2022
DOI: 10.1029/2022jc018598
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A Directional Decomposition Method to Estimate the Reflection and Transmission of Nonlinear Internal Waves Over a Slope

Abstract: Reflection and dissipation of NLIWs are crucial physical processes, which have a significant impact on the spatial features of wave energy along the sloping boundaries of the world's oceans (Boegman et al., 2005). These processes over a continental slope result in a complicated internal wave (IW) field comprising waves propagating in multiple directions with various frequencies and wavelengths (Nash et al., 2004).

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Cited by 2 publications
(4 citation statements)
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“…We also employed the Directional Fourier Filter (DFF) method [42,43] to decompose the altimetric signals induced by internal tides propagating in different directions. This method is suitable for grid-based data, and has been proved to be effective for the MIOST-IT dataset in the SCS [23].…”
Section: Directional Fourier Filter Methodsmentioning
confidence: 99%
“…We also employed the Directional Fourier Filter (DFF) method [42,43] to decompose the altimetric signals induced by internal tides propagating in different directions. This method is suitable for grid-based data, and has been proved to be effective for the MIOST-IT dataset in the SCS [23].…”
Section: Directional Fourier Filter Methodsmentioning
confidence: 99%
“…To quantify the IT energy flux reaching and leaving the USWC margin, we directionally decompose the complex IT signals near the USWC with a Discrete Fourier Transform (DFT) technique (Gong et al., 2021, 2022; Mercier et al., 2008; Siyanbola et al., 2023). The depth‐integrated IT energy flux can be expressed as boldF=H0ponbolduondzboldFon+H0poffbolduoffdzboldFoff+H0poffbolduondz+H0ponbolduoffdzboldFnormalx, $\mathbf{F}=\underset{{\mathbf{F}}_{\text{on}}}{\underbrace{\int \nolimits_{-H}^{0}\langle {p}_{\text{on}}^{\prime }{\mathbf{u}}_{\text{on}}^{\prime }\mathrm{d}z\rangle }}+\underset{{\mathbf{F}}_{\text{off}}}{\underbrace{\int \nolimits_{-H}^{0}\langle {p}_{\text{off}}^{\prime }{\mathbf{u}}_{\text{off}}^{\prime }\mathrm{d}z\rangle }}+\underset{{\mathbf{F}}_{\mathrm{x}}}{\underbrace{\int \nolimits_{-H}^{0}\langle {p}_{\text{off}}^{\prime }{\mathbf{u}}_{\text{on}}^{\prime }\mathrm{d}z\rangle +\int \nolimits_{-H}^{0}\langle {p}_{\text{on}}^{\prime }{\mathbf{u}}_{\text{off}}^{\prime }\mathrm{d}z\rangle }},$ where F on , F off , and F x are the time‐mean onshore (eastward), offshore (westward) propagating, and the cross‐term energy fluxes, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…To quantify the IT energy flux reaching and leaving the USWC margin, we directionally decompose the complex IT signals near the USWC with a Discrete Fourier Transform (DFT) technique (Gong et al, 2021(Gong et al, , 2022Mercier et al, 2008;Siyanbola et al, 2023). The depth-integrated IT energy flux can be expressed as…”
Section: It Energy Flux Directional Decompositionmentioning
confidence: 99%
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