  This work continues the parametric investigation on the sampling nuances of
the Dynamic Mode Decomposition (DMD) under the Koopman analysis. Through
turbulent wakes, the investigation corroborated the generality of the universal
convergence states for all DMD implementations. It discovered the implications
of sampling range and resolution -- the determinants of the spectral
discretisation by discrete frequency bins and the highest resolved frequency,
respectively. The work reaffirmed the necessity of the Convergence state for
sampling independence, too. Results also suggested that the observables derived
from the same flow may contain dynamically distinct information, thus altering
the DMD output. The static pressure and vortex identification criteria are
optimal variables for characterising structural response and fluid excitation.
The pressure, velocity magnitude, and turbulence kinetic energy fields also
suffice for general applications, but the Reynolds stresses and velocity
components shall be avoided. Mean-subtraction is recommended for best
approximations of the Koopman eigen tuples. Furthermore, the parametric
investigation on truncation discovered some low-energy states that dictate a
system's temporal integrity. The best practice for order reduction is to avoid
truncation and employ dominant mode selection on a full-state subspace, though
large-degree truncation supports fair data reconstruction with low
computational cost. Finally, this work demonstrated the synthetic noise
resulting from pre-decomposition interpolation. In unavoidable interpolations
to increase the spatial dimension n, high-order schemes are recommended for
better retention of the original dynamics. Finally, the observations herein,
derived from inhomogeneous anisotropic turbulence, offer constructive
references for DMD on fluid systems, if not also others beyond fluid mechanics.
