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Natural Hazards

, Volume 76, Issue 3, pp 1473–1495 | Cite as

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

  • Tanvir IslamEmail author
  • Prashant K. Srivastava
  • Miguel A. Rico-Ramirez
  • Qiang Dai
  • Manika Gupta
  • Sudhir K. Singh
Original Paper

Abstract

The Weather Research and Forecasting (WRF) model’s Advanced Research WRF (ARW) dynamic solver is one of the most popular regional numerical weather prediction models being used by operational and research personnel. In this study, we simulate a tropical cyclone to reproduce the track direction and strength of the storm that formed at low latitudes in the West Pacific Ocean. The cyclone is known as “Haiyan” and assessed as category-5 equivalent super typhoon status due to its strong sustained winds and gusts, making it the strongest tropical cyclone in the region. We study the sensitivity of three different model physics options: the microphysics schemes, the planetary boundary layer schemes, and the impact of cumulus parameterization schemes. The realism of the cyclone simulation for different physics options is assessed through the comparison between the model outputs and the best track data, which are taken from the Japan Meteorological Agency. The experimental model simulations are carried out with two different global datasets: the ERA-Interim analysis from the European Centre for Medium Range Weather Forecasts and NCEP GFS forecast data, as initialization and boundary conditions. In addition, wind–pressure relationships are developed for different physics combination runs. Verification results associated with the model physics and boundary condition are discussed in this article. Overall, irrespective of the physics sensitivity, while the WRF simulation performs well in predicting the track propagation of the typhoon, substantial underestimation is seen in the intensity prediction.

Keywords

Hurricane Track and intensity forecast Physics parameterizations Numerical weather prediction (NWP) Weather mesoscale model Tropical storm Extreme events ECMWF and GFS 

Notes

Acknowledgments

The authors would like to acknowledge the European Centre for Medium-Range Weather Forecasts (2009), ERA-Interim Project, http://rda.ucar.edu/datasets/ds627.0/, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO. The GFS data used in this effort were acquired from the National Oceanic and Atmospheric Administration (NOAA). The authors also acknowledge the Japan Meteorological Agency (JMA) for providing the best track data. The views expressed here are those of the authors solely and do not constitute a statement of policy, decision, or position on behalf of NOAA, NASA, or the authors’ affiliated institutions.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Tanvir Islam
    • 1
    • 2
    • 3
    Email author
  • Prashant K. Srivastava
    • 3
    • 4
    • 5
  • Miguel A. Rico-Ramirez
    • 3
  • Qiang Dai
    • 3
  • Manika Gupta
    • 6
  • Sudhir K. Singh
    • 7
  1. 1.NOAA/NESDIS/STAR, NOAA Center for Weather and Climate PredictionCollege ParkUSA
  2. 2.Cooperative Institute for Research in the AtmosphereColorado State UniversityFort CollinsUSA
  3. 3.Department of Civil EngineeringUniversity of BristolBristolUK
  4. 4.NASA Goddard Space Flight CenterGreenbeltUSA
  5. 5.Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA
  6. 6.Department of Civil EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  7. 7.Centre of Atmospheric and Ocean StudiesUniversity of AllahabadAllahabadIndia

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