  Data-driven Reynolds-Averaged Navier-Stokes (RANS) turbulence closures are
increasing seen as a viable alternative to general-purpose RANS closures, when
LES reference data is available -- also in wind-energy. Parsimonious closures
with few, simple terms have advantages in terms of stability,
interpret-ability, and execution speed. However experience suggests that
closure model corrections need be made only in limited regions -- e.g.\ in the
near-wake of wind turbines and not in the majority of the flow. A parsimonious
model therefore must find a middle ground between precise corrections in the
wake, and zero corrections elsewhere. We attempt to resolve this impasse by
introducing a classifier to identify regions needing correction, and only fit
and apply our model correction there. We observe that such a classifier (which
must be computed only from RANS-available quantities) is straightforward to
construct, and accurate in operation. We further observe that the correction
models are significantly simpler (with fewer terms) when limited to the
identified regions than otherwise, but have similar or better accuracy. We
apply our framework to three flows consisting of multiple wind-turbines in
neutral conditions with interacting wakes.
