Ronan Fablet

• Professor
• Professor (Full) at IMT Atlantique

In marine ecosystems, like most natural systems, patchiness is the rule. A characteristic of pelagic ecosystems is that their 'substrate' consists of constantly moving water masses, where ocean surface turbulence creates ephemeral oases. Identifying where and when hotspots occur and how predators manage those vagaries in their preyscape is challeng...

Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach--referred to as GeoTrackNet--for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of...

Data assimilation is a key component of operational systems and scientific studies for the understanding, modeling, forecasting and reconstruction of earth systems informed by observation data. Here, we investigate how physics‐informed deep learning may provide new means to revisit data assimilation problems. We develop a so‐called end‐to‐end learn...

The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables. These latent variables include unresolved small scales and/or rapidly evolving processes, partially observed couplings, or forcings in coupled systems. This is the case in ocean-atmosphere dynamics, for which u...

The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher re...

Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these ecosystem dynamics, especially in scenarios where gap-free ground-truth data is lacking, which poses a challenge for designing effective training frameworks. Using an adva...

Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of int...

In marine sciences, accurately mapping Suspended Particulate Matter (SPM) in the ocean is important in understanding the dynamics of primary production and managing its ecological impact. In recovering SPM spatio-temporal distribution, deterministic methods like the end-to-end neural variational data assimilation system, 4DVarNet, while robust, oft...

Real-time observation of ocean surface topography is essential for various oceanographic applications. Historically, these observations have mainly relied on satellite nadir altimetry data, which were limited to observation scales greater than approximately 60 km. However, the recent launch of the wide-swath Surface Water Ocean Topography (SWOT) mi...

Wind speed at the sea surface is a key quantity for a variety of scientific applications and human activities. For its importance, many observation techniques exist, ranging from in situ to satellite observations. However, none of such techniques can capture the spatiotemporal variability of the phenomenon at the same time. Reanalysis products, obt...

Defining end-to-end (or online) training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcom...

The gravitational pump plays a key role in the ocean carbon cycle by exporting sinking organic carbon from the surface to the deep ocean. Deep sediment trap time-series provide unique measurements of this sequestered carbon flux. Sinking particles are influenced by physical short-term spatio-temporal variability, which inhibits the establishment of...

The ocean's biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure deep-carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surfac...

Real-time observation of ocean surface topography is essential for various oceanographic applications. Historically, these observations relied mainly on satellite nadir altimetry data, which were limited to observe scales greater than approximately 60 km. However, the recent launch of the wide-swath SWOT mission in December 2022 marks a significant...

Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different prod...

Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space‐time interpolation problems. However, the training of state‐of‐the‐art neural schemes on real‐world case‐studies i...

The safe and efficient execution of offshore operations requires short-term (1 to 6 hours ahead) high-quality probabilistic forecasts of metocean variables. The development areas for offshore wind projects, potentially in high depths, make it difficult to gather measurement data. This paper explores the use of deep learning for wind speed forecasti...

We simulate Lagrangian drift on the sea surface and investigate deep learning approaches to address the shortcomings of current model-based and Markovian approaches, particularly concerning error propagation and computational complexity. We present a novel deep learning framework, referred to as DriftNet, inspired by the Eulerian Fokker-Planck repr...

Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the underlying physical equations make i...

Satellite altimetry offers a unique approach for direct sea surface current observation, but it is limited to measuring the surface‐constrained geostrophic component. Ageostrophic dynamics, prevalent at horizontal scales below 100 km and time scales below 10 days, are often underestimated by ocean reanalyzes employing data assimilation schemes. To...

Extracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide‐swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. However, SSH observations derived from wide‐swath altimetry are characterized by high spatial resolution while relat...

Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for traditional interpolation techniques. To address this, we developed the CLuster Optimal Interpolation Neural Network (CLOINet), which combines the robust mathematical framework of the Optimal In...

Vessel trajectory prediction plays a pivotal role in numerous maritime applications and services. While the Automatic Identification System (AIS) offers a rich source of information to address this task, forecasting vessel trajectory using AIS data remains challenging, even for modern machine learning techniques, because of the inherent heterogeneo...

Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA’s Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainfall events. However, their obs...

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying mesoscale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which provide 1-D-only along-track satellite observations of the SSH. The surface water and ocean topography (S...

The ocean biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure the deep carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surf...

In geosciences, data assimilation (DA) addresses the reconstruction of a hidden dynamical process given some observation data. DA is at the core of operational systems such as weather forecasting, operational oceanography and climate studies. Beyond the reconstruction of the mean or most likely state, the inference of the state posterior distributi...

The radius of maximum wind ( R max ), an important parameter in tropical cyclones (TCs) ocean surface wind structure, is currently resolved by only a few sensors, so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semi-empirical model relying on an outer wind radius, intensity and latitude was fit to b...

Wind speed retrieval at the sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea-surface winds produce sounds that propagate underwater, underwater acousti...

The problem of optimal sensor placement is central to data assimilation and geoscience. Recently, it has been shown that the use of volume-preserving generative models allows for the construction of non-Gaussian probabilistic models that preserve differential Shannon entropy. This provides an interesting criterion for uncertainty quantification. In...

Sea surface temperature (SST) is a critical factor in the global climate system and plays a key role in many marine processes. Understanding the variability of SST is therefore important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management. In this study, we use machine le...

We ran numerical experiments for the reconstruction of sea surface turbidity dynamics using a daily 1km resolution multisensor (MODIS, VIIRS, OLCI) L3 satellite product from 2019 to 2021, off the French coast, in the western Mediterranean Sea. Here, we explored neural interpolation schemes as an approach to conduct image gap filling. Among state-of...

Uncertainty quantification (UQ) plays a crucial role in data assimilation (DA) since it impacts both the quality of the reconstruction and near-future forecast. However, traditional UQ approaches are often limited in their ability to handle complex datasets and may have a large computational cost. In this paper, we present a new ensemble-based appr...

The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides...

Generating accurate extremes from an observational data set is crucial when seeking to estimate risks associated with the occurrence of future extremes which could be larger than those already observed. Applications range from the occurrence of natural disasters to financial crashes. Generative approaches from the machine learning community do not...

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning...

{Bernard O. Koopman proposed an alternative view of dynamical systems based on linear operator theory, in which the time evolution of a dynamical system is analogous to the linear propagation of an infinite-dimensional vector of observables. In the last few years, several works have shown that finite-dimensional approximations of this operator can...

The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Water and Ocean and Topography) altimeter mission. Operational systems, however, generally fail to retrieve mesoscale dynamics for horizontal scales below 100 km and timescales be...

Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning...

Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has difficulties accounting for non-wind processes such as rain events. Convolutional neural network, on the other hand, have the capacity to use contextual information and...

Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability of phytoplankton biomass (e.g. decadal variability). Machine learning models such as Support Vector Regression (SVR) or Multi-Layer Perceptron (MLP)...

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern preven...

Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has difficulties accounting for non-wind processes such as rain events. Convolutional neural network, on the other hand, have the capacity to use contextual information and...

Satellite altimetry is a unique way for direct observations of sea surface dynamics. This is however limited to the surface-constrained geostrophic component of sea surface velocities. Ageostrophic dynamics are however expected to be significant for horizontal scales below 100~km and time scale below 10~days. The assimilation of ocean general circu...

Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences. With the influx of more satellite missions, we have access to more and more observations and it is becoming more pertinent to take advantage of these observations in applications such as forecasting and reanalysis. Wit...

We address Lagrangian drift simulation in geophysical dynamics and explore deep learning approaches to overcome known limitations of state-of-the-art model-based and Markovian approaches in terms of computational complexity and error propagation. We introduce a novel architecture, referred to as DriftNet, inspired from the Eulerian Fokker-Planck re...

The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and Water Topography) altimeter mission. Operational systems however generally fail to retrieve mesoscale dynamics for horizontal scales below 100 km and time-scale below 10...

The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides...

The authors wish to make the following corrections to the paper [...]

The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and Water Topography) altimeter mission. Operational systems however generally fail to retrieve mesoscale dynamics for horizontal scales below 100km and time-scale below 10...

Combining remote-sensing data with in-situ observations to obtain a full 3D reconstruction of the ocean state is challenging for classical interpolation techniques. Therefore, we developed a CLuster Optimal Interpolation Neural Network (CLOINet), which leverages the well-consolidated mathematical basis of the Optimal Interpolation (OI) scheme with...

Uncertainty quantification in ill-posed inverse problems is a critical issue in a variety of scientific domains, including among others signal processing, imaging science, geo-science, remote sensing.... This has led to a variety of approaches , especially using Bayesian schemes such as Kalman methods, particle filtering schemes and variational Bay...

Data assimilation techniques are the state-of-the-art approaches in the reconstruction of a spatio-temporal geophysical state such as the atmosphere or the ocean. These methods rely on a numerical model that fills the spatial and temporal gaps in the observational network. Unfortunately, limitations regarding the uncertainty of the state estimate m...

Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstr...

The characterization of suspended sediment dynamics in the coastal ocean provides key information for both scientific studies and operational challenges regarding, among others, turbidity, water transparency and the development of micro-organisms using photosynthesis, which is critical to primary production. Due to the complex interplay between nat...

Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea surface winds produce sounds that propagate underwater, underwater acoustics r...

Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation measurements of rainfall events. However, the ob...

Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents, the associated sampling pattern prevents f...

The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher re...

For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest. A variety of satellite sensors deliver observation data with different sampling patterns due satellite orbits and/or their sensitivity to atmospheric conditions (e.g., clour cover, hea...

Despite the ever-growing amount of ocean’s data, the interior of the ocean remains under sampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean recon...

At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird t...

Miniature electronic devices have recently enabled ecologists to document relatively large amounts of animal trajectories. Modelling such trajectories may contribute to explaining the mechanisms underlying observed behaviours and to clarifying ecological processes at the scale of the population by simulating multiple trajectories. Existing approach...

Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are d...

Ocean processes can locally modify the upper ocean density structure, leading to an attenuation or a deflection of sound signals. Among these phenomena, eddies cause significant changes in acoustic properties of the ocean; this suggests a possible characterization of eddies via acoustics. Here, we investigate the propagation of sound signals in the...

Modeling the subgrid-scale dynamics of reduced models is a long standing open problem that finds application in ocean, atmosphere and climate predictions where direct numerical simulation (DNS) is impossible. While neural networks (NNs) have already been applied to a range of three-dimensional problems with success, the backward energy transfer of...