Using colocated ASCAT and ECMWF winds, a careful global analysis of ENVISAT and Sentinel‐1 synthetic aperture radar (SAR) measurements helps to refine, at medium resolution (tens of kilometers) and ...especially for HH configuration, a C‐band geophysical model function (GMF, i.e., C‐SARMOD) to analyze wind sensitivity for different incidence and azimuth angles. Results unify major findings from previous global and case studies for polarization ratio (PR, VV/HH), polarization difference (PD, VV‐HH), and cross‐polarization (CP). At lower level than standard two‐scale predictions, PR increases with increasing incidence angle and decreases with increasing wind speed. PR further exhibits a strong azimuthal modulation, with maximum values in downwind configurations. The PD azimuth modulation is found more pronounced for VV than HH (VV being larger than HH), reaching maximum values for wind speed about 10 m/s. CP signals decrease with incidence angle but increase with wind speed, especially beyond 10 m/s, with no evidence of saturation. Remarkably, this also applies to HH crosswind measurements. This comparable high wind sensitivity for both CP and HH crosswind signals, with a clear departure from PD ones, can be related to the onset of vigorous breaking events, large enough to impact in‐plane and out‐of‐plane local tilts. Considering that VV polarization best maximizes the polarized resonant contribution, combined CP and VV wide swath SAR observations can thus have the potential to efficiently map and contrast local directional aspects.
Key Points:
First C‐Band GMF in VV and HH and signals in cross‐polarization from Sentinel‐1 A are presented
Results unify major findings from literature for polarization combination and cross‐polarization
Comparable wind sensitivity for cross‐pol and HH crosswind signals is attributed to breaking events
Ocean currents can strongly impact the propagation of swell systems. Satellite altimetry routinely provides measurements of ocean surface significant wave heights (Hs). A self‐consistent space‐scale ...decomposition is applied to Hs measurements obtained from different altimeters. This method helps reveal overlooked statistical properties at scales less than 100 km, where mesoscale and submesoscale upper ocean circulation drives a significant part of the variability in the coupled ocean‐atmosphere system. In particular, systematic signatures related to wave‐current interactions are clear at global and regional scales. In the Agulhas current system, the proposed space‐scale decomposition further reveals organized and persistent patterns. To leading order, the redistribution of swell energy follows the cumulative impact of the large‐scale current vorticity field on wave train kinematics. This relationship causes significant swell ray deflection and localized trapping phenomena, which are adequately captured by altimeter measurements.
Plain Language Summary
Long and energetic surface waves radiating from distant storms may eventually cover a full ocean basin with a lifetime that can extend over a few weeks. Chances are that these swell fields will propagate over regions characterized by strong ambiant upper ocean currents. Resulting interactions can then trigger sea state variability, including the formation of severe crossing sea conditions. To better document these anticipated effects at global and regional scales, a new methodology is applied to best exploit multisatellite altimeter measurements. As obtained, the resulting augmented data sets provide an unprecedented evidence of the co‐variability of surface waves and currents over all ocean basins. More regionally, the analysis shows that persistent and localized sea state anomalies in the Agulhas current region are well explained by swell refraction and focusing effects in the variable current stream. The proposed methodology opens new perspectives for studies and applications combining numerical modeling and satellite observations.
Key Points
Long‐term altimeter sea state measurements exhibit persistent variations linked to surface current gradients
A self‐consistent space‐scale decomposition unveils rich statistical properties at small oceanic scales
Swell refraction and advection processes explain global and regional wave climate variability
This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched ...on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range 70-500 m is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.
Strong ocean currents can modify the height and shape of ocean waves, possibly causing extreme sea states in particular conditions. The risk of extreme waves is a known hazard in the shipping routes ...crossing some of the main current systems. Modeling surface current interactions in standard wave numerical models is an active area of research that benefits from the increased availability and accuracy of satellite observations. We report a typical case of a swell system propagating in the Agulhas current, using wind and sea state measurements from several satellites, jointly with state of the art analytical and numerical modeling of wave-current interactions. In particular, Synthetic Aperture Radar and altimeter measurements are used to show the evolution of the swell train and resulting local extreme waves. A ray tracing analysis shows that the significant wave height variability at scales <~100 km is well associated with the current vorticity patterns. Predictions of the WAVEWATCH III numerical model in a version that accounts for wave-current interactions are consistent with observations, although their effects are still under-predicted in the present configuration. From altimeter measurements, very large significant wave height gradients are shown to be well captured, and also associated with the current vorticity patterns at global scale.
•Satellite and models give a description of swell refraction by the Agulhas current.•Mesoscale wave height variability is forced by surface current gradients.•Altimeter data show the climatological link between sea state and currents.
Learning Variational Data Assimilation Models and Solvers Fablet, R.; Chapron, B.; Drumetz, L. ...
Journal of advances in modeling earth systems,
October 2021, 2021-10-00, 20211001, 2021-10, 2021-10-01, Volume:
13, Issue:
10
Journal Article
Peer reviewed
Open access
Data assimilation is a key component of operational systems and scientific studies for the understanding, modeling, forecasting and reconstruction of earth systems informed by observation data. Here, ...we investigate how physics‐informed deep learning may provide new means to revisit data assimilation problems. We develop a so‐called end‐to‐end learning approach, which explicitly relies on a variational data assimilation formulation. Using automatic differentiation embedded in deep learning framework, the key novelty of the proposed physics‐informed approach is to allow the joint training of the representation of the dynamical process of interest as well as of the solver of the data assimilation problem. We may perform this joint training using both supervised and unsupervised strategies. Our numerical experiments on Lorenz‐63 and Lorenz‐96 systems report significant gain w.r.t. a classic gradient‐based minimization of the variational cost both in terms of reconstruction performance and optimization complexity. Intriguingly, we also show that the variational models issued from the true Lorenz‐63 and Lorenz‐96 ODE representations may not lead to the best reconstruction performance. We believe these results may open new research avenues for the specification of assimilation models for earth systems, both to speed‐up the inversion problem with trainable solvers but possibly more importantly in the way data assimilation systems are designed, for instance regarding the representation of geophysical dynamics.
Plain Language Summary
Data assimilation is a key component in the modeling of earth systems to simulate their dynamics, forecast their evolution in the short‐term or the long‐term as well as to reconstruct earth systems' states from observation data. State‐of‐the‐art data assimilation schemes generally blend prior knowledge on the underlying governing laws with available observation data. Here, we turn data assimilation into a physics‐informed machine learning problem. Within a differentiable framework, we can learn from data not only a data assimilation solver but also jointly some representation of the inverse problem. Numerical experiments support the relevance of this end‐to‐end approach for chaotic dynamics informed by noisy and irregularly‐sampled observations. This opens new research avenues for the design of physics‐informed and data‐constrained simulation, forecasting and reconstruction schemes for earth systems.
Key Points
We develop a end‐to‐end neural architecture to design variational data assimilation models and solvers
We may jointly calibrate all the trainable components of the proposed scheme to optimize a data assimilation performance criterion
Applied to Lorenz‐63 and Lorenz‐96 case‐studies, the proposed approach may improve and speed up the reconstruction performance
Models under location uncertainty are derived assuming that a component of the velocity is uncorrelated in time. The material derivative is accordingly modified to include an advection correction, ...inhomogeneous and anisotropic diffusion terms and a multiplicative noise contribution. In this paper, simplified geophysical dynamics are derived from a Boussinesq model under location uncertainty. Invoking usual scaling approximations and a moderate influence of the subgrid terms, stochastic formulations are obtained for the stratified Quasi-Geostrophy and the Surface Quasi-Geostrophy models. Based on numerical simulations, benefits of the proposed stochastic formalism are demonstrated. A single realization of models under location uncertainty can restore small-scale structures. An ensemble of realizations further helps to assess model error prediction and outperforms perturbed deterministic models by one order of magnitude. Such a high uncertainty quantification skill is of primary interests for assimilation ensemble methods. MATLAB
®
code examples are available online.
A stochastic flow representation is considered with the Eulerian velocity decomposed between a smooth large scale component and a rough small-scale turbulent component. The latter is specified as a ...random field uncorrelated in time. Subsequently, the material derivative is modified and leads to a stochastic version of the material derivative to include a drift correction, an inhomogeneous and anisotropic diffusion, and a multiplicative noise. As derived, this stochastic transport exhibits a remarkable energy conservation property for any realizations. As demonstrated, this pivotal operator further provides elegant means to derive stochastic formulations of classical representations of geophysical flow dynamics.
Using a classical example, the Lorenz‐63 model, an original stochastic framework is applied to represent large‐scale geophysical flow dynamics. Rigorously derived from a reformulated material ...derivative, the proposed framework encompasses several meaningful mechanisms to model geophysical flows. The slightly compressible set‐up, as treated in the Boussinesq approximation, yields a stochastic transport equation for the density and other related thermodynamical variables. Coupled to the momentum equation through a forcing term, the resulting stochastic Lorenz‐63 model is derived consistently. Based on such a reformulated model, the pertinence of this large‐scale stochastic approach is demonstrated over classical eddy‐viscosity based large‐scale representations.
We describe an original stochastic framework derived through a stochastic transport formalism. The proposed framework encompasses several meaningful mechanism to model geophysical flows. The pertinence of this large‐scale stochastic approach over classical eddy‐viscosity based representations is demonstrated on the Lorenz 63 model.
Satellite altimetry offers a unique approach for direct sea surface current observation, but it is limited to measuring the surface‐constrained geostrophic component. Ageostrophic dynamics, prevalent ...at horizontal scales below 100 km and time scales below 10 days, are often underestimated by ocean reanalyzes employing data assimilation schemes. To address this limitation, we introduce a novel deep learning scheme, rooted in a variational data assimilation formulation with trainable observations and a priori terms, that harnesses the synergies between satellite‐derived sea surface observations, namely sea surface height (SSH) and sea surface temperature (SST), to enhance sea surface current reconstruction. Numerical experiments, conducted using realistic simulations, in a case study area of the Gulf Stream, demonstrate the potential of the proposed scheme to capture ageostrophic dynamics at time scales of 2.5–3.0 days and horizontal scales of 0.5°–0.7°. The analysis of diverse observation configurations, encompassing nadir along‐track altimetry, wide‐swath SWOT (Surface Water and Ocean Topography) altimetry, and SST data, highlights the pivotal role of SST features in retrieving a significant portion of the ageostrophic dynamics (approximately 47%). These findings underscore the potential of deep learning and 4DVarNet schemes in improving ocean reanalyzes and enhancing our understanding of ocean dynamics.
Plain Language Summary
Satellite altimetry provides a unique means for direct observation of sea surface currents, but it is confined to the geostrophic component, limiting the recovery of a substantial portion of mesoscale sea surface currents in operational products. To address this limitation, we present a novel deep learning framework, rooted in a variational data assimilation paradigm, that unlocks new avenues for leveraging the synergistic relationships between satellite‐derived sea surface observations, namely sea surface height and sea surface temperature. This innovative scheme demonstrates its remarkable potential to enhance sea surface current reconstruction and recover a substantial portion of the elusive ageostrophic dynamics. Numerical experiments, employing realistic simulations, in a case study area along the Gulf Stream, underscore the efficacy of our proposed approach. These findings support the pivotal role of physics‐informed deep learning in maximizing the utilization of available multimodal observation data sets and numerical simulations to elucidate partially observed sea surface dynamics.
Key Points
We present end‐to‐end deep learning schemes to improve the reconstruction of total sea surface currents from satellite‐derived observations
Experiments in a region of the Gulf Stream support the synergistic analysis of sea surface temperature and sea surface height data
The strain of sea surface dynamics is a proxy of the uncertainty of the retrieved estimation
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