Impact of high frequency waves on the ocean altimeter range bias

New aircraft observations are presented on the range determination error in satellite altimetry associated with ocean waves. Laser-based measurements of the cross correlation between the gravity wave slope and elevation are reported for the first time. These observations provide direct access to a long, O(10 m), gravity wave statistic central to nonlinear wave theory prediction of the altimeter sea state bias. Coincident Ka-band radar scattering data are used to estimate an electromagnetic (EM) range bias analogous to that in satellite altimetry. These data, along with ancillary wind and wave slope variance estimates, are used alongside existing theory to evaluate the extent of long- versus short-wave, O(cm), control of the bias. The longer wave bias contribution to the total EM bias is shown to range from 25 to as much as 100%. Moreover, on average the term is linearly related to wind speed and to the gravity wave slope variance, consistent with WNL theory. The EM bias associated with interactions between long and short waves is obtained assuming the effect is additive to the independently observed long-wave factor. This second component is also a substantial contributor, is observed to be quadratic in wind speed or wave slope, and dominates at moderate wind speeds. The behavior is shown to be consistent with EM bias prediction based in hydrodynamic modulation theory. Study implications for improved correction of the on-orbit satellite sea state bias are discussed

Physically Constrained Covariance Inflation from Location Uncertainty and Optimal Transportation

Motivated by the concept of ``location uncertainty", initially introduced in \cite{Memin2013FluidFD}, a scheme is sought to perturb the ``location" of a state variable at every forecast time step. Further considering Brenier's theorem \cite{Brenier1991}, asserting that the difference of two positive density fields on the same domain can be represented by a transportation map, perturbations are demonstrated to consistently define a SPDE from the original PDE. It ensues that certain quantities, up to the user, are conserved at every time step. Remarkably, derivations following both the SALT \cite{Holm2015VariationalPF} and LU \cite{Memin2013FluidFD, Resseguier2016GeophysicalFU} settings, can be recovered from this perturbation scheme. Still, it opens broader applicability since it does not explicitly rely on Lagrangian mechanics or Newton's laws of force. For illustration, a stochastic version of the thermal shallow water equation is presented

Champs Gaussiens conditionnels pour la modélisation inter-échelle de textures : Application à la super-résolution en télédétection satellitaire de l'océan

National audienceCet article s'intéresse à la modélisation et la simulation inter-échelle de textures. Nous considérons ici des modèles de champs gaussiens conditionnels obtenus comme solutions d'équations aux dérivées partielles stochastiques. Ces modèles sont utilisés pour la synthèse de texture non-stationnaire tout en ayant un contrôle sur les propriétés spectrales, statistiques et géométriques des champs générés. Nous montrons que ces modèles peuvent s'obtenir à l'aide d'une convolution non-stationnaire d'un bruit blanc gaussien par un opérateur linéaire associée à la la fonction de covariance, qui peut notamment prendre en considération des propriétés géométriques d'anisotropie. Les versions discrétisées de ces modèles correspondent à des modèles AR 2D, obtenus à l'aide de la représentation harmonisable de l'opérateur différentiel. Ces différents opérateurs sont explicités de manière analytique dans le cas des champs dits Matérn. L'apport de ces méthodes par rapport à des versions non paramétriques stationnaires est discuté. Une application à la super-résolution basée texture d'images satellitaires associées à des dynamiques turbulentes à la surface de l'océan permet de valider le modèle proposé

Sea surface salinity variability from a simplified mixed layer model of the global ocean

International audienceA bi-dimensional mixed layer model (MLM) of the global ocean is used to investigate the sea surface salinity (SSS) balance and variability at daily to seasonal scales. Thus a simulation over an average year is performed with daily climatological forcing fields. The forcing dataset combines air-sea fluxes from a meteorological model, geostrophic currents from satellite altimeters and in situ data for river run-offs, deep temperature and salinity. The model is based on the "slab mixed layer" formulation, which allows many simplifications in the vertical mixing representation, but requires an accurate estimate for the Mixed Layer Depth. Therefore, the model MLD is obtained from an original inversion technique, by adjusting the simulated temperature to input sea surface temperature (SST) data. The geographical distribution and seasonal variability of this "effective" MLD is validated against an in situ thermocline depth. This comparison proves the model results are consistent with observations, except at high latitudes and in some parts of the equatorial band. The salinity balance can then be analysed in all the remaining areas. The annual tendency and amplitude of each of the six processes included in the model are described, whilst providing some physical explanations. A map of the dominant process shows that freshwater flux controls SSS in most tropical areas, Ekman transport in Trades regions, geostrophic advection in equatorial jets, western boundary currents and the major part of subtropical gyres, while diapycnal mixing leads over the remaining subtropical areas and at higher latitudes. At a global scale, SSS variations are primarily caused by horizontal advection (46%), then vertical entrainment (24%), freshwater flux (22%) and lateral diffusion (8%). Finally, the simulated SSS variability is compared to an in situ climatology, in terms of distribution and seasonal variability. The overall agreement is satisfying, which confirms that the salinity balance is reliable. The simulation exhibits stronger gradients and higher variability, due to its fine resolution and high frequency forcing. Moreover, the SSS variability at daily scale can be investigated from the model, revealing patterns considerably different from the seasonal cycle. Within the perspective of the future satellite missions dedicated to SSS retrieval (SMOS and Aquarius/SAC-D), the MLM could be useful for determining calibration areas, as well as providing a first-guess estimate to inversion algorithms

The groupy wave model for simulating dynamical sea surface

International audience— Simulated radar observations of the sea surface dynamics as used in the MODENA project, are based on an original methodology for sea states : the " groupy " wave model (GWM). Random wave fields can have very different modulations but nearly identical spectra. Nevertheless, the response of a floating object to waves depends strongly on the likelihood of large wave encounters. Sea surface fluxes also depend on wave breaking and air-flow separation, both being consequences of large-amplitude events. So wave group structure is one key description to simulate radar clutter under various environmental and instrumental configurations. The GWM builds on random distributions of wave groups and conditionally distributed breaking waves over these groups. Each wave group travels across the simulated area, and breaking waves appear dynamically on the wave crest at the rear of a group, propagating and disappearing at the front of this group. The generation of sea states follows a prescribed sea wave directional spectrum, and any breaking wave statistical distribution such as Λ(c)d c describing the total length of breaker per unit area and time with phase speed between c and c + d c. Accordingly, the group density per surface unit can lead to very different sea state structures, and the results will be discussed

Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require great amount of labeled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improve the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms

Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery

Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave mode vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition as a preprocessing stage for SAR deep learning models. Our data centring approach surpassed the baseline by 0.7, obtaining state of the art on the TenGeoPSARwv data set. In addition, we empirically showed that subaperture decomposition could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models

The analog data assimilation: application to 20 years of altimetric data

International audienceThe reconstruction of geophysical dynamics remain key challenges in ocean, atmosphere and climate sciences. Data assimilation methods are the state-of-theart techniques to reconstruct the space-time dynamics from noisy and partial observations. They typically involve multiple runs of an explicit dynamical model and may have severe operational limitations, including the computational complexity, the lack of model consistante with respect to the observed data as well as modeling uncertainties. Here, we demonstrate how large amount of historical satellite data can open new avenues to address data assimilation issues, and to develop a fully data-driven assimilation. Assuming that a representative catalog of historical state trajectories is available, the key idea is to use the analog method to propose forecasts with no online evaluation of any physical model. The combination of these analog forecasts with observations resorts to classical stochastic filtering methods. For illustration of the proposed analog data assimilation, the brute force use of 20 years of altimetric data is demonstrated to reconstruct mesoscale sea surface dynamics