Results 21 to 30 of about 21,404,104 (365)

Impacts of Aeolus horizontal Line‐Of‐Sight (HLOS) wind assimilation on the Korean integrated model (KIM) forecast system

open access: yesAtmospheric 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]

open access: yesQuarterly 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

open access: yesEarth 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]

open access: yesJournal 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]

open access: yesWater 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

open access: yesFrontiers 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]

open access: yesTellus: 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

Assimilating monthly precipitation data in a paleoclimate data assimilation framework [PDF]

open access: yesClimate 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

Combining FY-3D MWTS-2 with AMSU-A Data for Inter-Decadal Diurnal Correction and Climate Trends of Atmospheric Temperature

open access: yesRemote 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

Data Assimilation Networks

open access: yesJournal 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

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