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This work continues the parametric investigation on the sampling nuances ofthe Dynamic Mode Decomposition (DMD) under the Koopman analysis. Throughturbulent wakes, the investigation corroborated the generality of the universalconvergence states for all DMD implementations. It discovered the implicationsof sampling range and resolution -- the determinants of the spectraldiscretisation by discrete frequency bins and the highest resolved frequency,respectively. The work reaffirmed the necessity of the Convergence state forsampling independence, too. Results also suggested that the observables derivedfrom the same flow may contain dynamically distinct information, thus alteringthe DMD output. The static pressure and vortex identification criteria areoptimal variables for characterising structural response and fluid excitation.The pressure, velocity magnitude, and turbulence kinetic energy fields alsosuffice for general applications, but the Reynolds stresses and velocitycomponents shall be avoided. Mean-subtraction is recommended for bestapproximations of the Koopman eigen tuples. Furthermore, the parametricinvestigation on truncation discovered some low-energy states that dictate asystem's temporal integrity. The best practice for order reduction is to avoidtruncation and employ dominant mode selection on a full-state subspace, thoughlarge-degree truncation supports fair data reconstruction with lowcomputational cost. Finally, this work demonstrated the synthetic noiseresulting from pre-decomposition interpolation. In unavoidable interpolationsto increase the spatial dimension n, high-order schemes are recommended forbetter retention of the original dynamics. Finally, the observations herein,derived from inhomogeneous anisotropic turbulence, offer constructivereferences for DMD on fluid systems, if not also others beyond fluid mechanics.

A Parametric and Feasibility Study for Data Sampling of the Dynamic Mode Decomposition: Spectral Insights and Further Explorations