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Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation
2021
IEEE Access
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A data-driven data assimilation method is proposed by combining fully connected neural network with ensemble Kalman filter to emulate dynamic models from sparse and noisy observations. ...
INDEX TERMS Data assimilation, fully connected neural network, machine learning, ensemble Kalman filter, Lorenz model. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. ...
doi:10.1109/access.2021.3120482
fatcat:vlpswbn6englpngdfvz6bj5zk4
Sea Surface Dynamics Reconstruction Using Neural Networks Based Kalman Filter
2019
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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In this work, we propose an alternative to the Ensemble Kalman filter through the implementation of a neural networks filtering scheme based on a parametric stochastic model. ...
From our numerical experiment, we prove the relevance of the proposed architecture in the reconstruction of geophysical fields with respect to the state-of-the-art schemes. ...
More specifically, we propose to replace the ensemble forecasting formulation in the ensemble Kalman filter by a stochastic forecasting model parametrized by a neural network. ...
doi:10.1109/igarss.2019.8898086
dblp:conf/igarss/OualaFHDCPCG19
fatcat:kbq6cwxcgfa3pa5vbl4nzpxngm
Data Assimilation by Neural Network for Ocean Circulation: Parallel Implementation
2022
Supercomputing Frontiers and Innovations
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The Kalman filter (KF) is a technique for data assimilation, but it is computationally expensive. An approach to reduce the computational effort for DA is to emulate the KF by a neural network. ...
The multi-layer perceptron neural network (MLP-NN) is employed to emulate the Kalman in a 2D ocean circulation model, and algorithmic complexity to KF and NN is presented. ...
Data Assimilation by Neural Network for Ocean Circulation: Parallel Implementation ...
doi:10.14529/jsfi220105
fatcat:b4it3tya6bh2hgqtmi2qdib7pa
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
[article]
2020
arXiv
pre-print
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Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows, but often require a set of ensemble forward simulations to estimate forecast covariance. ...
In this paper, we put forth a fully nonintrusive recurrent neural network approach based on a long short-term memory (LSTM) embedding architecture to estimate the nudging term, which plays a role not only ...
This report was prepared as an account of work sponsored by an agency of the United States Government. ...
arXiv:2005.11296v1
fatcat:fnzodjd3xfg7ni7ppgaj5coizm
Learning to Assimilate in Chaotic Dynamical Systems
[article]
2021
arXiv
pre-print
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We introduce amortized assimilation, a framework for learning to assimilate in dynamical systems from sequences of noisy observations with no need for ground truth data. ...
Data assimilation methods are used to infer these initial conditions by systematically combining noisy, incomplete observations and numerical models of system dynamics to produce effective estimation schemes ...
approach attempts to combine the flexibility of variational methods with the efficiency of sequential filters by training a neural network to act as an analysis update mechanism. ...
arXiv:2111.01058v1
fatcat:kx77t6l7xjb5teynf2iqrrzssq
Machine Learning for Earth System Observation and Prediction
2020
Bulletin of The American Meteorological Society - (BAMS)
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We would like to express our appreciation to ECMWF Events Manager Karen Clarke for her impeccable organization of the logistics of the virtual Workshop and its successful delivery. ...
itself by, e.g., devising data-driven tangent linear and adjoint models, or to "learn" the Kalman gain in an ensemble Kalman filter framework. ...
Here, in a method akin to transfer learning, an existing neural network has been retrained with the observations and the ECMWF model soil moisture, reducing the possibility of assimilating biased observations ...
doi:10.1175/bams-d-20-0307.1
fatcat:ddotbxuisnflzc3absqcrtlhq4
DAN – An optimal Data Assimilation framework based on machine learning Recurrent Networks
[article]
2020
arXiv
pre-print
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Data assimilation algorithms aim at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations thereof. ...
We propose a fully data driven deep learning architecture generalizing recurrent Elman networks and data assimilation algorithms which provably reaches the same prediction goals as the latter. ...
As well [7] uses an artificial neural network to emulate a localized ensemble transform Kalman filter (LETKF). Finally, DA algorithms can be seen as optimizers to train recurrent networks. ...
arXiv:2010.09694v1
fatcat:2ip2apa5fndp3jj4pdka7p4tji
Data Assimilation in the Latent Space of a Neural Network
[article]
2020
arXiv
pre-print
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We use a Convolutional neural network to reduce the dimensionality of the problem, a Long-Short-Term-Memory to build a surrogate model of the dynamic system and an Optimal Interpolated Kalman Filter to ...
In this paper, we formulate a new methodology called Latent Assimilation that combines Data Assimilation and Machine Learning. ...
An approach for employing artificial neural networks (NNs) to emulate the Local Ensemble Transform Kalman Filter (LETKF) as a method of data assimilation is presented in [17] . ...
arXiv:2012.12056v1
fatcat:amta5kceqrgnferlfja4cswqoy
Online learning of both state and dynamics using ensemble Kalman filters
[article]
2020
arXiv
pre-print
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To deal with partial and noisy observations in that endeavor, machine learning representations of the surrogate model can be used within a Bayesian data assimilation framework. ...
The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. ...
CEREA is a member of Institute Pierre-Simon Laplace (IPSL). ...
arXiv:2006.03859v1
fatcat:zplaz2lokjgitbbj3ogv2ylb4i
Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation
[article]
2021
arXiv
pre-print
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Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. ...
To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical ...
Results are shown assimilating both fully observed and sparsely observed dynamics, with a range of observational noise levels, using an RNN-based ensemble transform Kalman filter (ETKF) [Bishop et al. ...
arXiv:2109.12269v1
fatcat:vueqt5ufkvckdfzoi52iqtkhq4
Current challenges and future directions in data assimilation and reanalysis
2022
Bulletin of The American Meteorological Society - (BAMS)
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CAPSULE: Joint WCRP-WWRP Symposium on Data Assimilation and Reanalysis What: Scientists from the three research areas data assimilation, reanalyses and observing systems came together to discuss current ...
progress and future challenges and to highlight the synergies between the communities. ...
Service (DWD), the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP), and especially the European Centre for Medium-Range Weather Forecasts (ECMWF) for their support in ...
doi:10.1175/bams-d-21-0331.1
fatcat:6gfc4lv2kfgajocbrg3z72fpgi
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
[article]
2022
arXiv
pre-print
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Experiments using the Lorenz'96 model show that the ANNs are able to emulate some of the properties of ensemble forecasts like the filtering of the most unpredictable modes and a state-dependent quantification ...
The performance of the networks is examined at different lead times and in scenarios with and without model errors. ...
UBACyT-20020170100504) And also a special thanks to the National Meteorological Service of Argentina for their support and trust in this work. ...
arXiv:2111.14844v4
fatcat:uxuy5u2iu5cpzkusaxnwwp5ckm
Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences*
2020
Bulletin of The American Meteorological Society - (BAMS)
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Capsule Summary Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery ...
The authors are members of the Scientific and/or Local Organizing Committees of the NOAA workshop on "Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction." ...
Cintra and de Campos Velho (2018) demonstrated an emulation of the entire DA analysis by implementing a multilayer perceptron (MLP) model of the local ensemble transform Kalman filter (LETKF; Hunt et ...
doi:10.1175/bams-d-20-0031.1
fatcat:2wcohkjb2bb4nhmfteu2brydme
Online learning of both state and dynamics using ensemble Kalman filters
2019
Foundations of Data Science
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To deal with partial and noisy observations in that endeavor, machine learning representations of the surrogate model can be used within a Bayesian data assimilation framework. ...
The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. ...
CEREA is a member of Institute Pierre-Simon Laplace (IPSL). ...
doi:10.3934/fods.2020015
fatcat:hhnu6nt32fbznfqqwblojhveia
On closure parameter estimation in chaotic systems
2012
Nonlinear Processes in Geophysics
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Traditionally, parameters of dynamical systems are estimated by directly comparing the model simulations to observed data using, for instance, a least squares approach. ...
</strong> Many dynamical models, such as numerical weather prediction and climate models, contain so called closure parameters. ...
Martin Leutbecher from ECMWF is gratefully acknowledged for his support and help in designing the numerical experiments carried out with the stochastic Lorenz-95 system. ...
doi:10.5194/npg-19-127-2012
fatcat:l7rsw6vuenbkrhocbb2tqldrnu
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