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

Said Ouala, Ronan Fablet, Cédric Herzet, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier
2018 Remote Sensing  
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  ...  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  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10121864 fatcat:ytgmlxee5fejlerlhs3vb52iui

Sea surface temperature prediction and reconstruction using patch-level neural network representations [article]

Said Ouala, Cedric Herzet, Ronan Fablet
2018 arXiv   pre-print
assimilation of geophysical fields from satellite-derived remote sensing data.  ...  The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.  ...  In this work, we investigate neural network representations for dynamical systems. Neural networks are currently the state-of-the-art techniques for a wide range of machine learning issues.  ... 
arXiv:1806.00144v1 fatcat:hc4iumya5fcdvfp2fcnhsjcshe

Could Machine Learning Break the Convection Parameterization Deadlock?

P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi, G. Yacalis
2018 Geophysical Research Letters  
The machine learning representation of convection, which we call the Cloud Brain (CBRAIN), can skillfully predict many of the convective heating, moistening, and radiative features of superparameterization  ...  in climate models that are built "top-down," that is, by learning salient features of convection from unusually explicit simulations.  ...  In particular, we use a version of stochastic gradient descent called Adam (Kingma & Ba, 2014) . How much to step down the gradient is determined by the learning rate.  ... 
doi:10.1029/2018gl078202 fatcat:rvfln4xtqvfc7bloyv5bu5vkru

Table of contents

2019 IEEE Transactions on Image Processing  
Zhang 965 Learning Effective RGB-D Representations for Scene Recognition ..... X. Song, S. Jiang, L. Herranz, and C.  ...  Kautz 723 Radar Imaging, Remote Sensing, and Geophysical Imaging Hyperspectral Imagery Classification via Stochastic HHSVMs ................................................................ ............  ... 
doi:10.1109/tip.2018.2878280 fatcat:cwalaxbmvfd3xmp5wdn2kwsk3e

A Potential Disintegration of the West Antarctic Ice Sheet: Implications for Economic Analyses of Climate Policy

Delavane Diaz, Klaus Keller
2016 The American Economic Review  
Finally, the representation of endogenous learning is highly stylized and focuses on a subset of the relevant uncertainties.  ...  These studies broke important ground, but can still be considerably improved in aspects such as the representation of geophysical dynamics, diverse expert assessments, and the resulting impacts on the  ... 
doi:10.1257/aer.p20161103 fatcat:sq2tgyu3xnhitp3og5yefcsoj4

Page 2531 of Mathematical Reviews Vol. , Issue 95d [page]

1995 Mathematical Reviews  
Formal program development ........................ * 68095 Foundations of knowledge representation and reasoning skewers aude biaaiaasaeemabnenss tanewaamesadxeweose een * 68142 Foundations of software  ...  83002 Static Analysis, 3rd International, WSA °93 .... * 68005 Theoretical Foundations of Knowledge Representation Oted TROMSG oo 5.ssccss vscccecseccscavecceese * 68142  ... 

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence [article]

Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji, Pedram Hassanzadeh
2022 arXiv   pre-print
Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes  ...  re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation.  ...  Introduction A surge of interest in building deep learning-based data-driven models for complex systems such as chaotic dynamical systems [1, 2] , fully turbulent flow [3] , and weather and climate models  ... 
arXiv:2205.04601v1 fatcat:6ltfrr4uovavbi3hk6iz3xxcze

Learning Variational Data Assimilation Models and Solvers [article]

Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau
2020 arXiv   pre-print
This paper addresses variational data assimilation from a learning point of view.  ...  A key feature of the proposed end-to-end learning architecture is that we may train the NN models using both supervised and unsupervised strategies.  ...  Representation learning for geophysical dynamics: As stated in the introduction, the data-driven identification of representations of geophysical dynamics is very active research area.  ... 
arXiv:2007.12941v1 fatcat:ibviym7ojzaxtgvflmzg6a3mvm

Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system

Shlomo Orr, Alexander M. Meystel
2005 Hydrogeology Journal  
Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world  ...  A paradigm shift is introduced: an adaptation of new methods of intelligent control that will relax the dependency on rigid, computer-intensive, stochastic PDE, and will shift the emphasis to a goal-oriented  ...  Dong Zhang (U. of Oklahoma), Dr. S.P. Neumann (U. of Arozina), and Dr. Larry Lake (U. of Texas).  ... 
doi:10.1007/s10040-004-0424-3 fatcat:h4bzzrojfjehncrpxnihapyjka

Book Review

Andrzej Icha
2013 Pure and Applied Geophysics  
The authors also include historical notes throughout the text giving brief summaries of the history of probability theory and the main researchers in analysis, set theory, probability theory, and dynamic  ...  Presentation of the basic facts is complemented by practical interpretation of probability as a frequency of occurrence.  ...  Mathematical modelling of real-world processes leads, in general, to nonlinear deterministic and stochastic dynamic systems.  ... 
doi:10.1007/s00024-013-0739-x fatcat:mkob5dhrsfbjlkebdjxsmgo3ny

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder [article]

Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt
2019 arXiv   pre-print
Our results suggest that specific dynamic structures of auroras are highly correlated with GNSS phase scintillations.  ...  In this paper we use a multi-scale residual autoencoder (Res-AE) to show the correlation between specific dynamic structures of the aurora and the magnitude of the GNSS phase scintillations (σ_ϕ).  ...  Aurorae are highly dynamic phenomena that have long been thought to correlate with amplitude and phase scintillations due to similar geophysical drivers causing these phenomena.  ... 
arXiv:1910.03085v1 fatcat:rujp7ulrbrfblns42hvoxpufy4

Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System [chapter]

Pierre Tandeo, Pierre Ailliot, Juan Ruiz, Alexis Hannart, Bertrand Chapron, Anne Cuzol, Valérie Monbet, Robert Easton, Ronan Fablet
2015 Machine Learning and Data Mining Approaches to Climate Science  
Combining machine learning and stochastic filtering methods Data assimilation for dynamical systems is generally stated according to the following state space model (see e.g.  ...  From a methodological point of view, analog techniques provide nonparametric representations.  ... 
doi:10.1007/978-3-319-17220-0_1 fatcat:7dza7ahj25d2dexxdovo7fb2ri

Learning Variational Data Assimilation Models and Solvers

R. Fablet, B. Chapron, L. Drumetz, E. Mmin, O. Pannekoucke, F. Rousseau
2021 Journal of Advances in Modeling Earth Systems  
In earth science, the reconstruction of the dynamics of a given state or process from a sequence of partial and noisy observations is referred to as a data assimilation issue.  ...  By contrast, statistical data assimilation schemes generally relies on iterative formulations of stochastic filtering techniques such as Kalman and particle filters.  ...  Representation Learning for Data Assimilation As stated in the introduction, the data-driven identification of representations of geophysical dynamics is a very active research area.  ... 
doi:10.1029/2021ms002572 fatcat:kt3jrxqugnhvfioqurngf7vvly

Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods [article]

Boumediene Hamzi, Romit Maulik, Houman Owhadi
2021 arXiv   pre-print
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems.  ...  efficient geophysical forecasting for a large diversity of processes.  ...  Department of Energy  ... 
arXiv:2103.10935v2 fatcat:qyyzgudvvfdzhb6ki2w7zhplde

Page 4399 of Mathematical Reviews Vol. , Issue 88h [page]

1988 Mathematical Reviews  
NY-X) *Topics in geophysical fluid dynamics: atmospheric dynamics, dynamo theory, and climate dynamics.  ...  ISBN 0-387-96475-4 This book treats four separate but related topics in geophysical fluid dynamics, although the last topic (climate dynamics) is not really fundamentally fluid dynamical in nature.  ... 
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