Impact of Variational Data Assimilation for Simulating Tropical Cyclones over Bay of Bengal Using WRF-ARW - Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change - page 239

1 mm/day on third day. Spatial correlation for model-predicted rainfall is slightly
better in 3DVAR on first day; 3DVAR correlated value far better on second
and third days and is significant on the fourth day than control experiment.
The RMSE and MAE are slightly less for 3DVAR than CTL for all four days
which in turn indicates better forecast.
5. Conclusions
The present study is an attempt to examine the impact of assimilation of
operationally used data sets (NCEP PREPBUFR) on the simulation of the track
and intensity of tropical cyclones in the Indian Ocean region. The prepbufr
data sets from NCEP ADP are assimilated using 3DVAR within the WRF-
ARW model for ten tropical cyclones. It has been found that experiments with
assimilation of conventional observations using 3DVAR provide large
differences in the initial fields given as initial conditions in each case.
Considerable decrease in initial position error as well as reduction in track
errors has been found. The average track errors varied as 50 km to 100 km
from 24 h to 96 h of integration in experiments using 3DVAR. The 3DVAR
experiments have shown that the errors in Central Pressure are reduced by 3
hPa and those in maximum winds are reduced by 18 m/s between control and
assimilation experiments leading to improvements in intensity estimation. The
assimilation experiments have also led to better representation of rainfall in
location and distribution. This study shows that the assimilation of global
observations used in operational forecasting enhance the initial conditions in
regional models and improve the forecasting skills of the latter for tropical
cyclone track and intensity and associated rainfall predictions.
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