cyclones passing over the southern-central Appalachian mountains. Weather Forecast 30:1429–1449CrossRefGoogle Scholar
  18. Sheather SJ (2009) A modern approach to regression with R. Springer, New YorkCrossRefGoogle Scholar
  19. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR technical note-475 + STRGoogle Scholar
  20. Tao WK, Simpson J (1993) The Goddard cumulus ensemble model. Part I: model description. Terr Atmos Ocean Sci 4:35–72CrossRefGoogle Scholar
  21. Tao WK, Shi JJ, Chen SS, Lang S, Lin PL, Hong SY, Peters-Lidard C, Hou A (2011) The impact of microphysical schemes on hurricane intensity and track. Asia Pac J Atmos Sci 47(1):1–6CrossRefGoogle Scholar
  22. Thompson GR, Rasmussen M, Manning K (2004) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: description and sensitivity analysis. Mon Weather Rev 132:519–542CrossRefGoogle Scholar
  23. Unisys Weather (2015) Hurricane Isabel 2003. Accessed Sep 2015
  24. Whitaker JS, Loughe AF (1998) The relationship between ensemble spread and ensemble mean skill. Mon Weather Rev 126:3292–3302CrossRefGoogle Scholar
  25. Wobus RL, Kalnay E (1995) Three years of operational prediction of forecast skill at NMC. Mon Weather Rev 123:2132–2147CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of MathematicsNorth Carolina A&T State UniversityGreensboroUSA
  2. 2.Department of PhysicsNorth Carolina A&T State UniversityGreensboroUSA
  3. 3.Department of Energy and Environmental SystemsNorth Carolina A&T State UniversityGreensboroUSA

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