Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature
|Author(s)||Ouala Said1, Fablet Ronan1, Herzet Cedric1, 2, Chapron Bertrand3, Pascual Ananda4, Collard Fabrice5, Gaultier Lucile5|
|Affiliation(s)||1 : UBL, IMT Atlantique, Lab STICC, F-29280 Brest, France.
2 : SIMSMART, INRIA Bretagne Atlantique, F-35042 Rennes, France.
3 : IFREMER, LOPS, F-29280 Brest, France.
4 : UIB, CSIC, IMEDEA, Esporles 07190, Spain.
5 : ODL, F-29280 Brest, France.
|Source||Remote Sensing (2072-4292) (Mdpi), 2018-12 , Vol. 10 , N. 12 , P. 1864 (16p.)|
|WOS© Times Cited||15|
|Note||This article belongs to the Special Issue Deep Learning for Remote Sensing|
|Keyword(s)||data assimilation, dynamical model, Kalman filter, neural networks, data-driven models, interpolation|
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).