Segmentation of mesoscale ocean surface dynamics using satellite SST and SSH observations

International audienceMulti-satellite measurements of altimeter-derived Sea Surface Height (SSH) and Sea Surface Temperature (SST) provide a wealth of information about ocean circulation, especially mesoscale ocean dynamics which may involve strong spatio-temporal relationships between SSH and SST fields. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics may be decomposed into a mixture of dynamical modes, characterized by different local regressions between SSH and SST fields. Formally, we develop a novel latent class regression model to identify dynamical modes from joint SSH and SST observation series. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatio-temporal segmentation of the upper ocean dynamics

Ocean surface current retrieval using a non homogeneous Markov-switching multi-regime model

International audienceThis paper addresses the reconstruction of sea surface currents from satellite ocean sensing data. Whereas the classical surface currents derived from the SSH (Sea Surface Height) products are rather low space-time resolution fields (typically, 50 km and 12-day actual space-time grid resolution), we investigate the extent to which we can retrieve sea surface currents at higher resolution using daily SST (Sea Surface Temperature) satellite observations. State-of-the-art methods, which exploit classical optical flow schemes or nonlinear regression techniques, do not provide satisfactory results due to the space-time variabilities of the relationships between the SST and the sea surface current. Motivated by our recent joint SST-SSH identification of characterization of upper ocean dynamical modes, we here show that a multi-regime model, formally stated as a Markov-switching latent class regression model, provides a relevant model to capture the above-mentioned variabilities and reconstruct SST-driven sea surface currents. The considered case study within the Agulhas current demonstrates that our model retrieves high-resolution space-time details which cannot be resolved by the classical SSH-derived products

Hidden surface dynamical modes and SSH retrievals from a joint analysis of altimetry and microwave SST

International audienceThe availability of daily satellite Sea Surface Temperature (SST) data and theoretical results advocate for new methods to retrieve the Sea Surface Height (SSH) and the surface geostrophic currents from SST observations. The underlying hypothesis comes to assume that the local variations of the SST relate to the surface currents. Ocean turbulence models, such as the Surface Quasi Geostrophic (SQG) theory, or statistical methods like neural networks or latent class regressions provide different means to state the SST-SSH relationships. This later approach has the advantage to be completely parametric and to account for different transfer functions between SST and SSH. It relies on a conditional setting with respect to a hidden variable related to different dynamical modes at the surface of the ocean. In this paper, we aim at further developing such latent models with an emphasis on two aspects: (i) the modeling and learning of the spatio-temporal dynamics of the hidden dynamical modes using Markovian priors, (ii) the reconstruction of daily SSH fields from a joint analysis of microwave SST and altimetry observation series. We evaluate the proposed model both qualitatively and quantitatively with respect to the reference altimetry product

Ocean surface currents reconstruction from microwave radiometers measurements

Premi Extraordinari de Doctorat, promoció 2014-2015. Àmbit d'Enginyeria de les TICOcean currents are a key component to understanding many oceanic and climatic phenomena and knowledge of them is crucial for both navigation and operational applications. Therefore, a key problem in oceanography is the estimation of the synoptic velocity field. Currently, global ocean surface velocities are routinely estimated from Sea Surface Height (SSH) measurements provided by altimeters. However, the separation between passes, as well as and the limited number of available altimeters leads to errors in the accurate location of oceanic currents when these measurements are used exclusively. Contrarily, satellite images of Sea Surface Temperature (SST) provide a good qualitative view of the location of ocean patterns, which has encouraged the investigation of alternative methodologies to reconstruct the velocity field based on these observations. This Ph.D. thesis has assessed the capability of SST microwave radiometers observations to retrieve ocean surface currents. The reconstruction of the ocean surface currents from SST observations can be expressed in terms of a transfer function notation, that allows to convert SST maps into SSH, and thus into currents. Because under geostrophic balance, the slope of SSH is proportional to ocean surface currents. This transfer function can be theoretically derived using the Surface Quasi-Geostrophic equations (SQG). Two different approaches were analyzed at a global scale: on one side, the analysis of the validity of the SQG approach has been performed, and on the other, an approach based on the synergetic properties between simultaneous SST and SSH observations has been analyzed. Both approaches have been compared with ocean surface currents retrieved from merged altimetric observations. The study has been focused on the period from October 2002 to May 2005, since during that period there were available four different altimeters, and the quality of the merged altimetric observations was enhanced. The analysis of the validity of SQG at a global scale revealed that this dynamical model is valid near the major extratropic current system such us the Gulf Stream, the Antartic Circumpolar Current, Kuroshio currents. Besides, the potential of MW SST observations to reconstruct ocean surface currents was analyzed using a synergetic approach: the combination of the SST phase with the SSH spectra. Actually, we explored under which environmental conditions the phase of the MW SST is close to the SSH phase. Results showed that the phase of the MW SST can be used to retrieve ocean currents during winter, near the major extratropical current systems, which are characterized by an intense mesoscale activity and the presence of strong thermal gradients, and deep ML. Furthermore, the reconstruction of the velocity fields from an ideal transfer function built up from simultaneous SST and SSH observations revealed that the SQG approach can be enhanced. The spectral properties of this ideal transfer function derived from simultaneous SST and SSH observations were characterized at a global scale. The analysis of spectral properties of the transfer function between SST and SSH observations revealed that despite daily spectral can be flatter or steeper than the k^{-1} predicted by SQG theory, in mean eSQG is a good statistically approach to retrieve ocean currents, when no simultaneous observations of SSH and SST are available.Las corrientes oceánicas son clave en muchos procesos oceánicos y climáticos, y su conocimiento es crucial para aplicaciones operacionales y de navegación. Por lo tanto, un aspecto importante en oceanografía es la estimación de campos sinópticos del campo de velocidades superficiales del mar. Actualmente, las velocidades superficiales el mar se estiman rutinariamente a partir de medidas del nivel del mar proporcionadas por altimetros, denotadas a partir de ahora con sus siglas en inglés SSH. Sin embargo, la llocalización de las corrientes puede no ser la correcta si solo se utilizan este tipo de medidas para su estimación, debido a la separación entre trazas del satélite. Por contra, las imágenes de temperatura superficial del mar, SST, proporiconan una visión cualitativa de la localización de las estructruas oceánicas. Este hecho ha motivado la investigación de metodologías alternativas para reconstruir los campos de velocidades superficiales del mar basados en estas observaciones. Esta tesis doctoral ha investigado la capacidad de las observaciones de SST proporcionadas por radiometros de microondas para recuperar las corrientes oceánicas superficiales. La reconstrucción de estas velocidades a partir de observaciones de SST se puede expresar en términos de una función de transferencia que relacione las observaciones de SST con las observaciones de SSH. Con lo que la estimación del campo de velocidades es directa, puesto que bajo la condición de equilibrio geostrófico la pendiente de la SSH es proporcional a las corrientes oceánicas. Esta función de transferencia se puede derivar teóricamente mediante las equaciones superficiales cuasi-geotróficas, denotadas con sus siglas en inglés SQG a partir de ahora. Una pregunta clave, es si las ecuaciones de este modelo dinámico son válidas. En esta tesis, se han llevado a cabo dos aproximaciones diferentes para la reconstrucción del campo de velocidades superficiales del mar: por un lado, el análisis de la validez de las ecuaciones SQG, y por otro, una aproximación basada en las propiedades espectrales de medidas simultáneas de SST y SSH. El estudio se ha centrado en el período comprendido entre Octubre del 2002 y Mayo del 2005, puesto que durante este período había disponibles hasta cuatro altmímetros, y consecuentemente la calidad de las observaciones es mayor. El análisis de la validez de SQG a escala global reveló que este modelo dinámico es válido en las regiones cerca de los sistemas de corrientes extratropicales, como la corriente del Golfo, la Corriente Circumpolar Antártica (ACC), o la Kuroshio. Además, el potencial de las observaciones de SST en el rango de las microondas para la recuperación del campo de velocidades superficiales del mar, ha sido analizado utilizando un método que combina la fase de la SST con el espectro de SSH. En realidad, se ha investigado bajo que condiciones la SST y SSH están en fase. Los resultados mostraron que la fase de la SST de microondas puede utilizarse para para la reconstrucción en invierno, cerca de los sistemas de corrientes extratropicales, caracterizados por una intensa actividad de mesoscala y la presencia de fuertes gradientes termales, así como de capas de mezcla profundas. Asimismo, la reconstrucción del campo de velocidades a partir de una función de transferencia ideal, construida a partir de imágenes simultaneas de SST y SSH, reveló que la aproximación SQG puede ser mejorada. Las propiedades espectrales de esta función de tranferencia ideal han sido estudiadas., así como su variabilidad temporal. Este análisis desveló que para escalas pequeñas y zonas enegéticas, la aproximación SQG es una buena aproximación, al menos, desde un punto de vista estádistico.Award-winningPostprint (published version

SymmetricNet: end-to-end mesoscale eddy detection with multi-modal data fusion

Mesoscale eddies play a significant role in marine energy and matter transportation. Due to their huge impact on the ocean, mesoscale eddy detection has been studied for many years. However, existing methods mainly use single-modal data, such as the sea surface height (SSH), to detect mesoscale eddies, resulting in inaccurate detection results. In this paper, we propose an end-to-end mesoscale eddy detection method based upon multi-modal data fusion. Particularly, we don’t only use SSH, but also add data of other two modals, i.e., the sea surface temperature (SST) and the velocity of flow, which are closely related to mesoscale eddy detection. Moreover, we design a novel network named SymmetricNet, which is able to achieve multi-modal data fusion in mesoscale eddy detection. The proposed SymmetricNet mainly contains a downsampling pathway and an upsampling pathway, where the low-level feature maps from the downsampling pathway and the high-level feature maps from the upsampling pathway are merged through lateral connections. In addition, we apply dilated convolutions to the network structure to increase the receptive field without sacrificing resolution. Experiments on multi-modal mesoscale eddy dataset demonstrate the advantages of the proposed method over previous approaches for mesoscale eddy detection

Multiscale analysis of geometric planar deformations: application to wild animals electronic tracking and satellite ocean observation data

International audienceThe development of animal tracking technologies (including for instance GPS and ARGOS satellite systems) and the increasing resolution of remote sensing observations call for tools extracting and describing the geometric patterns along a track or within an image over a wide range of spatial scales. Whereas shape analysis has largely been addressed over the last decades, the multiscale analysis of the geometry of opened planar curves has received little attention. We here show that classical multiscale techniques cannot properly address this issue and propose an original wavelet-based scheme. To highlight the generic nature of our multiscale wavelet technique, we report applications to two different observation datasets, namely wild animal movement paths recorded by electronic tags and satellite observations of sea surface geophysical fields

Scale-aware neural calibration for wide swath altimetry observations

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which provide one-dimensional-only along-track satellite observations of the SSH. The Surface Water and Ocean Topography (SWOT) mission deploys a new sensor that acquires for the first time wide-swath two-dimensional observations of the SSH. This provides new means to observe the ocean at previously unresolved spatial scales. A critical challenge for the exploiting of SWOT data is the separation of the SSH from other signals present in the observations. In this paper, we propose a novel learning-based approach for this SWOT calibration problem. It benefits from calibrated nadir altimetry products and a scale-space decomposition adapted to SWOT swath geometry and the structure of the different processes in play. In a supervised setting, our method reaches the state-of-the-art residual error of ~1.4cm while proposing a correction on the entire spectral from 10km to 1000kComment: 8 pages, 7 figures, Preprin

Training neural mapping schemes for satellite altimetry with simulation data

Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL) schemes have emerged as appealing solutions to address space-time interpolation problems. The scarcity of real altimetry dataset, in terms of space-time coverage of the sea surface, however impedes the training of state-of-the-art neural schemes on real-world case-studies. Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height and demonstrate their performance for real altimetry datasets. We analyze further how the ocean simulation dataset used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy-present configurations to eddy-rich ones, forced simulations vs. reanalyses using data assimilation and tide-free vs. tide-resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5-altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation-based 4DVarNets outperform the operational observation-driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation dataset used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy-rich and tide-free simulation datasets. It improves the resolved longitudinal scale from 151 kilometers for DUACS and 241 kilometers for GLORYS to 98 kilometers and reduces the root mean squared error (RMSE) by 23% and 61%. These results open research avenues for new synergies between ocean modelling and ocean observation using learning-based approaches

Understanding variability across the Crossroad transect from 3 years (2013 to 2015) of hydrographic data

The southwest continental shelf of Africa is characterized by a strong western boundary current with three interdependent components, namely the Agulhas Current, Agulhas Retroflection and Agulhas Return Current. This system plays a key role in setting oceanic conditions south of Africa. The Crossroad transect intersects both the Agulhas Current and Agulhas Return Current; a monitoring line established in 2013 to sample both the currents and determine inter-ocean fluxes, as well as the influence of the Agulhas Current on the Agulhas Bank shelf. The objective of the study was to examine both mesoscale and submesoscale features that influence the dynamic and variant nature of the Agulhas system. In this study we make use of Ship board Acoustic Doppler Current Profiler (SADCP), Conductivity Temperature and Depth (CTD), Thermosalinograph (TSG) and satellite Sea Surface Height data as main observations for analysis. The study also examines both the spatial and temporal characteristics of water properties across the Crossroad transect. The fundamental findings of the study include the abundance of both the mesoscale and submesoscale features observed in the Agulhas system, which are often overlooked. In addition, a noticeable variability in current measurements was observed, where velocity ranging from 2 to 2.5 m/s represented the Agulhas Current and 1.4 to 1.7 m/s, Agulhas Return Current. The position of the Agulhas Current and Agulhas Return Current displayed variation from 2013 to 2015, with the Agulhas Return Current exhibiting a meandering pattern in 2014 along the transect. Furthermore, an intrusion of cool (8 to 13 °C), lower salinity (34.8 to 35 psu) South Indian Central Water masses were also observed along the Agulhas Bank. The ability to combine altimetry and in situ data also contributed to the analysis of the results. Therefore, given the inherent advantage of satellite and in situ measurements, an overview of the variability across the Crossroad transect was determined

Modelling seasonal environmental preferences of tropical tuna purse seine fisheries in the Mozambique Channel

The spatial-temporal environmental preferences and biomass aggregation of tropical tuna from purse seine fishery in the Mozambique Channel (MZC) have barely been investigated. In this study, tuna biomass volume from Fish Aggregating Devices (FADs) and Free-Swimming Schools (FSC), collected by Spanish fishing logbooks during 2003–2013, were modelled separately as a function of a set of oceanographic variables (sea surface temperature, sea surface height, geostrophic currents, salinity, and chlorophyll-a) using Generalized Additive Models (GAMs). Temporal variables (natural day, month and year), and spatial variables (latitude and longitude) were included in the models to account for the spatio-temporal structure of dynamic biomass of tropical tuna volume gathering. Oceanographic, temporal and spatial effects on aggregated catches differed between fishing modes, even though some common aspects appeared along the area and the period of study. Fishable patches of tuna biomass accumulation were explained by sea surface temperature, productivity, sea surface height, geostrophic currents, and apart from the spatio-temporal variables interactions. Although the models predicted slight differences for tuna fishing spots preferences, both fishing modes partially overlapped. Goodness of fit for selected variables showed that models were able to predict tuna catches assembled patterns in the MZC reasonably well. These results highlight a connection between the biophysical state of the oceans and purse seine tuna catches in the MZC, and ultimately may contribute to the scientific advice for the appropriate management and conservation of the exploited resources by purse seine fleets in the area of MZC.Postprint1,58