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The processing steps and error corrections needed to retrieve estimates of sea surface range Doppler velocities from Envisat Advanced Synthetic Aperture Radar Wide Swath Medium resolution image products are presented. Retrieval accuracies based on examination of the corrected Doppler shift measurements are assessed. The root-mean-square errors of the Doppler shift after bias corrections are found to be 4.7 and 3.9 Hz in VV and HH polarizations, respectively. At 35^\circ incidence angle, this corresponds to horizontal Doppler velocities of 23 and 19 cm/s. [ABSTRACT FROM AUTHOR]

Doppler radars at all incidence angles measure mean velocities and spreads that have complex relations to oceanic motions, with opportunities to measure winds, waves, and currents. Here, we extend previous theoretical models of backscatter and Doppler using a Kirchhoff approximation and the physical optics model. We show that in Ka-band, around 12° incidence, range-resolved measurements of Doppler and backscatter provide unambiguous estimations of the wave spectrum and surface current. This property is illustrated with numerical examples and airborne data from the Air Surface Water Ocean Topography instrument. The same measurement conditions can be exploited for global ocean mapping from the low Earth orbit sensor satellite configuration. [ABSTRACT FROM AUTHOR]

Polar-orbiting satellite observations are of fundamental importance to explore the main scientific challenges in the Arctic Ocean, as they provide information on bio-geo-physical variables with a denser spatial and temporal coverage than in-situ instruments in such a harsh and inaccessible environment. However, they are limited by the lack of coverage near the North Pole (Polar gap), the polar night, and frequent cloud cover or haze over the ocean and sea ice, which prevent the use of optical satellite instruments, as well as by the limited availability of external validation data. The satellite sensors' coverage and repeat cycles may also have limitations in properly identifying and resolving the dominant spatial and temporal scales of atmospheric, ocean, cryosphere and land variability and their interactive processes and feedback mechanisms. In this paper, we provide a state of the art of contribution of satellite observations to the understanding of the polar environment and climate scientific challenges tackled within the Arktalas Hoavva project funded by the European Space Agency. We identify the current limitations to the wider use of polar orbiting remote sensing data, as well as the observational gaps of the existing satellite missions. A comprehensive overview of all satellite missions and applications is given provided with a primary focus on the European satellites. Finally, we assess the expected capability of the approved future satellite missions to answer today's scientific challenges in the Arctic Ocean. [ABSTRACT FROM AUTHOR]

In the previous companion study, satellite data were used to describe peculiar characteristics of ocean surface wave fields, generated by two extra-tropical cyclones (ETCs) rapidly propagating in the North Atlantic. Based on a 2D parametric wave model, further details are now provided to analyse and interpret the spatio-temporal evolution of very intense ETC-generated waves. Significant wave height and wavelength values are shown to reach extreme values, 18 m and 500 m, respectively. Resulting energetic swell systems waves then radiate in the whole eastern part of the North Atlantic, and more particularly in the Norwegian sea region. Moving to higher latitudes, wind forcing characteristics of ETCs evolve, with the shape of the wind field changing from quasi-cyclonic to "air jets/Icelandic lows". In this paper, the resulting swell generation and propagation, after the deformation of an individual ETC, were studied, as well. Confirmed with comparisons with multi-satellite observations, the application of the parametric-2D wave-ray model was demonstrated to provide robust and highly detailed information on wave generation under very complex wind regime changes. [ABSTRACT FROM AUTHOR]

Surface waves generated by Extra-Tropical Cyclones (ETCs) can significantly affect shipping, fishing, offshore oil and gas production, and other marine activities. This paper presents the results of a satellite data-based investigation of wind waves generated by two North Atlantic ETCs. These ETCs were fast-moving systems, inhibiting resonance (synchronism) between the group velocity of the generated waves and the ETC translation velocity. In these cases, wave generation begins when the front boundary of the storm appears at a given ocean location point. Since developing waves are slow, they move backward relative to the storm, grow in time, and then leave the ETC stormy area through the rear sector. Multi-satellite observations confirm such a paradigm, revealing that the storm regions are filled with young developing wind waves, the most developed in the rear-right sector. As observed, the energy of these waves grew in time during the ETC life span. It is demonstrated that the extended-fetch concept (inherent for Tropical Cyclones) does not apply to ETC. Instead, by analogy, the concept of extended-duration wave growth is more relevant. Satellite observations confirmed the validity of duration-laws for waves generated by ETCs, and demonstrated that extended-fetch solutioncan be valid at time scales exceeding the lifespan of considered ETCs. [ABSTRACT FROM AUTHOR]

Simple Summary: Multi-satellite observations to jointly analyze extreme surface wind and wave properties can now be readily obtained to study tropical cyclone (TC) events. Developing over the Philippine Sea, TC Goni was one of the most powerful TCs in 2020. Constrained by Sentinel-1/RadarSat-2 SAR and CFOSAT SCAT satellite data, wind field in the intense TC region is reconstructed. Significant wave height measurements are further obtained from altimeters on-board the CFOSAT, Jason-3, and Sentinel-3A, B satellites. The directional wave spectrum information can be derived from the CFOSAT SWIM off-nadir radar measurements. Using a simplified 2D parametric model and derived self-similar analytical solutions, the measured surface wave amplification in the right-hand TC sector, relative to its propagation direction, is well captured and interpreted. For TC Goni, observed and predicted waves reach 8 m and wavelengths larger than 200 m, leaving the TC inner region in the forward and forward-left direction. In the far TC zone, swell attenuates and superposes with wind waves, not necessarily aligned, to give observed significant wave height values. Multi-satellite data together with simplified parametric model outputs open new perspectives to more precisely study and predict surface waves generated by moving and rapidly evolving TCs for different scientific and practical purposes. Over the Philippine Sea, the tropical cyclone (TC) Goni reaches category 5 on 29–31 October 2020. Multi-satellite observations, including CFOSAT SWIM/SCAT and Sentinel-1 SAR data, are jointly analyzed to assess the performances of a parametric model. Recently developed to provide a fast estimation of surface wave developments under rapidly evolving TCs, this full 2D parametric model (KYCM) and its simplified self-similar solutions (TC-wave geophysical model function (TCW GMF)) are thoroughly compared with satellite observations. TCW GMF provides immediate first-guess estimates, at any location in space and time, for the significant wave height, wavelength, and wave direction parameters. Moving cyclones trigger strong asymmetrical wave fields, associated to a resonance between wave group velocity and TC heading velocity. For TC Goni, this effect is well evidenced and captured, leading to extreme waves reaching up to 8 m, further outrunning as swell systems with wavelengths about 200–250 m in the TC heading direction, slightly shifted leftwards. Considering wind field constrained with very highly resolved Sentinel-1 SAR measurements and medium resolution CFOSAT SCAT data, quantitative agreements between satellite measurements and KYCM/TCW GMF results are obtained. Far from the TC inner core (∼10 radii of maximum wind speed), the superposition of outrunning swell systems and local wind waves estimates leads to Hs values very close to altimeter measurements. This case study demonstrates the promising capabilities to combine multi-satellite observations, with analytical self-similar solutions to advance improved understandings of surface wave generation under extreme wind conditions. [ABSTRACT FROM AUTHOR]

Swells from strong storms can spread over very long distances. Ocean currents alter this propagation, with the possible formation of constructive or destructive interference. This effect, still neglected in current models of atmospheric, oceanic, and even wave prediction, is often traced in current measurements, altimetry or even scatterometry / radiometry at medium and high resolution.Large-scale currents are indirectly measured by satellite. Since the small-scale currents are generally unknown, we propose to consider them as random in wave dynamics simulations. Specifically, the statistical spatial structure of these currents is inferred from large-scale currents through self-similar assumptions. The temporal correlations of the small-scale currents is neglected due to the short-time wave-current interaction. The dispersion ratio is modified and becomes stochastic. From there, we can derive and simulate the random dynamics of wave group the rays. Analytic and semi-analytic solutions have also been derived for simple – though realistic – cases.Our results not only improve wave simulation capabilities, but also bring new insights about the large wave's developments at small scales and the wave-current effects on satellite measurements. At longer term, those type of random dynamics will bring new data assimilation procedures for joint wave-current estimations from space. [ABSTRACT FROM AUTHOR]

Abstract An assessment of variance in ocean current signal and noise shared by in situ observations (drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of the year for 1993–2015 and across a broad spectrum of current speed. Regardless of the division of collocations, it is difficult to claim that any synoptic assessment can be based on independent observations. Instead, a measurement model that departs from ordinary linear regression by accommodating error correlation is proposed. The interpretation of independence is explored by applying Fuller's (1987) concept of equation and measurement error to a division of error into shared (correlated) and unshared (uncorrelated) components, respectively. The resulting division of variance in the new model favours noise. Ocean current shared (equation) error is of comparable magnitude to unshared (measurement) error and the latter is, for GlobCurrent and drifters respectively, comparable to ordinary and reverse linear regression. Although signal variance appears to be small, its utility as a measure of agreement between two variates is highlighted. Sparse collocations that sample a dense (high resolution) grid permit a first order autoregressive form of measurement model to be considered, including parameterizations of analysis-in situ error cross-correlation and analysis temporal error autocorrelation. The former (cross-correlation) is an equation error term that accommodates error shared by both GlobCurrent and drifters. The latter (autocorrelation) facilitates an identification and retrieval of all model parameters. Solutions are sought using a prescribed calibration between GlobCurrent and drifters (by variance matching). Because the true current variance of GlobCurrent and drifters is small, signal to noise ratio is near zero at best. This is particularly evident for moderate current speed and for the meridional current component. Highlights • Signal/noise of two ocean current datasets are compared by day of the year and speed. • A first order autoregressive form of regression model and its solution are derived. • Fuller's (1987) equation/measurement errors are applied to shared/unshared errors. • Shared true variance is smaller, and equation error larger, than expected. • Measurement error is saturated at ordinary and reverse least squares bounds. [ABSTRACT FROM AUTHOR]

The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables. These latent variables include unresolved small scales and/or rapidly evolving processes, partially observed couplings, or forcings in coupled systems. This is the case in ocean–atmosphere dynamics, for which unknown interior dynamics can affect surface observations. The identification of computationally-relevant representations of such partially-observed and highly nonlinear systems is thus challenging and often limited to short-term forecast applications. Here, we investigate the physics-constrained learning of implicit dynamical embeddings, leveraging neural ordinary differential equation (NODE) representations. In particular, we restrict the NODE representation to linear–quadratic dynamics and enforce a global boundedness constraint, which promotes the generalization of the learned dynamics to arbitrary initial conditions. The proposed architecture is implemented within a deep learning framework, and its relevance is demonstrated with respect to state-of-the-art schemes for different case studies representative of geophysical dynamics. • Neural ODEs are studied for the modeling of partially observed dynamical systems. • The models are constrained to remain bounded using the Schlegel boundedness theorem. • This framework allows deriving dynamical models with good asymptotic behaviors. • These models also have good generalization properties for unprecedented initial conditions. [ABSTRACT FROM AUTHOR]

Measurements of wave heights in marginal ice zones are limited to very few in situ data. Here we revisit the linear and quasilinear theories of Synthetic Aperture Radar imaging of waves in the particular case of waves in sea ice. Instead of only working with spectra, we have developed an iterative nonlinear algorithm to estimate phase-resolved deterministic maps of wave-induced orbital velocities, from which elevation spectra can be derived. Application of this algorithm to Sentinel 1A wave mode images in the Southern Ocean shows that it produces reasonable results for swells in all directions except when they propagate at a few degrees off the range direction. The estimate of wave parameters is expected to work best when the shortest wave components, those which cause a pixel displacement of the order of the dominant wavelength in azimuth, can be neglected. Otherwise short waves produce a blurring of the image, increasing exponentially with the azimuthal wavenumber and reducing the estimated wave amplitude. Given the expected spatial attenuation of waves in ice-covered regions, our deterministic method should apply beyond a few tens of kilometers in the ice, without any correction for short wave effects. In situ data collected around the ice edge as part of the 2015 SeaState DRI cruise in the Beaufort confirm the progressive image blurring caused by such short waves, and the apparent reduction in the wave modulation. When short waves propagate from the open ocean towards the ice, this blurring can produce an unrealistic apparent increase of wave height, from the open ocean up to a few tens of kilometers inside the ice. [ABSTRACT FROM AUTHOR]

The article provides information on the Synthetic Aperture Radar for Africa (SAR-Africa) for marine research. It mentions that the radar aims to develop African capacity in engaging oceanographic research and encouraging operational applications. It says that the first SAR-Africa workshop reviewed data processing and data limitations and highlighted presentations that the ideal area for testing applications and developing further SAR products are the coast and shelf seas of southern Africa.

In this study we show that spectral and phase-resolving wave models, used for coastal engineering applications, can provide significant differences in the local wave heights and wave power magnitude in the presence of strong bottom-induced refraction. We present here methodologies to compare, in a consistent manner, model outputs to an alternative source of spatial data derived from Synthetic Aperture Radar (SAR) satellite images. Normalized amplitude-derived data are computed from SAR images, compared to both normalized amplitude-derived map from a phase-resolving model outputs and normalized significant wave height-derived map from a spectral model. Two methodologies of SAR images analysis are developed in this paper. While one appears unsuitable due to stochastic wave group modulations, the second methodology provides encouraging similarities between the SAR data and the phase-resolving model outputs. • Spectral and phase-resolving wave models behave differently in coastal areas with strong bottom-induced refraction. • SAR data may represent a new source of data to investigate the spatial variability of the wave field in coastal waters. • Methodologies are developed to derive comparable models and SAR data. • Encouraging similarities are observed between the phase-resolving model and SAR data for the site and situations considered. [ABSTRACT FROM AUTHOR]

The article explores changes in sea states and swell in northeastern U.S. caused by North Atlantic storm Quirin. The National Oceanic and Atmospheric Administration (NOAA) Ocean Prediction Center considered the storm as the last of four deep lows with hurricane-force winds that developed over the northern Atlantic. A consistent model hindcast forced by Climate Forecast System Reanalysis was used to provide full space-time coverage of the global sea state.

Abstract: A method is proposed to retrieve and interpret fine spatial variations of the sea surface roughness in sun glitter imagery. Observed sun glitter brightness anomalies are converted using a transfer function determined from the smoothed shape of sun glitter brightness. The method is applied to MODIS and MERIS sun glitter imagery of natural oil seeps and the catastrophic Deepwater Horizon oil spill in the Gulf of Mexico. The short-scale roughness variations in the presence of mineral oils slicks are consistently extracted and compared to variations associated with the biogenic slicks. In doing so, the wind speed dependency on the roughness anomalies is also considered. A comparison to normalized radar cross section (NRCS) anomalies taken from the corresponding high resolution ASAR images is performed, and similarities as well as differences are investigated. The results document significant benefit from the synergetic use of sun glitter and radar imagery for detection and monitoring of surface slicks. [Copyright &y& Elsevier]

Abstract: The variability of small-size iceberg distributions is revealed from a novel analysis of satellite altimeter data. A strong annual cycle is modulated by pulse-like events confined to single ocean basins, with dense iceberg populations in the South Atlantic in 2004–2005, and in the South Pacific in 2008. Anomalies in sea surface temperatures of the order of 1°C may be related to the iceberg distribution. Icebergs also appear very strongly associated with anomalies in the heights of ocean waves. A preliminary parameterization of wave blocking by icebergs significantly reduces wave model errors in the region south of 45° South, and has a perceptible influence on all the west coasts of the Southern hemisphere. [Copyright &y& Elsevier]

New parameterizations for the spectral dissipation of wind-generated waves are proposed. The rates of dissipation have no predetermined spectral shapes and are functions of the wave spectrum and wind speed and direction, in a way consistent with observations of wave breaking and swell dissipation properties. Namely, the swell dissipation is nonlinear and proportional to the swell steepness, and dissipation due to wave breaking is nonzero only when a nondimensional spectrum exceeds the threshold at which waves are observed to start breaking. An additional source of short-wave dissipation is introduced to represent the dissipation of short waves due to longer breaking waves. A reduction of the wind-wave generation of short waves is meant to account for the momentum flux absorbed by longer waves. These parameterizations are combined and calibrated with the discrete interaction approximation for the nonlinear interactions. Parameters are adjusted to reproduce observed shapes of directional wave spectra, and the variability of spectral moments with wind speed and wave height. The wave energy balance is verified in a wide range of conditions and scales, from the global ocean to coastal settings. Wave height, peak and mean periods, and spectral data are validated using in situ and remote sensing data. Some systematic defects are still present, but, overall, the parameterizations probably yield the most accurate estimates of wave parameters to date. Perspectives for further improvement are also given. [ABSTRACT FROM AUTHOR]

The Sentinel-2 MultiSpectral Instrument (MSI) collects multiple spectral band images, corresponding to specific sensing wavelengths and spatial resolutions, i.e. 10 m , 20 m and 60 m , respectively. Images are collected one at the time with a given time-lag between observations. Under favorable conditions, spatio-temporal characteristics of propagating ocean surface waves can thus uniquely be retrieved. A method for surface current vector field reconstruction is then developed. Demonstrated over different deep ocean regions, the retrieved surface current fields well compare with medium-resolution ocean circulation model or derived-velocities from altimeter measurements. At finer scales, the surface wave-conservation law is recovered, with the associated relationship between current vorticity and wave-ray curvature. Over shallow water regions, the wave propagation properties well follow sea depth variations, consistent with ETOPO1 data. Finally, time-lag between detector bands can also be exploited to estimate speed and direction properties of detected surface wave breaking whitecaps. An analysis of velocity reconstruction errors further reveals that Sentinel-2 MSI inter-channel co-registration is realized with an accuracy better than 0.1 pixel. Overall, these results confirm very promising capabilities of optical imagery to provide direct surface current velocity measurements from space, over relatively large areas, O(100 km), with a spatial resolution down to O(1 km). • A method is developed for surface current reconstruction from Sentinel-2 images •It is based on cross-spectral phase estimation between images from different bands •Retrieved current fields well compare with model and altimeter measurements •Over shallow water regions, sea depth is reconstructed •Inter-channel time delay is used to estimate speed and direction of large whitecaps [ABSTRACT FROM AUTHOR]

The constantly increasing wealth of simulation and observation data on geophysical dynamics make more and more appealing data-driven strategies as new means to address key issues in ocean and atmosphere science, including for instance forecasting and assimilation issues. In this respect, recent studies have investigated data-driven strategies to identify governing equations from data using different machine learning frameworks, especially sparse regression models and neural networks. Among others, such data-driven representations of dynamical operators have shown to be of key interest for data assimilation issues, especially the spatio-temporal interpolation of geophysical fields from ocean remote sensing data. The availability of representative training datasets is a strong requirement for the development of such approaches. When considering observation datasets (e.g., satellite-derived data or in-situ observations), the question whether one may learn such data-driven representations from noisy and partial observation data naturally arises. For instance, regarding sea surface dynamics, beyond observation noise patterns, satellite sensors also involve irregular space-time sampling patterns due to their intrinsic characteristics or their sensitivity to the atmospheric conditions. In this work, we investigate these issues. We show that the effectiveness of previously proposed learning-based methods is strongly affected when applied to noisy and partial observation datasets. Within a neural-network-based framework, we address the data-driven identification of governing equations as the joint learning of dynamical models and the assimilation of the hidden states from noisy and partial observations. We restate state-of-the-art assimilation schemes (e.g., Ensemble Kalman Filters, Variational schemes) as neural network architectures, such that the identification of the dynamical operator comes to the minimization of a data assimilation cost, rather than a simple forecasting error. From numerical experiments on Lorenz-63 dynamics and sea surface dynamics, we demonstrate the relevance of the proposed approaches for the data-driven identification of dynamical operators, both in terms of forecasting performance and long-term topological patterns, within respect to the state-of-the-art. These experiments also illustrate that different assimilation-inspired neural-network-based architectures may be of interest depending on the noise levels. [ABSTRACT FROM AUTHOR]

The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential representativity. From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven framework. In this work we adress this challenge and develop a novel Neural-Network-based (NN-based) Kalman filter for spatio-temporal interpolation of sea surface dynamics. Based on a data-driven probabilistic representation of spatio-temporal fields, our approach can be regarded as an alternative to classical filtering schemes such as the ensemble Kalman filters (EnKF) in data assimilation. Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks, (ii) we derive the associated parametric Kalman-like filtering scheme for a computationally-efficient spatio-temporal interpolation of Sea Surface Temperature (SST) fields. We illustrate the relevance of our contribution for an OSSE (Observing System Simulation Experiment) in a case-study region off South Africa. Our numerical experiments report significant improvements in terms of reconstruction performance compared with operational and state-of-the-art schemes (e.g., optimal interpolation, Empirical Orthogonal Function (EOF) based interpolation and analog data assimilation). [ABSTRACT FROM AUTHOR]