Results 11 to 20 of about 605,555 (344)
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
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 observations with a dynamic model in an optimal way.
Rossella Arcucci +3 more
openaire +3 more sources
The Efficiency of Data Assimilation [PDF]
Water Resources Research, 2018 AbstractData assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real‐world application of Bayes' theorem is approximate, and therefore, we cannot expect that data assimilation will preserve all of the information available from models and observations.
Jicheng Liu +7 more
openaire +3 more sources
Scale-dependent background-error covariance localisation [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2015 A new approach is presented and evaluated for efficiently applying scale-dependent spatial localisation to ensemble background-error covariances within an ensemble-variational data assimilation system.
Mark Buehner, Anna Shlyaeva
doaj +1 more source
Remote Sensing, 2021 Microwave temperature sounding observations from polar-orbiting meteorological satellites have been widely used for research on climate trends of atmospheric temperature at different heights around the world.
Xinlu Xia, Xiaolei Zou
doaj +1 more source
A High Resolution Reanalysis for the Mediterranean Sea
Frontiers in Earth Science, 2021 In order to be able to forecast the weather and estimate future climate changes in the ocean, it is crucial to understand the past and the mechanisms responsible for the ocean variability.
Romain Escudier +13 more
doaj +1 more source
Assimilating monthly precipitation data in a paleoclimate data assimilation framework [PDF]
Climate of the Past, 2019 Abstract. Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information from proxy records and documentary data to instrumental measurements were assimilated in previous studies to reconstruct past climate fields. However,
Yuri Brugnara +7 more
openaire +4 more sources