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Fig. 6: Divergence at 850 hPa and 200 hPa from CTRL and DWR2
experiments at 0900 UTC.
and inner core convection especially in eye wall and rain band regions of the
cyclone. DWR wind data assimilation has significantly modified wind field.
As a result there is a remarkable increase of upper-level divergence and lower-
level convergence (negative divergence) in the storm vortex area. Thus cyclone
intensity has been improved by data assimilation of wind data.
The study reveals that cyclone structure is well simulated by assimilating
both radar reflectivity and wind data simultaneously. Although these results
are based on one cyclone but the influence of DWR data on cyclone intensity
is significant. This study also demonstrates successful coupling of data
assimilation package ADAS with WRF Model for Indian DWR data. Coupling
of data assimilation package ADAS with WRF Model will be tested for more
number of cyclone cases and implemented for assimilation Indian DWR data
in WRF Model in real time.
Acknowledgement
Authors are thankful to Guru Gobind Singh Indraprastha University, New Delhi
for their support to carry out this study. The authors are also grateful to the
Director General of Meteorology, IMD, New Delhi for encouragement to carry
out this work. Authors are also thankful to Radar unit at H/Q, DWR Kolkata,
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