Multi-platform experiments, numerical simulations and data science techniques for generation of new altimetric products: focus on mesoscale and sub- mesoscale variability (MANATEE – OSTST proposal)

Trabajo presentado en la Ocean Surface Topography Science Team Meeting (OSTST), celebrada online del 19 al 23 de octubre de 2020

Approches pilotées par les données pour la télédétection océanique : de la décomposition non négative d'opérateurs à la reconstruction des dynamiques de la surface de l'océan à partir de données satellitaires

In the last few decades, the ever-growing availability of multi-source ocean remote sensing data has been a key factor for improving our understanding of upper ocean dynamics. In this regard, developing efficient approaches to exploit these datasets is of major importance. Particularly, the decomposition of geophysical processes into relevant modes is a key issue for characterization, forecasting and reconstruction problems. Inspired by recent advances in blind source separation, we aim, in the first part of this thesis dissertation, at extending non-negative blind source separation models to the problem of the observation-based characterization and decomposition of linear operators or transfer functions between variables of interest. We develop mathematically sound and computationally efficient schemes. We illustrate the relevance of the proposed decomposition models in different applications involving the analysis and forecasting of geophysical dynamics. Subsequently, given that the ever-increasing availability of multi-source datasets supports the exploration of data-driven alternatives to classical model-driven formulations, we explore recently introduced data-driven models for the interpolation of geophysical fields from irregularly sampled satellite-derived observations. Importantly, with a view towards the future SWOT mission, the first satellite mission to produce complete two-dimensional wide-swath satellite altimetry observations, we focus on assessing the extent to which SWOT data may lead to an improved reconstruction of altimetry fields.Au cours des dernières années, la disponibilité toujours croissante de données de télédétection multi-source de l'océan a été un facteur clé pour améliorer notre compréhension des dynamiques de la surface de l'océan. A cet égard, il est essentiel de mettre au point des approches efficaces pour exploiter ces ensembles de données. En particulier, la décomposition des processus géophysiques en modes pertinents est une question clé pour les problèmes de caractérisation, de prédiction et de reconstruction. Inspirés par des progrès récents en séparation aveugle des sources, nous visons, dans la première partie de cette thèse, à étendre les modèles de séparation aveugle de sources sous contraintes de non-négativité au problème de la caractérisation et décomposition d'opérateurs ou fonctions de transfert entre variables d'intérêt. Nous développons des schémas computationnels efficaces reposant sur des fondations mathématiques solides. Nous illustrons la pertinence des modèles de décomposition proposés dans différentes applications impliquant l'analyse et la prédiction de dynamiques géophysiques. Par la suite, étant donné que la disponibilité toujours croissante d'ensembles de données multi-sources supporte l'exploration des approches pilotées par les données en tant qu'alternative aux formulations classiques basées sur des modèles, nous explorons des approches basées sur les données récemment introduits pour l'interpolation des champs géophysiques à partir d'observations satellitaires irrégulièrement échantillonnées. De plus, en vue de la future mission SWOT, la première mission satellitaire à produire des observations d'altimétrie par satellite complètement bidimensionnelles et à large fauchée, nous nous intéressons à évaluer dans quelle mesure les données SWOT permettraient une meilleure reconstruction des champs altimétriques

SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High-Frequency Instrument maps

In the present work, we propose a neural-network-based data-inversion approach to reduce structured contamination sources, with a particular focus on the mapmaking for Planck High Frequency Instrument data and the removal of large-scale systematic effects within the produced sky maps. The removal of contamination sources is made possible by the structured nature of these sources, which is characterized by local spatiotemporal interactions producing couplings between different spatiotemporal scales. We focus on exploring neural networks as a means of exploiting these couplings to learn optimal low-dimensional representations, which are optimized with respect to the contamination-source-removal and mapmaking objectives, to achieve robust and effective data inversion. We develop multiple variants of the proposed approach, and consider the inclusion of physics-informed constraints and transfer-learning techniques. Additionally, we focus on exploiting data-augmentation techniques to integrate expert knowledge into an otherwise unsupervised network-training approach. We validate the proposed method on Planck High Frequency Instrument 545 GHz Far Side Lobe simulation data, considering ideal and nonideal cases involving partial, gap-filled, and inconsistent datasets, and demonstrate the potential of the neural-network-based dimensionality reduction to accurately model and remove large-scale systematic effects. We also present an application to real Planck High Frequency Instrument 857 GHz data, which illustrates the relevance of the proposed method to accurately model and capture structured contamination sources, with reported gains of up to one order of magnitude in terms of performance in contamination removal. Importantly, the methods developed in this work are to be integrated in a new version of the SRoll algorithm (SRoll3), and here we describe SRoll3 857 GHz detector maps that were released to the community

Non-negative Observation-based Decomposition of Operators

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

Non-negative decomposition of geophysical dynamics

International audienceThe decomposition of geophysical processes into relevant modes is a key issue for characterization, forecasting and reconstruction problems. The blind separation of contributions from different sources is a well-studied problem in signal and image processing. Recently, significant advances have been reported with the introduction of non-negative and sparse formulations. In this work, we address an extension to the blind decomposition of linear operators or transfer functions between variables of interest with an emphasis on a non-negative setting. As illustrated here, such decompositions are of key interest for the analysis of geophysical dynamics and the relationships between different geophysical variables

Décomposition Non-négative de Dynamiques Géophysiques

National audienceLa décomposition des processus géophysiques en modes pertinents est un point clé pour les problèmes de caractérisation, prédiction et reconstruction dans le domaine des sciences de l'environnement. Par ailleurs, la séparation aveugle des contributions associées à différentes sources est un problème classique dans le domaine du traitement de signal et des images. Récemment, des progrès significatifs ont été obtenus avec l'introduction de formulations non-négatives et parcimonieuses. Dans ce travail, nous abordons la décomposition aveugle d'opérateurs linéaires ou de fonctions de transfert entre variables d'intérêt, en mettant l'accent sur un cadre non négatif. Nous illustrons l'intérêt de ces décompositions pour l'analyse, la prédiction et la reconstruction de dynamiques géophysiques

Non-negative and Sparse Decomposition of Geophysical Dynamics

International audienceThe growing availability of multi-source environmental data (remote sensing data, numerical simulations, in situ data, etc.) paves the way for the development of novel data-driven models and strategies for the characterization, reconstruction and forecasting of geophysical dynamics. In this context, the observation-driven identification and separation of contributions and operators associated with different geophysical sources or processes is a key issue. Following significant advances reported in signal processing with the introduction of non-negative and sparse formulations, we address this issue from the blind decomposition of linear operators or transfer functions between variables or processes of interest. The proposed scheme relies on multiple superimposing linear regressions and on their calibration from the observed data. We explore locally-adapted multi-modal regression models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. This is regarded as a key feature to improve model calibration robustness. We illustrate and demonstrate the relevance of such decompositions for the analysis and reconstruction of geophysical dynamics. We first address forecasting issues. Using Lorenz ‘96 dynamical system as case-study, we introduce the blind dictionary-based decomposition of local linear operators. Our numerical experiments resort to improved forecasting performance when dealing with small-sized and noisy observation datasets. A second application addresses the super-resolution of irregularly-sampled ocean remote sensing images. We focus on the reconstruction of high-resolution Sea Surface Height (SSH) from the synergy between along-track altimeter data, OI-interpolated SSH fields and satellite-derived high-resolution Sea Surface Temperature (SST) fields. The reported experiments, for a case study region in the Western Mediterranean Sea, demonstrate the relevance of the proposed model, specially of locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions

Learning multi-tracer convolutional models for the reconstruction of high-resolution SSH fields

International audienceThis paper addresses the reconstruction of high-resolution Sea Surface Height (SSH) from the synergy between along-track altimeter data, OI-interpolated SSH fields and satellite-derived high-resolution Sea Surface Temperature (SST) fields. We aim at better resolving the fine-scale range, typically below 100km, which remains scarcely resolved by operational optimal interpolation schemes. The proposed scheme relies on multi-tracer convolutional models and on their calibration from the observed along-track data. We explore a dictionary-based decomposition of the convolutional models to improve the robustness of the calibration. We report a numerical evaluation using an Observation Simulation System Experiment (OSSE) for a case study region in the western Mediterranean sea. Our numerical experiments demonstrate that we can improve reconstruction performance by about 20\%, in terms of mean square error, compared to optimally-interpolated fields. Dictionary-based decompositions also resort to similar potential improvement. We further analyze different parameterizations of the convolution models in relation to physical priors (e.g., SGQ dynamics, isotropical transfer functions)

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

International audienceThe 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

Assimilation par Analogues de Données Altimétriques Nadir et SWOT dans la Mer Méditerranée Occidentale

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