Improving Mesoscale Altimetric Data From a Multitracer Convolutional Processing of Standard Satellite-Derived Products

Fablet, Ronan; Verron, Jacques; Mourre, Baptiste; Chapron, Bertrand; Pascual, Ananda

doi:10.1109/tgrs.2017.2750491

Cited by 15 publications

(13 citation statements)

References 30 publications

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“…We also demonstrate that AnDA can embed complementary variables in a simple manner through the regression variables used in the locally-linear analog forecasting operator. In agreement with our recent analysis [17], we demonstrate that the additional use of large-scale SLA information may further improve the reconstruction performance for fine-scale structures.…”

Section: Resultssupporting

confidence: 91%

“…Interestingly, a significantly greater improvement is obtained when adding the true low-resolution information. The mean results are in accordance with [17], which reported that large-scale SLA information was more informative than SST to improve the reconstruction of the SLA at finer scales. Though mean statistics over one year leads to rather limited improvement, daily RMSE time series ( Figure 6) reveal that for some periods, for instance between day 130 and 150, relative improvements in terms of RMSE may reach 10% with the additional information brought by the large-scale component.…”

Section: Pb-anda Models With Auxiliary Variablessupporting

confidence: 90%

“…Such variables may be considered in the search for analogs as well as regression variables in a locally-linear analog setting. Regarding the targeted application to the reconstruction of SSH fields and the proposed two-scale decomposition (Equation 11), two types of auxiliary variables seem to be of interest: The low-resolution component X to take into account inter-scale relationship [17], and Sea Surface Temperature (SST) with respect to the widely acknowledged SST-SSH synergies [17,19,21]. We also apply patch-level EOF-based decompositions to include both types of variables in the considered analog forecasting models (Equation (13)).…”

Section: Patch-based Analog Dynamical Modelsmentioning

confidence: 99%

“…This has led to a variety of research studies to improve the reconstruction of the altimetric fields. One may cite both methodological alternatives to OI, for instance locally-adapted convolutional models [17] and variational assimilation schemes using model-driven dynamical priors [18], as well as studies exploring the synergy between different sea surface tracers, especially the synergy between SSH and SST (Sea Surface Temperature) fields and Surface Quasi-Geostrophic dynamics [17,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

et al. 2019

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From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.

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Section: Resultssupporting

confidence: 91%

Section: Pb-anda Models With Auxiliary Variablessupporting

confidence: 90%

Section: Patch-based Analog Dynamical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

et al. 2019

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“…The application of the proposed framework to other sea surface geophysical tracers, including multi-source and multi-modal interpolation issues is considered as our first priority. SLA (Sea Level Anomaly) fields could provide an interesting case-study as the associated space-time sampling is particularly scarce and multi-source strategies are of key interest [40]. Improving the formulation and training of the covariance model is also an important issue.…”

Section: Resultsmentioning

confidence: 99%

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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Impact of ocean data assimilation on climate predictions with ICON-ESM

et al. 2022

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We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.

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Improving Mesoscale Altimetric Data From a Multitracer Convolutional Processing of Standard Satellite-Derived Products

Cited by 15 publications

References 30 publications

Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

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

Impact of ocean data assimilation on climate predictions with ICON-ESM

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