58 research outputs found

    An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

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    Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models

    Improving the Altimeter-Derived Surface Currents Using Sea Surface Temperature (SST) Data: A Sensitivity Study to SST Products

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    Measurements of ocean surface topography collected by satellite altimeters provide geostrophic estimates of the sea surface currents at relatively low resolution. The effective spatial and temporal resolution of these velocity estimates can be improved by optimally combining altimeter data with sequences of high resolution interpolated (Level 4) Sea Surface Temperature (SST) data, improving upon present-day values of approximately 100 km and 15 days at mid-latitudes. However, the combined altimeter/SST currents accuracy depends on the area and input SST data considered. Here, we present a comparative study based on three satellite-derived daily SST products: the Remote Sensing Systems (REMSS, 1/10 ∘ resolution), the UK Met Office OSTIA (1/20 ∘ resolution), and the Multiscale Ultra-High resolution SST (1/100 ∘ resolution). The accuracy of the marine currents computed with our synergistic approach is assessed by comparisons with in-situ estimated currents derived from a global network of drifting buoys. Using REMSS SST, the meridional currents improve up to more than 20% compared to simple altimeter estimates. The maximum global improvements for the zonal currents are obtained using OSTIA SST, and reach 6%. Using the OSTIA SST also results in slight improvements (≃1.3%) in the zonal flow estimated in the Southern Ocean (45 ∘ S to 70 ∘ S). The homogeneity of the input SST effective spatial resolution is identified as a crucial requirement for an accurate surface current reconstruction. In our analyses, this condition was best satisfied by the lower resolution SST products considered

    Analogs of Rashba-Edelstein effect from density functional theory

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    Studies of structure-property relationships in spintronics are essential for the design of materials that can fill specific roles in devices. For example, materials with low symmetry allow unconventional configurations of charge-to-spin conversion which can be used to generate efficient spin-orbit torques. Here, we explore the relationship between crystal symmetry and geometry of the Rashba-Edelstein effect (REE) that causes spin accumulation in response to an applied electric current. Based on a symmetry analysis performed for 230 crystallographic space groups, we identify classes of materials that can host conventional or collinear REE. Although transverse spin accumulation is commonly associated with the so-called 'Rashba materials', we show that the presence of specific spin texture does not easily translate to the configuration of REE. More specifically, bulk crystals may simultaneously host different types of spin-orbit fields, depending on the crystallographic point group and the symmetry of the specific kk-vector, which, averaged over the Brillouin zone, determine the direction and magnitude of the induced spin accumulation. To explore the connection between crystal symmetry, spin texture, and the magnitude of REE, we perform first-principles calculations for representative materials with different symmetries. We believe that our results will be helpful for further computational and experimental studies, as well as the design of spintronics devices.Comment: 10 pages, 5 figure

    Mississippi River and Sea Surface Height Effects on Oil Slick Migration

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    Millions of barrels of oil escaped into the Gulf of Mexico (GoM) after the 20 April, 2010 explosion of Deepwater Horizon (DH). Ocean circulation models were used to forecast oil slick migration in the GoM, however such models do not explicitly treat the effects of secondary eddy-slopes or Mississippi River (MR) hydrodynamics. Here we report oil front migration that appears to be driven by sea surface level (SSL) slopes, and identify a previously unreported effect of the MR plume: under conditions of relatively high river discharge and weak winds, a freshwater mound can form around the MR Delta. We performed temporal oil slick position and altimeter analysis, employing both interpolated altimetry data and along-track measurements for coastal applications. The observed freshwater mound appears to have pushed the DH oil slick seaward from the Delta coastline. We provide a physical mechanism for this novel effect of the MR, using a two-layer pressure-driven flow model. Results show how SSL variations can drive a cross-slope migration of surface oil slicks that may reach velocities of order km/day, and confirm a lag time of order 5–10 days between mound formation and slick migration, as observed form the satellite analysis. Incorporating these effects into more complex ocean models will improve forecasts of slick migration for future spills. More generally, large SSL variations at the MR mouth may also affect the dispersal of freshwater, nutrients and sediment associated with the MR plume

    SEASTAR: a mission to study ocean submesoscale dynamics and small-scale atmosphere-ocean processes in coastal, shelf and polar seas

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    High-resolution satellite images of ocean color and sea surface temperature reveal an abundance of ocean fronts, vortices and filaments at scales below 10 km but measurements of ocean surface dynamics at these scales are rare. There is increasing recognition of the role played by small scale ocean processes in ocean-atmosphere coupling, upper-ocean mixing and ocean vertical transports, with advanced numerical models and in situ observations highlighting fundamental changes in dynamics when scales reach 1 km. Numerous scientific publications highlight the global impact of small oceanic scales on marine ecosystems, operational forecasts and long-term climate projections through strong ageostrophic circulations, large vertical ocean velocities and mixed layer re-stratification. Small-scale processes particularly dominate in coastal, shelf and polar seas where they mediate important exchanges between land, ocean, atmosphere and the cryosphere, e.g., freshwater, pollutants. As numerical models continue to evolve toward finer spatial resolution and increasingly complex coupled atmosphere-wave-ice-ocean systems, modern observing capability lags behind, unable to deliver the high-resolution synoptic measurements of total currents, wind vectors and waves needed to advance understanding, develop better parameterizations and improve model validations, forecasts and projections. SEASTAR is a satellite mission concept that proposes to directly address this critical observational gap with synoptic two-dimensional imaging of total ocean surface current vectors and wind vectors at 1 km resolution and coincident directional wave spectra. Based on major recent advances in squinted along-track Synthetic Aperture Radar interferometry, SEASTAR is an innovative, mature concept with unique demonstrated capabilities, seeking to proceed toward spaceborne implementation within Europe and beyond

    A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements

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    An efficient combination of remotely-sensed data and in situ measurements is needed to obtain accurate 3D ocean state estimates, representing a fundamental step to describe ocean dynamics and its role in the Earth climate system and marine ecosystems. Observations can either be assimilated in ocean general circulation models or used to feed data-driven reconstructions and diagnostic models. Here we describe an innovative deep learning algorithm that projects sea surface satellite data at depth after training with sparse co-located in situ vertical profiles. The technique is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and is applied here to the measurements collected between 2010 and 2018 over the North Atlantic Ocean. The model provides hydrographic vertical profiles and associated uncertainties from corresponding remotely sensed surface estimates, outperforming similar reconstructions from simpler statistical algorithms and feed-forward networks

    Recent advances on mesoscale variability in the Western Mediterranean Sea: complementarity between satellite altimetry and other sensors

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    Trabajo presentado en la EGU General Assemby 2012, celebrada del 22 al 27 de abril de 2012 en Viena (Austria)Satellite altimetry has provided a unique contribution to the global observation of mesoscale variability, the dominant surface signal in the ocean circulation at mid and high latitudes. In particular, it is now possible to quantify and monitor surface mesoscale eddies. However, the single use satellite altimetry only allows providing surface information with a limited spatio/temporal coverage. Thus, to circumvent these limitations and to fully understand the three-dimensional variability it is necessary to complement altimetry data with alternative remote and in-situ sensors. In this study we review recent advances on mesoscale variability as seen by the synergy of altimetry and independent observations in the Western Mediterranean, where the circulation is rather complex due to the presence of multiple interacting scales, including basin, sub-basin scale and mesoscale structures. The challenges of characterizing these processes imply therefore precise and high-resolution observations in addition to multi-sensor approaches. Accordingly, multi-platform experiments have been designed and carried out in the different sub-basins of the Western Mediterranean Sea highlighting the need of synergetic approaches through the combined use of observing systems at several spatial/temporal scales, with the aim of better understanding mesoscale dynamics.Peer Reviewe

    Improved Surface Currents from Altimeter-Derived and Sea Surface Temperature Observations: Application to the North Atlantic Ocean

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    We present a study on the ocean surface currents reconstruction by merging Level-4 (L4, gap-free) altimeter-derived geostrophic currents and satellite sea surface temperature. Building upon past studies on the multi-variate reconstruction of geostrophic currents from satellite observations, we regionalized and optimized an algorithm to improve the altimeter-derived surface circulation estimates in the North Atlantic Ocean. A ten-year-long time series (2010–2019) is presented and validated by means of in situ observations. The newly optimized algorithm allowed us to improve the currents estimate along the main axis of the Gulf Stream and in correspondence of well-known upwelling areas in the North Eastern Atlantic, with percentage improvements of around 15% compared to standard operational altimetry products

    A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016)

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    Monitoring sea surface salinity (SSS) and density variations is crucial to investigate the global water cycle and the ocean dynamics, and to analyse how they are impacted by climate change. Historically, ocean salinity and density have suffered a poor observational coverage, which hindered an accurate assessment of their surface patterns, as well as of associated space and time variability and trends. Different approaches have thus been proposed to extend the information obtained from sparse in situ measurements and provide gap-free fields at regular spatial and temporal resolution, based on the combination of in situ and satellite data. In the framework of the Copernicus Marine Environment Monitoring Service, a daily (weekly sampled) global reprocessed dataset at ¼° × ¼° resolution has been produced by modifying a multivariate optimal interpolation (OI) technique originally developed within MyOcean project. The algorithm has been applied to in situ salinity/density measurements covering the period from 1993 to 2016, using satellite sea surface temperature differences to constrain the surface patterns. This improved algorithm and the new dataset are described and validated here with holdout approach and independent data
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