Results 21 to 30 of about 21,404,104 (365)
Atmospheric Science Letters, 2023 The Korean Integrated Model (KIM) forecast system, based on a hybrid four‐dimensional ensemble‐variational method, was extended to assimilate Horizontal Line‐Of‐Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on ...
Sihye Lee +3 more
doaj +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
Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020
Earth System Science Data, 2021 . BRAN2020 is an ocean reanalysis that combines ocean observations with an eddy-resolving, near-global ocean general circulation model, to produce four-dimensional estimates of the ocean state.
M. Chamberlain +5 more
semanticscholar +1 more source
Learning Variational Data Assimilation Models and Solvers [PDF]
Journal of Advances in Modeling Earth Systems, 2020 Data assimilation is a key component of operational systems and scientific studies for the understanding, modeling, forecasting and reconstruction of earth systems informed by observation data.
R. Fablet +5 more
semanticscholar +1 more source
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
On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models
Frontiers in Applied Mathematics and Statistics, 2022 Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction (NWP) in non-Gaussian setting. In this manuscript, two recent versions of particle filters, namely the Localized Adaptive Particle Filter (LAPF) and
Nora Schenk +5 more
doaj +1 more source
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
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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
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
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Journal of Advances in Modeling Earth Systems, 2022 AbstractData Assimilation aims at estimating the posterior conditional probability density functions based on error statistics of the noisy observations and the dynamical system. State of the art methods are sub‐optimal due to the common use of Gaussian error statistics and the linearization of the non‐linear dynamics.
Boudier, Pierre +4 more
openaire +3 more sources