Joint Estimation of Balanced Motions and Internal Tides From Future Wide‐Swath Altimetry

Abstract

Wide‐swath altimetry, for example, the Surface Water and Ocean Topography mission is expected to provide Sea Surface Height (SSH) measurements resolving scales of a few tens of kilometers. Over a large fraction of the globe, the SSH signal at these scales is essentially a superposition of a component due to balanced motions (BMs) and another component due to internal tides (ITs). Several oceanographic applications require the separation of these components and their mapping on regular grids. For that purpose, the paper introduces an alternating minimization algorithm that iteratively implements two data assimilation techniques, each specific to the mapping of one component: a quasi‐geostrophic model with Back‐and‐Forth Nudging for BMs, and a linear shallow‐water model with 4‐Dimensional Variational assimilation for ITs. The algorithm is tested with Observation System Simulation Experiments where the truth is provided by a primitive‐equation ocean model in an idealized configuration simulating a turbulent jet and mode‐one ITs. The algorithm reconstructs almost 80% of the variance of BMs and ITs, the remaining 20% being mostly due to dynamics that cannot be described by the simple models used. Importantly, in addition to the reconstruction of stationary ITs, the amplitude and phase of nonstationary ITs are reconstructed. Sensitivity experiments show that the quality of reconstruction significantly depends upon the timing of observations. Although idealized, this study represents a step forward towards the disentanglement of BMs and ITs signals from real wide‐swath altimetry data.

Internal Tide Surface Signature and Incoherence in the North Atlantic

“…Shorter decorrelation timescales, and especially for higher vertical modes which have a smaller group velocity (the group velocity roughly scales like the inverse mode number) will have a much shorter typical decorrelation lengthscale, down to a few hundreds of km (e.g., 400 km for C g = 1 m/s and T inc = 5 days), indicating that statistically based inverse method are unlikely to be able to reconstruct a significant part of the incoherent tide from altimeter data. These considerations support that using a model of the internal tide accounting for interactions with the mesoscale, as proposed by Kelly et al (2021) and Le Guillou et al (2021) may be key to circumvent these limitations.…”

Observed Structure of an Internal Tide Beam Over the Mid‐Atlantic Ridge

“…Second, they play a crucial role in the irreversible mixing of water masses. The first point has recently received a regained interest in the context of new satellite missions that unveil the ocean surface topography at unprecedented resolution (Le Guillou et al., 2021; Morrow et al., 2019). The second point has since been confirmed (St. Laurent & Garrett, 2002) and the community now has a clearer view on the lifecycle of internal tides (e.g., reviewed in Whalen et al., 2020; Musgrave et al., 2022), on their global characteristics and interactions within the internal wave continuum (Le Boyer & Alford, 2021; Pollmann, 2020), on the geography of their induced mixing (de Lavergne et al., 2019, 2020) and how this mixing impacts the circulation (e.g., reviewed in de Lavergne et al.…”

A Deep Learning Approach to Extract Internal Tides Scattered by Geostrophic Turbulence

“…This has motivated the community to regard the extraction of IT signals as an operation on high‐resolution 2D snapshots. Current methods rely on exploiting distinct spectral signatures of TBMs and internal waves (Torres et al., 2019), empirical modal mapping methods (Egbert & Erofeeva, 2021), dynamical relations to surface density fields (Ponte et al., 2017), or on data assimilation techniques (Le Guillou et al., 2021; Metref et al., 2020). A common element between these works and the present one is the circumvention of time series information, as motivated by the poor temporal resolution of SWOT.…”

“…As a result, they will produce spatially two–dimensional (2D) images and it will be possible to regard the extraction of IT signals as an operation on 2D snapshots. Recently proposed methods rely on exploiting distinct wavenumber spectral signatures of TBMs and internal waves (Torres et al., 2019), empirical modal mapping methods (Egbert & Erofeeva, 2021), dynamical relations to surface density fields (Ponte et al., 2017), or on data assimilation techniques (Metref et al., 2020; Le Guillou et al., 2021). Our goal is to propose an alternative method based on a deep–learning, image processing method.…”