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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.
Grey Nearing +5 more
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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
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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
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Data Assimilation in Reduced Modeling [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2017 We consider the problem of optimal recovery of an element $u$ of a Hilbert space $\mathcal{H}$ from $m$ measurements obtained through known linear functionals on $\mathcal{H}$. Problems of this type are well studied \cite{MRW} under an assumption that $u$ belongs to a prescribed model class, e.g. a known compact subset of $\mathcal{H}$.
Binev, Peter +5 more
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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
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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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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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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
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Mitigation of Significant Data Noise in F17 SSMIS Observations since October 2017
Remote Sensing, 2022 Special Sensor Microwave Imager Sounder (SSMIS) temperature sounding observations have been made available since the launch of the Defense Meteorological Satellite Program (DMSP), F16, on 18 October 2003. These conical-scanning observations of brightness
Huijie Dong, Xiaolei Zou
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, 2016 Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution.
Asch, Mark +2 more
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