  This study assesses a Continuous Data Assimilation (CDA)
dynamical-downscaling algorithm for enhancing the simulation of the Indian
summer monsoon (ISM) system. CDA is a mathematically rigorous technique that
has been recently introduced to constrain the large-scale features of
high-resolution atmospheric models with coarse spatial scale data. It is
similar to spectral nudging but does not require any spectral decomposition for
scales separation. This is expected to be particularly relevant for ISM, which
involves various interactions between large-scale circulations and regional
physical processes. Along with a control simulation, several downscaling
simulations were conducted with the Weather Research and Forecasting (WRF)
model using CDA, spectral (retaining different wavenumbers) and grid nudging
for three ISM seasons: normal (2016), excess (2013), and drought (2009). The
simulations are nested within the NCEP Final Analysis and the model outputs are
evaluated against the observations. Compared to grid and spectral nudging, the
simulations using CDA produce enhanced ISM features over the Indian
subcontinent including the low-level jet, tropical easterly jet, easterly wind
shear, and rainfall distributions for all investigated ISM seasons. The major
ISM processes, in particular the monsoon inversion over the Arabian Sea,
tropospheric temperature gradients and moist static energy over central India,
and zonal wind shear over the monsoon region, are all better simulated with
CDA. Spectral nudging outputs are found to be sensitive to the choice of the
wavenumber, requiring careful tuning to provide robust simulations of the ISM
system. In contrast, control and grid nudging generally fail to well reproduce
some of the main ISM features.
