Hidden surface dynamical modes and SSH retrievals from a joint analysis of altimetry and microwave SST

International audienceThe availability of daily satellite Sea Surface Temperature (SST) data and theoretical results advocate for new methods to retrieve the Sea Surface Height (SSH) and the surface geostrophic currents from SST observations. The underlying hypothesis comes to assume that the local variations of the SST relate to the surface currents. Ocean turbulence models, such as the Surface Quasi Geostrophic (SQG) theory, or statistical methods like neural networks or latent class regressions provide different means to state the SST-SSH relationships. This later approach has the advantage to be completely parametric and to account for different transfer functions between SST and SSH. It relies on a conditional setting with respect to a hidden variable related to different dynamical modes at the surface of the ocean. In this paper, we aim at further developing such latent models with an emphasis on two aspects: (i) the modeling and learning of the spatio-temporal dynamics of the hidden dynamical modes using Markovian priors, (ii) the reconstruction of daily SSH fields from a joint analysis of microwave SST and altimetry observation series. We evaluate the proposed model both qualitatively and quantitatively with respect to the reference altimetry product

Ocean surface current retrieval using a non homogeneous Markov-switching multi-regime model

International audienceThis paper addresses the reconstruction of sea surface currents from satellite ocean sensing data. Whereas the classical surface currents derived from the SSH (Sea Surface Height) products are rather low space-time resolution fields (typically, 50 km and 12-day actual space-time grid resolution), we investigate the extent to which we can retrieve sea surface currents at higher resolution using daily SST (Sea Surface Temperature) satellite observations. State-of-the-art methods, which exploit classical optical flow schemes or nonlinear regression techniques, do not provide satisfactory results due to the space-time variabilities of the relationships between the SST and the sea surface current. Motivated by our recent joint SST-SSH identification of characterization of upper ocean dynamical modes, we here show that a multi-regime model, formally stated as a Markov-switching latent class regression model, provides a relevant model to capture the above-mentioned variabilities and reconstruct SST-driven sea surface currents. The considered case study within the Agulhas current demonstrates that our model retrieves high-resolution space-time details which cannot be resolved by the classical SSH-derived products

The analog data assimilation

In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.Fil: Lguensat, Redouane. Université Bretagne Loire; FranciaFil: Tandeo, Pierre. Université Bretagne Loire; FranciaFil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; FranciaFil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: Fablet, Ronan. Université Bretagne Loire; Franci

Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures

International audienceIn this work, we propose a novel non parametric method for the temporally consistent inpainting of dynamic texture sequences. The inpainting of texture image sequences is stated as a stochastic assimilation issue, for which a novel model-free and data-driven Ensemble Kalman method is introduced. Our model is inspired by the Analog Ensemble Kalman Filter (AnEnKF) recently proposed for the assimilation of geophysical space-time dynamics, where the physical model is replaced by the use of statistical analogs or nearest neighbours. Such a non-parametric framework is of key interest for image processing applications, as prior models are seldom available in general. We present experimental evidence for real dynamic texture that using only a catalog database of historical data and without having any assumption on the model, the proposed method provides relevant dynamically-consistent interpolation and outperforms the classical parametric (autoregressive) dynamical prior

The analog data assimilation: application to 20 years of altimetric data

International audienceThe reconstruction of geophysical dynamics remain key challenges in ocean, atmosphere and climate sciences. Data assimilation methods are the state-of-theart techniques to reconstruct the space-time dynamics from noisy and partial observations. They typically involve multiple runs of an explicit dynamical model and may have severe operational limitations, including the computational complexity, the lack of model consistante with respect to the observed data as well as modeling uncertainties. Here, we demonstrate how large amount of historical satellite data can open new avenues to address data assimilation issues, and to develop a fully data-driven assimilation. Assuming that a representative catalog of historical state trajectories is available, the key idea is to use the analog method to propose forecasts with no online evaluation of any physical model. The combination of these analog forecasts with observations resorts to classical stochastic filtering methods. For illustration of the proposed analog data assimilation, the brute force use of 20 years of altimetric data is demonstrated to reconstruct mesoscale sea surface dynamics

Spatio-temporal interpolation of Sea Surface Temperature using high resolution remote sensing data

International audienceIn this work, we present a statistical model to generate relevant reanalysis of geophysical parameters. In particular, we use a stochastic equation to control the temporal and spatial variability of the signal and we take into account the possible error of the observations. We resolve the system iteratively using an ensemble Kalman filter and smoother. We apply the methodology to remote sensing data of Sea Surface Temperature (SST). We use high resolution SST maps provided by an infrared sensor, sensible to the presence of cloud. Comparing the results with the reference SST reanalysis, we demonstrate the capability of our approach to interpolate missing data and keep into account the spatial and temporal consistency of the SST signal

The Analog Ensemble Kalman Filter and Smoother

International audienceThe amount of observational and model-simulated data in geosciences has grown rapidly since the early 1980s. These data, still widely underexploited, has a unique potential for the modeling and prediction of geophysical space-time dynamics. Here, we show how a statistical emulator, based on a catalog of historical datasets, and a sequential Monte Carlo filter and smoother, provide a relevant data-driven analog assimilation of complex dynamics. As an illustration, we consider the chaotic Lorenz-63 model

Segmentation of mesoscale ocean surface dynamics using satellite SST and SSH observations

International audienceMulti-satellite measurements of altimeter-derived Sea Surface Height (SSH) and Sea Surface Temperature (SST) provide a wealth of information about ocean circulation, especially mesoscale ocean dynamics which may involve strong spatio-temporal relationships between SSH and SST fields. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics may be decomposed into a mixture of dynamical modes, characterized by different local regressions between SSH and SST fields. Formally, we develop a novel latent class regression model to identify dynamical modes from joint SSH and SST observation series. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatio-temporal segmentation of the upper ocean dynamics

Spatio-temporal segmentation and estimation of ocean surface currents from satellite sea surface temperature fields

International audienceThe use of satellite Sea Surface Temperature (SST) fields to retrieve zonal and meridional surface currents (U,V) is now a widespread idea. Since the classical approach involves temporal differencing of SST fields, we investigate in this paper the extent to which mesoscale ocean dynamics may be decomposed into a superposition of dynamical modes, characterized by different linear relationships between surface currents and temperature fields. Based on a completely observation-driven approach, we propose a latent class regression model from local satellite surface currents and patches of SST measurements. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatio-temporal segmentation and tracking of the ocean surface dynamical modes. Moreover, we show the accuracy of the proposed model to predict mesoscale surface currents from SST single maps

Interpolated swell fields from SAR measurements

International audienceSynthetic Aperture Radar (SAR) sensors on-board satellites are very well suited for observing sea surface geophysical parameters such as ocean swell. But on a very large scale, SAR data are too sparse for deriving some global information. From the original work of Collard et al. (2009), and following some generic assumption on the physics of the swell propagation in deep water, it was shown that using a back-propagating scheme, it was possible to retrieve the source of the swell system and then generate a propagating field. In this paper, we are proposing a simpler and original approach, by assimilating the SAR data into a given swell field and then using a Kalman Filter/Smoother technique for updating the main parameters of the swell (wavelength, direction, and significant wave height) within the complete field. This method shows very encouraging results which will be confronted with in situ measurements when available