Data-Driven Models for the Spatio-Temporal Interpolation of Satellite-Derived SST Fields

Fablet, Ronan; Viet, Phi Huynh; Lguensat, Redouane

doi:10.1109/tci.2017.2749184

Cited by 33 publications

(72 citation statements)

References 33 publications

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“…Analog methods are one of the first data-driven techniques developed within a data assimilation framework [19]. In our recent study [20][21][22], we proved the relevance of such data-driven approache when addressing the spatio-temporal interpolation of sea surface geophysical tracers. Combining analog data assimilation (AnDA) with a patch-based representation have shown great results with respect to the state-of-the-art OI and EOF-based schemes.…”

Section: Introductionmentioning

confidence: 83%

“…For this reason, a global representation of the spatio-temporal field is most likely to fail due to computational limitations. Following our previous works on analog data assimilation [21,22], we consider a patch-based representation as sketched in Figure 1 (A patch is a P × P subregion of a 2D field with P the width and the height of the patch). This patch-based representation is fully embedded in the considered NN architecture to make explicit both the extraction of the patches from a 2D field and the reconstruction of a 2D field from the collection of patches.…”

Section: Patch-based Nn Architecturementioning

confidence: 99%

“…Compared with the patch-based analog data assimilation [22], it might be noted that we iterate patch-level assimilation steps and global reconstruction steps thanks to the NN-based propagation of the patch-based covariance structure. This procedure potentially allows information propagation from one patch to neighborhing ones after each assimilation step.…”

Section: Algorithm 1 Patch-based Nnkf Reconstructionmentioning

confidence: 99%

“…Overall, the methodological contributions of this work are two-fold: (i) we propose a new probabilistic NN-based representation of 2D geophysical dynamics, (ii) we derive the associated NN-based Kalman filtering scheme for spatio-temporal interpolation issues. We demonstrate the relevance of these contributions with respect to state-of-the-art approaches [5,11,22] for the spatio-temporal interpolation of satellite-derived SST fields in a case study region off South Africa. This region involves complex fine-scale SST dynamics (e.g., fronts, filaments) which can't be retrieved using classical state-of-the-art techniques.…”

Section: Introductionmentioning

confidence: 96%

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Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

et al. 2018

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The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential representativity. From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven framework. In this work we adress this challenge and develop a novel Neural-Network-based (NN-based) Kalman filter for spatio-temporal interpolation of sea surface dynamics. Based on a data-driven probabilistic representation of spatio-temporal fields, our approach can be regarded as an alternative to classical filtering schemes such as the ensemble Kalman filters (EnKF) in data assimilation. Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks, (ii) we derive the associated parametric Kalman-like filtering scheme for a computationally-efficient spatio-temporal interpolation of Sea Surface Temperature (SST) fields. We illustrate the relevance of our contribution for an OSSE (Observing System Simulation Experiment) in a case-study region off South Africa. Our numerical experiments report significant improvements in terms of reconstruction performance compared with operational and state-of-the-art schemes (e.g., optimal interpolation, Empirical Orthogonal Function (EOF) based interpolation and analog data assimilation).

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Section: Introductionmentioning

confidence: 83%

Section: Patch-based Nn Architecturementioning

confidence: 99%

Section: Algorithm 1 Patch-based Nnkf Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

et al. 2018

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“…These works aim at improving spatial classification [3] and interpolation [4] of in situ data. When time is a factor, these data-driven approaches can also use analog Kalman filters [5] and analog Hidden Markov Models (HMMs) [6]. In optical remote sensing, machine learning is used to develop accurate algorithms for specific regional surface waters observed with hyperspectral [7] and multi-spectral [8] sensors.…”

Section: Introductionmentioning

confidence: 99%

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

et al. 2017

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Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled "Hidden" and "Observable". The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) from the Iberian-Biscay-Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error-NRMSE of less than 16%) is the first step in demonstrating the robustness of the method.

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End-to-End Kalman Filter in a High Dimensional Linear Embedding of the Observations

Ouala

Chapron

Collard³

et al. 2022

Mathematics of Planet Earth

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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 may arise when considering the restriction of the data assimilation problems to a small subset of observations, as encountered for instance in ocean surface reconstruction. These limitations motivated the exploration of reconstruction techniques that do not rely on numerical models. In this context, the increasing availability of geophysical observations and model simulations motivates the exploitation of machine learning tools to tackle the reconstruction of ocean surface variables. In this work, we formulate sea surface spatio-temporal reconstruction problems as state space Bayesian smoothing problems with unknown augmented linear dynamics. The solution of the smoothing problem, given by the Kalman smoother, is written in a differentiable framework which allows, given some training data, to optimize the parameters of the state space model.

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Data-Driven Models for the Spatio-Temporal Interpolation of Satellite-Derived SST Fields

Cited by 33 publications

References 33 publications

Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

End-to-End Kalman Filter in a High Dimensional Linear Embedding of the Observations

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