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Registros recuperados: 6
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A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode ArchiMer
Wang, Chen; Mouche, Alexis; Tandeo, Pierre; Stopa, Justin; Longépé, Nicolas; Erhard, Guillaume; Foster, Ralph C.; Vandemark, Douglas; Chapron, Bertrand.
The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed...
Tipo: Text Palavras-chave: Manual labelling; Ocean surface phenomena; Sentinel-1 wave mode; Synthetic aperture radar.
Ano: 2019 URL: https://archimer.ifremer.fr/doc/00512/62406/66659.pdf
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A Multivariate Regression Approach to Adjust AATSR Sea Surface Temperature to In Situ Measurements ArchiMer
Tandeo, Pierre; Autret, Emmanuelle; Piolle, Jean-francois; Tournadre, Jean; Ailliot, Pierre.
The Advanced Along-Track Scanning Radiometer (AATSR) onboard Envisat is designed to provide very accurate measurements of sea surface temperature (SST). Using colocated in situ drifting buoys, a dynamical matchup database (MDB) is used to assess the AATSR-derived SST products more precisely. SST biases are then computed. Currently, Medspiration AATSR SST biases are discrete values and can introduce artificial discontinuities in AATSR level-2 SST fields. The new AATSR SST biases presented in this letter are continuous. They are computed, for nighttime and best proximity confidence data, by linear regression with different MDB covariables (wind speed, latitude, aerosol optical depth, etc.). As found, the difference between dual-view and nadir-only SST...
Tipo: Text Palavras-chave: Validation; Sea surface temperature (SST); Remote sensing; Advanced Along Track Scanning Radiometer (AATSR).
Ano: 2009 URL: http://archimer.ifremer.fr/doc/2009/publication-6135.pdf
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Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean ArchiMer
Maze, Guillaume; Mercier, Herle; Fablet, Ronan; Tandeo, Pierre; Radcenco, Manuel Lopez; Lenca, Philippe; Feucher, Charlene; Le Goff, Clement.
A quantitative understanding of the integrated ocean heat content depends on our ability to determine how heat is distributed in the ocean and what are the associated coherent patterns. This study demonstrates how this can be achieved using unsupervised classification of Argo temperature profiles. The classification method used is a Gaussian Mixture Model (GMM) that decomposes the Probability Density Function of a dataset into a weighted sum of Gaussian modes. It is determined that the North Atlantic Argo dataset of temperature profiles con- tains 8 groups of vertically coherent heat patterns, or classes. Each of the temperature profile classes reveals unique and physically coherent heat distributions along the vertical axis. A key result of this study is...
Tipo: Text Palavras-chave: Heat content; Classification North Atlantic; Stratification; Water mass; Thermocline; Argo; Pattern.
Ano: 2017 URL: http://archimer.ifremer.fr/doc/00363/47431/47456.pdf
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Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures ArchiMer
Picart, Stephane Saux; Tandeo, Pierre; Autret, Emmanuelle; Gausset, Blandine.
Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for...
Tipo: Text Palavras-chave: Machine learning; Systematic error; Sea surface temperature; Random forest.
Ano: 2018 URL: http://archimer.ifremer.fr/doc/00426/53797/54721.pdf
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Linear Gaussian state-space model with irregular sampling: application to sea surface temperature ArchiMer
Tandeo, Pierre; Ailliot, Pierre; Autret, Emmanuelle.
Satellites provide important information on many meteorological and oceanographic variables. State-space models are commonly used to analyse such data sets with measurement errors. In this work, we propose to extend the usual linear and Gaussian state-space to analyse time series with irregular time sampling, such as the one obtained when keeping all the satellite observations available at some specific location. We discuss the parameter estimation using a method of moment and the method of maximum likelihood. Simulation results indicate that the method of moment leads to a computationally efficient and numerically robust estimation procedure suitable for initializing the Expectation-Maximisation algorithm, which is combined with a standard numerical...
Tipo: Text Palavras-chave: State-space model; Irregular sampling; Ornstein-Uhlenbeck process; EM algorithm; Sea surface temperature.
Ano: 2011 URL: http://archimer.ifremer.fr/doc/00039/15047/12441.pdf
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SST spatial anisotropic covariances from METOP-AVHRR data ArchiMer
Tandeo, Pierre; Autret, Emmanuelle; Chapron, Bertrand; Fablet, Ronan; Garello, Rene.
The Advanced Very High Resolution Radiometer (AVHRR) instrument on-board the METOP satellite is designed to provide very accurate measurements of Sea Surface Temperature (SST). In this work, using one year of METOP-AVHRR data and a geostatistical approach, we characterize the spatial anisotropy and non-stationarity of the SST variability using oriented ellipsoids. The method is also able to separate the true SST variability from the artificial error introduced by the METOP-AVHRR sensor. These spatial parameters are then used for producing variability atlases (available on-line) over the whole ocean.
Tipo: Text Palavras-chave: SST; METOP-AVHRR; Spatial variability; Anisotropy.
Ano: 2014 URL: http://archimer.ifremer.fr/doc/00165/27586/25818.pdf
Registros recuperados: 6
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