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

Tanvir Islam (), Prashant Srivastava, Miguel Rico-Ramirez, Qiang Dai, Manika Gupta and Sudhir Singh

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 76, issue 3, 1473-1495

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. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Hurricane; Track and intensity forecast; Physics parameterizations; Numerical weather prediction (NWP); Weather mesoscale model; Tropical storm; Extreme events; ECMWF and GFS (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-014-1494-8

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