  Bluff body wakes in stratified fluids are known to exhibit a rich range of
dynamic behavior that can be categorized into different regimes based on
Reynolds number ($Re$) and Froude number ($Fr$). Topological differences in
wake structure across these different regimes have been clarified recently
through the use of Dynamic Mode Decomposition (DMD) on Direct Numerical
Simulation (DNS) and laboratory data for a sphere in a stratified fluid for
$Re\in [200,1000]$ and $Fr\in[0.5,16]$. In this work, we attempt to identify
the dynamic regime from limited measurement data in a stratified wake with
(nominally) unknown $Re$ and $Fr$. A large database of candidate basis
functions is compiled by pooling the DMD modes obtained in prior DNS. A sparse
model is built using the Forward Regression with Orthogonal Least Squares
(FROLS) algorithm, which sequentially identifies DMD modes that best represent
the data and calibrates their amplitude and phase. After calibration, the
velocity field can be reconstructed using a weighted combination of the
dominant DMD modes. The dynamic regime for the measurements is estimated via a
projection-weighted average of $Re$ and $Fr$ corresponding to the identified
modes. Regime identification is carried out from a limited number of 2D
velocity snapshots from numerical and experimental datasets, as well as 3 point
measurements in the wake of the body. A metric to assess confidence is
introduced based on the observed predictive capability. This approach holds
promise for the implementation of data-driven fluid pattern classifiers.
