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Abstract Physical processes behind flow‐topography interactions and turbulent transitions are essential for parameterization in numerical models. We examine how the Kuroshio cascades energy into turbulence upon passing over a seamount, employing a combination of shipboard measurements, tow‐yo microstructure profiling, and high‐resolution mooring. The seamount, spanning 5 km horizontally with two summits, interacts with the Kuroshio, whose flow speed ranges from 1 to 2 m s⁻¹, modulated by tides. The forward energy cascade process is commenced by forming a train of 2–3 nonlinear lee waves behind the summit with a wavelength of 0.5–1 km and an amplitude of 50–100 m. A train of Kelvin‐Helmholtz (KH) billows develops immediately below the lee waves and extends downstream, leading to enhanced turbulence. The turbulent kinetic energy dissipation rate isO(10⁻⁷–10⁻⁴) W kg⁻¹, varying in phase with the upstream flow speed modulated by tides. KH billows occur primarily at the lee wave's trailing edge, where the combined strong downstream shear and low‐stratification recirculation trigger the shear instability,Ri < 1/4. The recirculation also creates an overturn susceptible to gravitational instability. This scenario resembles the rotor, commonly found in atmospheric mountain waves but rarely observed in the ocean. A linear stability analysis further suggests that critical levels, where the KH instability extracts energy from the mean flow, are located predominantly at the strong shear layer of the lee wave's upwelling portion, coinciding with the upper boundary of the rotor. These novel observations may provide insights into flow‐topography interactions and improve physics‐based turbulence parameterization. 

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially α, β, and fast ϕ-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length (ARL) and upper bounds for average-detection-delay (ADD) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast ϕ-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially α/β-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.