Results 11 to 20 of about 21,726,964 (386)
Deep Data Assimilation: Integrating Deep Learning with Data Assimilation [PDF]
Applied Sciences, 2021 In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed ...
Rossella Arcucci +3 more
semanticscholar +4 more sources
The analog data assimilation [PDF]
Monthly Weather Review, 2017 In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA).
Ailliot, Pierre +4 more
core +6 more sources
Displacement Data Assimilation [PDF]
Journal of Computational Physics, 2016 We show that modifying a Bayesian data assimilation scheme by incorporating kinematically-consistent displacement corrections produces a scheme that is demonstrably better at estimating partially observed state vectors in a setting where feature ...
Mariano, Arthur J. +3 more
core +3 more sources
Evaluating Data Assimilation Algorithms [PDF]
Monthly Weather Review, 2012 Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role.
A. M. Stuart +68 more
core +8 more sources
Data assimilation in operator algebras. [PDF]
Proc Natl Acad Sci U S A, 2023 We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states).
Freeman D +4 more
europepmc +4 more sources
Southern Europe and western Asian marine heatwaves (SEWA-MHWs): a dataset based on macroevents [PDF]
Earth System Science Data, 2023 Marine heatwaves (MHWs) induce significant impacts on marine ecosystems. There is a growing need for knowledge about extreme climate events to better inform decision-makers on future climate-related risks.
G. Bonino +4 more
doaj +1 more source
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization [PDF]
Foundations of Data Science, 2020 The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii ...
M. Bocquet +3 more
semanticscholar +1 more source
Combining data assimilation and machine learning to infer unresolved scale parametrization [PDF]
Philosophical Transactions of the Royal Society A, 2020 In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models.
J. Brajard +3 more
semanticscholar +1 more source
Learning earth system models from observations: machine learning or data assimilation?
Philosophical Transactions of the Royal Society A, 2021 Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth system models directly from the observations. Earth sciences already use data assimilation (DA), which underpins decades of progress in weather forecasting.
A. Geer, Ecmwf, Shinfield Park
semanticscholar +1 more source
Using machine learning to correct model error in data assimilation and forecast applications [PDF]
Quarterly Journal of the Royal Meteorological Society, 2020 The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences, in which the key output is a surrogate model meant to emulate the dynamical model.
A. Farchi +3 more
semanticscholar +1 more source