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\begin{document}

\title{Intercomparison of the Performance of Four Data Assimilation Schemes in
a Limited-Area Model on Forecasts of an Extreme Rainfall Event over the
Himalayas}



\author[1]{Rakesh S}%
\author[1]{Govindan Kutty}%
\affil[1]{Indian Institute of Space Science and Technology}%


\vspace{-1em}



  
  \date{May 15, 2024}


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\begin{abstract}
This study compares the performance of four data assimilation (DA)
systems: Ensemble Adjustment Kalman Filter (EAKF), Variational
(3DVAR/4DVAR), and Hybrid ensemble-3DVAR (HYBRID) in the Weather
Research and Forecast (WRF) model. A heavy rainfall event that produced
notorious floods in the Uttarakhand over the Himalayan region is
considered. Observations are assimilated at every 6 h interval and all
the conventional observations including cloud tracked-wind from the
satellite are used. The forecast initialized from the analysis of four
DA systems at different lead times is evaluated. A non-cycled nested
assimilation strategy that provides advantages of increased resolution
in the DA system is tested. The results indicate that 4DVAR experiments
produce more skillful forecasts for wind while both 4DVAR and EAKF
experiments show improvement for upper tropospheric temperature
forecasts as compared to the other experiments. The evaluation of
rainfall forecast depicts that the 4DVAR DA system has outperformed the
other DA systems when the effect of high-resolution assimilation is
mimicked in the system using the nested assimilation strategy. Further
analysis of the event indicates that an early merging of the southward
protruding trough with the westward-moving monsoon depression has
resulted in stronger southeastward flow in EAKF and HYBRID experiments,
which is suggested as a potential reason for enhanced precipitation over
the Uttarakhand in both the experiments.%
\end{abstract}%



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