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133 results on '"Aissa-El-Bey, Abdeldjalil"'

1. MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators

Author

Kern, Jonathan, Henwood, Sébastien, Mordido, Gonçalo, Dupraz, Elsa, Aïssa-El-Bey, Abdeldjalil, Savaria, Yvon, and Leduc-Primeau, François

Subjects
Computer Science - Machine Learning, Computer Science - Emerging Technologies, Computer Science - Neural and Evolutionary Computing
Abstract

Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators. However, computations in memristors suffer from hardware non-idealities and are subject to different sources of noise that may negatively impact system performance. In this work, we theoretically analyze the mean squared error of DNNs that use memristor crossbars to compute MVM. We take into account both the quantization noise, due to the necessity of reducing the DNN model size, and the programming noise, stemming from the variability during the programming of the memristance value. Simulations on pre-trained DNN models showcase the accuracy of the analytical prediction. Furthermore the proposed method is almost two order of magnitude faster than Monte-Carlo simulation, thus making it possible to optimize the implementation parameters to achieve minimal error for a given power constraint., Comment: To be presented at AICAS 2022

Published
2022

2. Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Author

Kern, Jonathan, Dupraz, Elsa, Aïssa-El-Bey, Abdeldjalil, Varshney, Lav R., and Leduc-Primeau, François

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the computations, and develop an error propagation model that takes into account these two sources of errors. In addition to providing updated Kalman filter equations, the proposed error model accurately predicts the covariance of the estimation error and gives a relation between the performance of the filter and its energy consumption, depending on the noise level in the memories. Then, since memories are responsible for a large part of the energy consumption of embedded systems, optimization methods are introduced so as to minimize the memory energy consumption under a desired estimation performance of the filter. The first method computes the optimal energy levels allocated to each memory bank individually, and the second one optimizes the energy allocation per groups of memory banks. Simulations show a close match between the theoretical analysis and experimental results. Furthermore, they demonstrate an important reduction in energy consumption of more than 50%., Comment: 29 pages, 8 figures, Submitted to IEEE Transactions on Signal Processing

Published
2021

7. Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Author

López-Radcenco, Manuel, Fablet, Ronan, Aïssa-El-Bey, Abdeldjalil, and Ailliot, Pierre

Subjects
Statistics - Machine Learning
Abstract

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions., Comment: 4 pages, 3 figures

Published
2017

16. Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data

Author

Frion, Anthony, Drumetz, Lucas, Dalla Mura, Mauro, Tochon, Guillaume, Aissa El Bey, Abdeldjalil, Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Océan Dynamique Observations Analyse (ODYSSEY), Université de Bretagne Occidentale - UFR Sciences et Techniques (UBO UFR ST), Université de Brest (UBO)-Université de Brest (UBO)-Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Laboratoire de Recherche et de Développement de l'EPITA (LRDE), Ecole Pour l'Informatique et les Techniques Avancées (EPITA), Equipe Communication System Design (Lab-STICC_COSYDE), ANR-21-CE48-0005,LEMONADE,Apprentissage et modélisation de la dynamique spectrale de séries temporelles d'images satellitaires(2021), Université de Brest (UBO)-Université de Brest (UBO)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Inria Rennes – Bretagne Atlantique

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Continuous dynamical systems, Dynamical Systems (math.DS), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), Data assimilation, FOS: Mathematics, [INFO]Computer Science [cs], [MATH]Mathematics [math], Mathematics - Dynamical Systems, Learning dynamical systems, Koopman operator, Forecasting
Abstract

International audience; Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.

Published
2023

17. Fast and Accurate Output Error Estimation for Memristor-Based Deep Neural Networks

Author

Kern, Jonathan, Henwood, Sebastien, Mordido, Goncalo, Dupraz, Elsa, Aissa-El-Bey, Abdeldjalil, Savaria, Yvon, and Leduc-Primeau, Francois

Abstract

Memristors allow computing in memory, which may be leveraged by deep neural network (DNN) accelerators to reduce energy footprint. However, such gains in energy efficiency come at the cost of noise on the computation results due to the analog nature of memristors. In this work, we introduce a theoretical framework to estimate the mean squared error (MSE) of a memristor-based DNN. We propose an efficient software implementation of this framework which is shown to be orders of magnitude faster than using Monte-Carlo simulations. Additionally, we study two different techniques for mapping convolutional layers to memristors and compare their relative impact on the mean squared error and its computation time. The accuracy of the proposed analysis is first evaluated on a simple regression problem, and then on a more complex classification task with a network capable of achieving high accuracy on the CIFAR-10 dataset, which shows that our method is efficient over practical up-to-date DNNs. The proposed framework is then used to perform a meta-heuristic optimization of the memristor maximal conductance value so as to minimize the energy usage.1The code for the framework presented in this work is available at https://github.com/sebastienwood/MemSE

Published
2024
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20. On the Identifiability Testing in Blind Source Separation Using Resampling Technique

Author

Aïssa-El-Bey, Abdeldjalil, Abed-Meraim, Karim, Grenier, Yues, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rosca, Justinian, editor, Erdogmus, Deniz, editor, Príncipe, José C., editor, and Haykin, Simon, editor

Published
2006
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26. Underdetermined blind separation of nondisjoint sources in the time-frequency domain

Author

Aissa-El-Bey, Abdeldjalil, Linh-Trung, Nguyen, Abed-Meraim, Karim, Belouchrani, Adel, and Grenier, Yves

Subjects
Signal processing -- Methods, Time-domain analysis -- Usage, Vector spaces -- Analysis, Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The time-frequency (TF) representations are used to introduce new methods for the underdetermined blind source separation (UBSS) of TF-nondisjoint nonstationary sources. The advantages of the new separation algorithms include a weaker assumption on the source 'sparseness' and an explicit treatment of the overlapping points using subspace projection, which has improved the performance.

Published
2007

34. Analog data assimilation of along-track nadir and wide-swath SWOT altimetry observations in the Western Mediterranean sea

Author

Lopez-Radcenco, Manuel, Pascual, Ananda, Gomez-Navarro, Laura, Aissa-El-Bey, Abdeldjalil, Chapron, Bertrand, Fablet, Ronan, Lopez-Radcenco, Manuel, Pascual, Ananda, Gomez-Navarro, Laura, Aissa-El-Bey, Abdeldjalil, Chapron, Bertrand, and Fablet, Ronan

Abstract

The growing availability of ocean data brought forth by recent advancements in remote sensing, in situ measurements, and numerical models supports the development of data-driven strategies as a powerful, computationally efficient alternative to model-based approaches for the interpolation of high-resolution, gap-free, regularly gridded sea surface geophysical fields from partial satellite-derived observations. In this paper, we investigate such data-driven strategies for the spatio-Temporal interpolation of sea level anomaly (SLA) fields in the Western Mediterranean Sea from satellite-derived altimetry data. We introduce and evaluate the analog data assimilation (AnDA) framework, which exploits patch-based analog forecasting operators within a classic Kalman-based data assimilation scheme. With a view toward the upcoming wide-swath surface water and ocean topography (SWOT) mission, two different types of altimetry data are assimilated: Along-Track nadir data and wide-swath SWOT altimetry data. Using an observing system simulation experiment, we demonstrate the relevance of AnDA as an improved interpolation method, particularly for mesoscale features in the 20-to 100-km horizontal scale range. Results report an SLA reconstruction RMSE (correlation) improvement of 42% (14%) with respect to optimal interpolation, and show a clear gain when the joint assimilation of SWOT and along-Track nadir observations are considered.

Published
2019

35. Can SWOT Improve the Reconstruction of Sea Level Anomaly Fields? Insights for Data-driven Approaches in the Western Mediterranean Sea

Author

Lopez Radcenco, Manuel, Pascual, Ananda, Gomez-Navarro, Laura, Aissa El Bey, Abdeldjalil, Chapron, Bertrand, Fablet, Ronan, Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Institut Mediterrani d'Estudis Avancats (IMEDEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de las Islas Baleares (UIB), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Lab-STICC_IMTA_CACS_COM, Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Sea surface, Radar altimetry, Sig-nal sampling, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Remote sensing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Interpolation
Abstract

Current generation satellite altimetry missions have played a fundamental role in improving our understanding of sea surface dynamics, despite only being able to provide measurements along the satellite track. In this respect, the future SWOT altimetry mission will be the first mission to produce complete two-dimensional wide-swath satellite observations. With a view towards the upcoming SWOT mission launch, we explore the potential of SWOT observations to improve the reconstruction of high-resolution sea level anomaly (SLA) fields from satellite-derived data. Given the ever-increasing availability of multi-source datasets that supports the exploration of data-driven alternatives to classical model-driven formulations, we focus here on recently introduced data-driven models for the interpolation of geophysical fields. Using an Observing System Simulation Experiment (OSSE), we demonstrate the relevance of SWOT observations to better constraint data-driven interpolation models in order to improve the reconstruction of mesoscale features. Reported results suggest that SWOT observations can provide more information than currently available nadir along-track al-timetry observations and show an additional SLA reconstruction performance improvement when the the joint assimilation of SWOT and nadir along-track observations is considered.

Published
2018

36. Non-negative Observation-based Decomposition of Operators

Author

Lopez Radcenco, Manuel, Fablet, Ronan, Aissa El Bey, Abdeldjalil, Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CACS_COM, Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)

Subjects
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

The problem of observation-based characterization of operators, closely related to the well-studied problem of blind source separation, remains nonetheless considerably less studied. Inspired by the recent success of non-negative and sparse blind source separation, we aim at extending constrained blind source separation models to the data-driven characterization of operators. We introduce a novel non-negative decomposition model for linear operators and investigate different parameter estimation algorithms. We study and compare the proposed algorithms in terms of identification and reconstruction performance in a variety of experimental settings, in order to gain insight into the robustness and limitations of the proposed algorithms. We further discuss the main contribution of our approach compared with state-of-the-art methods for the analysis and decomposition of operators.

Published
2018

37. Analog data assimilation of along-track nadir and SWOT altimetry observations in the Western Mediterranean Sea

Author

Lopez Radcenco, Manuel, Pascual, Ananda, Gomez-Navarro, Laura, Aissa El Bey, Abdeldjalil, Fablet, Ronan, Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Instituto Mediterráneo de Estudios Avanzados (CSIC-UIB) (IMEDEA), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Lab-STICC_IMTA_CACS_COM, École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), and IMT Atlantique (IMT Atlantique)

Subjects
Spatio-temporal interpolation, Sea Level Anomaly, Altimetry data, Data assimilation, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], SWOT, Data-driven model, Western Mediterranean Sea, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The growing availability of ocean data brought forth by recent advancements in remote sensing, in situ measurements and numerical models supports the development of data-driven strategies as a powerful, computationally-efficient alternative to model-based approaches for the interpolation of high-resolution, gap-free, regularly-gridded sea surface geophysical fields from partial satellite-derived observations. In this work, we investigate such data-driven strategies for the spatio-temporal interpolation of Sea Level Anomaly (SLA) fields in the Western Mediterranean Sea from satellite-derived altimetry data. We develop and evaluate an Analog Data Assimilation (AnDA) framework, which exploits patch-based analog forecasting operators. Two different types of altimetry data are assimilated: along-track nadir data and wide-swath SWOT altimetry data. Using an OSSE, we analyze the sensitivity of the proposed framework to its most relevant internal parameters. We demonstrate the relevance of AnDA as an improved interpolation method, particularly for mesoscale features in the 20-100 km horizontal scale range. Results report an SLA reconstruction RMSE (correlation) improvement of 42% (12%) with respect to Optimal Interpolation (OI), and suggest an additional potential gain when the joint assimilation of SWOT and along-track nadir observations is considered.

Published
2018

46. Non-negative Decomposition of Sea Surface Dynamics from Multi-source Ocean Remote Sensing Data

Author

Lopez Radcenco, Manuel, Aissa El Bey, Abdeldjalil, Ailliot, Pierre, Tandeo, Pierre, Fablet, Ronan, Lab-STICC_TB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Département Signal et Communications ( SC ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Lab-STICC_TB_CACS_COM, Laboratoire de Mathématiques de Bretagne Atlantique ( LMBA ), Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (SC), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Brest (UBO)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS), Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque, and Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Linear decomposition, [PHYS.PHYS.PHYS-GEO-PH] Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Remote sensing, Multi-source satellite data, Non-negativity, Linear regression, Gaussian mixture, EM algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Physics::Atmospheric and Oceanic Physics, Finite mixture model, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The growing availability of multi-source ocean remote sensing data is a key factor for improving our understanding of upper ocean dynamics, ocean circulation and atmospheric-ocean interactions. Following an ongoing body of work that investigates mesoscale upper ocean dynamics from linear couplings between SST (sea surface temperature) and SSH (sea surface height), we propose a novel observation-driven framework for the identification and characterization of sea surface dynamical modes. It relies on a multi-modal decomposition of SST-SSH relationships. Our findings suggest that upper ocean dynamics may be decomposed as the superimposition of several dynamical modes, rather than mutually exclusive ones as investigated in previous work. Our study stresses the relevance of a non-negative bi-modal additive decomposition to capture the complex space-time variability of mesoscale upper ocean dynamics.

Published
2016

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