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Turbulent energy cascade and intermittency are very important characteristics in the turbulent energy evolution process. However, understanding the temporal–spatial features of kinetic energy transfer and quantifying the correlations between different scales of turbulent energy remains an outstanding challenge. To deeply understand the spatial–temporal features in the energy transfer process, an integrated features identification and extraction method is proposed to quantitatively investigate the correlations using the ocean shear turbulence measured by an autonomous vertical reciprocating profiler (AVRP). The proposed integrated method mainly contains two parallel features analysis modules: first, temporal multiscale features structures of the nonlinear and nonstationary turbulent cascade are identified by Variational Mode Decomposition (VMD); then, the ocean microstructure shear fluctuation data are decomposed into a series of intrinsic mode functions (IMFs), which are characterized by different time scales and frequency bandwidths. The local features of energy transfer are identified when the local intermittency peaks overlap and the phase-synchronization case occurs between two neighboring scales; second, the spatial statistical characteristics of the turbulent energy dissipation are quantitatively studied. The cumulative probability distribution functions (CPDFs) of kinetic energy dissipation are approximated well by a normal distribution, indicating that the turbulent dissipation process exhibits a robust spatial scaling correlation and a few intense dissipation locations dominate the integrated process. Finally, the proposed integrated method is evaluated through experiments using an autonomous vertical reciprocating profiler deployed in the South China Sea. Preliminary experimental results show that the proposed novel method is useful to improve our understanding of turbulent energy transfer and the evolution process in the ocean dynamic systems.

An Integrated Spatio-Temporal Features Analysis Approach for Ocean Turbulence Using an Autonomous Vertical Profiler