Results 11 to 20 of about 21,404,104 (365)
The analog data assimilation [PDF]
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
Deep Data Assimilation: Integrating Deep Learning with Data Assimilation [PDF]
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
Displacement Data Assimilation [PDF]
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]
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
Variational assimilation of Lagrangian data in oceanography [PDF]
We consider the assimilation of Lagrangian data into a primitive equations circulation model of the ocean at basin scale. The Lagrangian data are positions of floats drifting at fixed depth.
Assenbaum M Reverdin G+18 more
core +13 more sources
Data assimilation: The Schrödinger perspective [PDF]
Data assimilation addresses the general problem of how to combine model-based predictions with partial and noisy observations of the process in an optimal manner. This survey focuses on sequential data assimilation techniques using probabilistic particle-
S. Reich
semanticscholar +5 more sources
Data assimilation in operator algebras. [PDF]
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
Combining data assimilation and machine learning to infer unresolved scale parametrization [PDF]
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
Southern Europe and western Asian marine heatwaves (SEWA-MHWs): a dataset based on macroevents [PDF]
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
Learning earth system models from observations: machine learning or data assimilation?
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