Frontiers in Fine-Scale in situ Studies: Opportunities During the SWOT Fast Sampling Phase

d’Ovidio, Francesco; Pascual, Ananda; Wang, Jinbo; Doglioli, Andrea M.; Jing, Zhao; Moreau, Sébastien; Grégori, Gérald; Swart, Sebastiaan; Speich, Sabrina; Cyr, Frédéric; Legrésy, B.; Chao, Yi; Fu, L.; Morrow, Rosemary

doi:10.3389/fmars.2019.00168

Cited by 36 publications

(16 citation statements)

References 54 publications

(68 reference statements)

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“…The selected region belongs to the fast-sampling phase crossover in the western Mediterranean Sea. This is one of the regions selected for calibration/validation (Cal/Val) [30] in which in situ measurements have been made in the frame of SWOT [31]. To mitigate the computational complexity of the study and to avoid the presence of continents and islands, limited subregions of the SWOT swaths are sampled.…”

Section: Simulated Swot Datasetmentioning

confidence: 99%

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

et al. 2020

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In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60 ∘ simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of 10 4 and 10 3 for summer and winter, respectively. This is encouraging for the processing of the future SWOT data.

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Section: Simulated Swot Datasetmentioning

confidence: 99%

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

et al. 2020

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“…Optimal interpolation (OI) will be used for this analysis, as this method has been widely used to reconstruct in situ observations (e.g. Bretherton, et al, 1976;Davis, 1985;Le Traon, 1990, Gomis et al, 2001Pascual et al, 2004Pascual et al, , 2017Ruiz et al, 2019, Barceló-Llull et al, 2017. This analysis will be done in each region of study and using all models to provide robustness to the results.…”

Section: Scientific Goals and Subtasksmentioning

confidence: 99%

“…Optimal Interpolation is a powerful method of data analysis that has been widely used by oceanographers and meteorologists to estimate values of geophysical variables on a regular grid from irregularly sampled observations (e.g. Bretherton, et al, 1976;Davis, 1985;Le Traon, 1990, Gomis et al, 2001Pascual et al, 2004Pascual et al, , 2017Melnichenko et al, 2014, Barceló-Llull et al, 2017Ruiz et al, 2019). This technique determines a point-wise estimate of the interpolated field with minimum ensemble mean-square error, considering information about the variances and correlation functions of the estimated field and the observational data.…”

Section: Optimal Interpolationmentioning

confidence: 99%

Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine- scale structures

Barceló‐Llull¹,

Pascual²,

Cutolo³

et al. 2020

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“…Because of this spatial scale overlap between balanced and IGW motions, understanding the small-scale content of high-resolution observations is not trivial. In particular, the next generation of altimeters will use wide-swath radar interferometry in the Ka band, which will allow the forthcoming Surface Water Ocean Topography (SWOT) satellite to sample sea level spatial scales potentially down to 15 km (Fu and Ubelmann, 2013). Thus, the SWOT satellite will be able to provide more observational information in terms of balanced motions at the short ME range and long SM ranges.…”

Section: Introductionmentioning

confidence: 99%

Scale-dependent analysis of in situ observations in the mesoscale to submesoscale range around New Caledonia

et al. 2020

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Abstract. Small-scale ocean dynamics around New Caledonia (22∘ S) in the southwest Pacific Ocean occur in regions with substantial mesoscale eddies, complex bathymetry, complex intertwined currents, islands and strong internal tides. Using second-order structure functions applied to observational acoustic Doppler current profiler (ADCP) and thermosalinograph (TSG) datasets, these small-scale dynamics are characterised in the range of scales of 3–100 km in order to determine the turbulent regime at work. A Helmholtz decomposition is used to analyse the contribution of rotational and divergent motions. A surface-intensified regime is shown to be at work south and east of New Caledonia, involving substantial rotational motions such as submesoscale structures generated by mixed layer instabilities and frontogenesis. This regime is, however, absent north of New Caledonia, where mesoscale eddies are weaker and surface available potential energy is smaller at small scales. North of New Caledonia and below 200 m, in the regions south and east of New Caledonia, the dynamical regime at work could be explained by stratified turbulence as divergent and rotational motions have similar contribution, but weakly nonlinear interaction between inertia–gravity waves is also possible as structure functions get close to the empirical spectrum model for inertia–gravity waves. Seasonal variations of the available potential energy reservoir, associated with a change in the vertical profile rather than in horizontal density variance, suggest that submesoscale motions would also seasonally vary around New Caledonia. Overall, a loss of geostrophic balance is likely to occur at scales smaller than 10 km, where the contribution of divergent motions become significant.

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Frontiers in Fine-Scale in situ Studies: Opportunities During the SWOT Fast Sampling Phase

Cited by 36 publications

References 54 publications

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

Development of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission

Design of the Observing System Simulation Experiments with multi-platform in situ data and impact on fine- scale structures

Scale-dependent analysis of in situ observations in the mesoscale to submesoscale range around New Caledonia

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