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
复制标题
基于神经网络的卡尔曼滤波器用于卫星海面温度的时空插值
DOI：
10.3390/rs10121864
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
L. Gaultier
中科院分区：
文献类型：
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作者：
Said Ouala;Ronan Fablet;C. Herzet;B. Chapron;A. Pascual;F. Collard;L. Gaultier
海洋动力学的预测和重建是一个关键的挑战。而模型驱动策略仍然是时空动力学重构的主流方法。海洋学数据收集的不断增加提高了数据驱动方法作为时空场重建的计算效率表示的相关性。这种工具被证明优于经典的最先进的插值技术，如最佳插值和DINEOF在检索的精细尺度结构，同时仍然是计算效率相比，基于模型的数据同化方案。然而，将这种数据驱动的先验耦合到经典滤波方案限制了它们的潜在代表性。从这个角度来看，机器学习，特别是神经网络和深度学习的最新进展可以为数据驱动框架内的动态建模和插值提供新的基础设施。在这项工作中，我们解决这一挑战，并开发了一种新的基于神经网络（NN为基础的）卡尔曼滤波器的海平面动态的时空插值。基于数据驱动的时空场的概率表示，我们的方法可以被视为一种替代经典的滤波方案，如集合卡尔曼滤波器（EnKF）在数据同化。总体而言，所提出的方法的主要特点是双重的：（i）我们提出了一种新的架构的随机表示的二维（2D）地球物理动力学的神经网络的基础上，（ii）我们推导出相关的参数卡尔曼滤波方案的计算效率高的时空插值的海表面温度（SST）字段。我们说明了我们的贡献的OSSE（观测系统模拟实验）在南非的案例研究区域的相关性。我们的数值实验报告了与操作和最先进的方案（例如，最优插值、基于经验正交函数（Empirical Orthogonal Function，EFL）的插值和模拟数据同化）。
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).
DOI：
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发表时间：
2018-02
期刊：
ArXiv
影响因子：
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作者：
A. G. Matthews;Mark Rowland;Jiri Hron;Richard E. Turner;Zoubin Ghahramani
通讯作者：
A. G. Matthews;Mark Rowland;Jiri Hron;Richard E. Turner;Zoubin Ghahramani