  This study compares the skills of two numerical models having the same
horizontal resolution but based on different principles in representing meso-
and submesoscale features of ocean dynamics in the Lakshadweep Sea (North
Indian Ocean). The first model, titled LD20-NEMO, is based on solving primitive
equations using the NEMO (Nucleus for European Modelling of the Ocean)
modelling engine. The second one, titled LD20-SDD, uses a newer
Stochastic-Deterministic Downscaling method. Both models have 1/20o resolution
and use the outputs from a Global Ocean Physics Analysis and Forecast model at
1/12o resolution available from Copernicus Marine Service (CMEMS). The
LD20-NEMO uses only a 2D set of data from CMEMS as lateral boundary conditions.
The LD20-SDD consumes the full 3D set of data from CMEMS and exploits the
stochastic properties of these data to generate the downscaled field variables
at higher resolution than the parent model. The skills of the three models,
CMEMS, LD20-NEMO and LD20-SDD are assessed against remotely sensed and in-situ
observations for the four-year period 2015-2018. All models show similar skills
in reproducing temperature and salinity, however the SDD version performs
slightly better than the NEMO version. This difference in resolution is
particularly significant in simulation of vorticity and computation of the
share of the sea occupied by highly non-linear processes. While the NEMO and
SDD model show similar skill, the SDD model is more computationally efficient
than the NEMO model by a large margin.
