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

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

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

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 figure

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

Mascaret: Pedagogical multi-agents system for virtual environment for training.

International audienceThis study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we explain how we integrated, in MASCARET, models necessary to the creation of Intelligent Tutoring System. We notably incorporate pedagogical strategies and pedagogical actions. We present pedagogical agents. To validate our model, the SÉCURÉVI application for fire-fighters training is developed

On the assimilation of SWOT type data into 2D shallow-water models

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission