Blending drifters and altimetric data to estimate surface currents: Application in the Levantine Mediterranean and objective validation with different data types

Baaklini, Georges; Issa, Leila; Fakhri, Milad; Brajard, Julien; Fifani, Gina; Menna, Milena; Taupier‐Letage, Isabelle; Bosse, Anthony; Mortier, Laurent

doi:10.1016/j.ocemod.2021.101850

Cited by 10 publications

(10 citation statements)

References 54 publications

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“…The ever-increasing availability of observation data and numerical simulations also greatly contribute to the development and evaluation of learning-based and data-driven approaches as illustrated by the considered experimental setting based on an open data challenge 5 . We could apply and extend the proposed framework to other space-time geophysical products such as ocean colour [51,44], sea surface turbidity [50,42], sea and land surface temperature [5], sea surface currents [11,41]... Future challenges also involve joint calibration and interpolation issues for future satellite missions [22] as well as multimodal synergies between satellite data and other remote sensing and in situ data sources such as drifters [45,4], underwater acoustics data [8], moored buoys [23], argo profilers [13,17]... Especially, the latter might provide new ways to better monitor the interior of the ocean which cannot be directly observed from space.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the previous experiments, we synthesize the performance of the best configurations of the proposed multimodal framework with respect to that of state-of-the-art schemes according to the benchmarking framework presented in [35]. As detailed in the associated data challenge 4 , wee first include approaches which only rely on altimetry data. They include the operational optimal-interpolation-based method (DUACS) [46], model-driven interpolations using variational data assimilation schemes [35,49] and a multiscale interpolation approach [48].…”

Section: Comparison To State-of-the-art Schemesmentioning

confidence: 99%

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Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies

Fablet¹,

Febvre²,

Chapron³

2022

Preprint

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The space-time reconstruction of ocean dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling of the sea surface. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed 4DVarNet schemes combine a variational formulation involving trainable observation and a priori terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the application and extension of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison To State-of-the-art Schemesmentioning

confidence: 99%

Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies

Fablet¹,

Febvre²,

Chapron³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is a variational approach, for which the altimetry is corrected by matching observed drifter positions with those predicted by an advection model (Issa et al, 2016). The method proved its efficiency in providing an improved representation of the surface circulation along and around the drifters' trajectories, especially in high-vorticity areas (Baaklini et al, 2021).…”

Section: Vortex-dominated Cluster Analysismentioning

confidence: 99%

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

Baaklini

Hourany²,

Fakhri³

et al. 2022

Ocean Sci.

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Abstract. The eastern Mediterranean surface circulation is highly energetic and composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping the general circulation is yet unknown. In the following paper, we use an unsupervised neural network clustering method to analyze the long-term variability of the different mesoscale structures. We decompose 26 years of altimetric data into clusters reflecting different circulation patterns of weak and strong flows with either strain or vortex-dominated velocities. The vortex-dominated cluster is more persistent in the western part of the basin, which is more active than the eastern part due to the strong flow along the coast, interacting with the extended bathymetry and engendering continuous instabilities. The cluster that reflects a weak flow dominated the middle of the basin, including the Mid-Mediterranean Jet (MMJ) pathway. However, the temporal analysis shows a frequent and intermittent occurrence of a strong flow in the middle of the basin, which could explain the previous contradictory assessment of MMJ existence using in-situ observations. Moreover, we prove that the Levantine Sea is becoming more and more energetic as the activity of the main mesoscale features is showing a positive trend.

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“…In this coastal region, altimetry seemed insufficient for the detection of near-shore fronts where the spilled oil was observed (the red slicks in Figure 9c). This is not surprising, as one limitation for satellite altimeters is their inaccuracy near the land shore (at least 5-10 km from the coast) that increases in regions of small Rossby radius such as in the Mediterranean [55][56][57][58][59]. For a more reliable near-shore analysis in the southeastern Mediterranean basin, we chose the data assimilating model analysis from the Mediterranean Forecast System (MFS).…”

Section: Regional Application Of Lagrangian Fronts On the East Mediterranean Pollution: Coastal Dynamics And Strong Windmentioning

confidence: 99%

Drifting Speed of Lagrangian Fronts and Oil Spill Dispersal at the Ocean Surface

et al. 2021

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Due to its dire impacts on marine life, public health, and socio-economic services, oil spills require an immediate response. Effective action starts with good knowledge of the ocean dynamics and circulation, from which Lagrangian methods derive key information on the dispersal pathways present in the contaminated region. However, precise assessments of the capacity of Lagrangian methods in real contamination cases remain rare and limited to large slicks spanning several hundreds of km. Here we address this knowledge gap and consider two medium-scale (tens of km wide) events of oil in contrasting conditions: an offshore case (East China Sea, 2018) and a recent near-coastal one (East Mediterranean, 2021). Our comparison between oil slicks and Lagrangian diagnostics derived from near-real-time velocity fields shows that the calculation of Lagrangian fronts is, in general, more robust to errors in the velocity fields and more informative on the dispersion pathways than the direct advection of a numerical tracer. The inclusion of the effect of wind is also found to be essential, being capable of suddenly breaking Lagrangian transport barriers. Finally, we show that a usually neglected Lagrangian quantity, the Lyapunov vector, can be exploited to predict the front drifting speed, and in turn, its future location over a few days, on the basis of near-real-time information alone. These results may be of special relevance in the context of next-generation altimetry missions that are expected to provide highly resolved and precise near-real-time velocity fields for both open ocean and coastal regions.

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Blending drifters and altimetric data to estimate surface currents: Application in the Levantine Mediterranean and objective validation with different data types

Cited by 10 publications

References 54 publications

Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies

Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

Drifting Speed of Lagrangian Fronts and Oil Spill Dispersal at the Ocean Surface

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