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Assimilation of Doppler Weather Radar Data in WRF Model for Numerical Simulation of Structure of Cyclone Aila (2009) of the Bay of Bengal at the Time of Landfall

  • Kuldeep Srivastava
  • Rashmi Bhardwaj
  • S. K. Roy Bhowmik

Abstract

For effective forecasting of tropical cyclone (TC) it is very important to have accurate initial structure of cyclone in numerical models. Assimilation of local data such as Doppler Weather Radar (DWR) data in numerical weather prediction (NWP) models has potential to improve the initial and boundary condition for the prediction of land falling cyclones. Xiao et al. (2005, 2007) carried out study on the assimilation of DWR radial wind and reflectivity into NWP model using the 3-dimensional variational data assimilation (3DVAR) system for the heavy rainfall events. A number of case studies on the positive impact of DWR radial wind and reflectivity observations in the assimilation cycle of Advanced Regional Prediction System (ARPS) were documented by Xue et al. (2003). Kun Zhao and Ming Xue (2009) studied the impact of radar data on the analysis and prediction of the structure, intensity and track of land falling Hurricane Ike-2008, at a cloud-resolving resolution. The hurricane landfall, intensification and weakening during the simulation period are well captured by assimilating both airborne Doppler radar reflectivity and wind data (Zhaoxia et al., 2009). The Bratseth successive correction technique and cloud analysis are part of ARPS Model developed by Center for Analysis and Prediction of Storms (CAPS), Oklahoma University, USA (Bratseth, 1986; Brewster, 1996). The ARPS data assimilation system (ADAS) and cloud analysis technique have capability to be applied for assimilation of radar data in Weather Research and Forecasting (WRF) Model.

Keywords

Data Assimilation Radar Data Radar Reflectivity Doppler Weather Radar Advanced Regional Prediction System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Capital Publishing Company 2014

Authors and Affiliations

  • Kuldeep Srivastava
    • 1
  • Rashmi Bhardwaj
    • 2
  • S. K. Roy Bhowmik
    • 1
  1. 1.India Meteorological DepartmentNew DelhiIndia
  2. 2.Guru Gobind Singh Indraprastha UniversityDwarka, New DelhiIndia

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