Meteorology and Atmospheric Physics

, Volume 131, Issue 1, pp 11–28 | Cite as

Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system

  • K. S. Singh
  • M. Mandal
  • Prasad K. BhaskaranEmail author
Original Paper


This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.



The authors sincerely acknowledge the IMD for providing the best-fit track data to validate model results, NCEP for providing PREBUFR, GFS analysis and forecasted data sets and NCAR for the WRF and its 3DVAR software. NASA is acknowledged for providing TRMM precipitation data sets. The CSIR is acknowledged for the funding the research activity. First author would like to express his special thanks to Prof. Ramesh Ramchandran, director of NCSCM and Dr. Purvaja Ramchandran, division chair, NCSCM, Ministry of Environment, Forest and Climate Change. We would like to appreciatively acknowledge the IIT Kharagpur for providing necessary facilities to conduct research work.


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Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • K. S. Singh
    • 1
    • 2
  • M. Mandal
    • 2
  • Prasad K. Bhaskaran
    • 3
    Email author
  1. 1.National Centre for Sustainable Coastal ManagementMinistry of Environment, Forest and Climate ChangeChennaiIndia
  2. 2.Centre for Oceans, Rivers, Atmosphere and Land ScienceIndian Institute of Technology, KharagpurKharagpurIndia
  3. 3.Ocean Engineering and Naval ArchitectureIndian Institute of Technology, KharagpurKharagpurIndia

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