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On Serial Observation Processing in Localized Ensemble Kalman Filters

Monthly weather review, 2015-05, Vol.143 (5), p.1554-1567

Nerger, Lars

2015

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Particle filters for high‐dimensional geoscience applications: A review

Quarterly journal of the Royal Meteorological Society, 2019-07, Vol.145 (723), p.2335-2365

2019

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The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Remote sensing (Basel, Switzerland), 2024-01, Vol.16 (2), p.430

2024

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Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation

PloS one, 2024-04, Vol.19 (4), p.e0300138-e0300138

2024

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On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods

Monthly weather review, 2014-06, Vol.142 (6), p.2165-2175

2014

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Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously

Quarterly journal of the Royal Meteorological Society, 2018-01, Vol.144 (711), p.529-538

2018

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State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

Tellus. Series A, Dynamic meteorology and oceanography, 2018-01, Vol.70 (1), p.1-43

2018

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Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter

Quarterly journal of the Royal Meteorological Society, 2022-01, Vol.148 (743), p.620-640

Nerger, Lars

2022

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DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

Journal of glaciology, 2021-12, Vol.67 (266), p.1235-1240

2021

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A Unification of Ensemble Square Root Kalman Filters

Monthly weather review, 2012-07, Vol.140 (7), p.2335-2345

2012

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Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

Monthly weather review, 2016-01, Vol.144 (1), p.409-427

2016

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A regulated localization scheme for ensemble-based Kalman filters

Quarterly journal of the Royal Meteorological Society, 2012-04, Vol.138 (664), p.802-812

2012

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On Domain Localization in Ensemble-Based Kalman Filter Algorithms

Monthly weather review, 2011-07, Vol.139 (7), p.2046-2060

2011

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Towards reliable Arctic sea ice prediction using multivariate data assimilation

Science bulletin, 2019-01, Vol.64 (1), p.63-72

2019

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Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Frontiers in Marine Science, 2022-09, Vol.9

2022

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Software for ensemble-based data assimilation systems—Implementation strategies and scalability

Computers & geosciences, 2013-06, Vol.55, p.110-118

2013

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The smoother extension of the nonlinear ensemble transform filter

Tellus. Series A, Dynamic meteorology and oceanography, 2017-01, Vol.69 (1), p.1327766

2017

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Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations

Ocean-land-atmosphere research, 2023-01, Vol.2

2023

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Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model

Journal of advances in modeling earth systems, 2020-04, Vol.12 (4), p.n/a

2020

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The Impacts of Optimizing Model‐Dependent Parameters on the Antarctic Sea Ice Data Assimilation

Geophysical research letters, 2023-11, Vol.50 (22)

2023

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Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter

Ocean modelling (Oxford), 2015-12, Vol.96, p.254-264

2015

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An ensemble Kalman filter for the time-dependent analysis of the geomagnetic field

Geochemistry, geophysics, geosystems : G3, 2013-10, Vol.14 (10), p.4035-4043

2013

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Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

Geoscientific Model Development, 2020-09, Vol.13 (9), p.4305-4321

2020

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Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Quarterly journal of the Royal Meteorological Society, 2020-10, Vol.146 (733), p.4014-4029

2020

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On the influence of model nonlinearity and localization on ensemble Kalman smoothing

Quarterly journal of the Royal Meteorological Society, 2014-10, Vol.140 (684), p.2249-2259

2014

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On Serial Observation Processing in Localized Ensemble Kalman Filters

Particle filters for high‐dimensional geoscience applications: A review

The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case

Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation

On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods

Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously

State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter

DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

A Unification of Ensemble Square Root Kalman Filters

Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

A regulated localization scheme for ensemble-based Kalman filters

On Domain Localization in Ensemble-Based Kalman Filter Algorithms

Towards reliable Arctic sea ice prediction using multivariate data assimilation

Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Software for ensemble-based data assimilation systems—Implementation strategies and scalability

The smoother extension of the nonlinear ensemble transform filter

Improving Arctic Sea-Ice Thickness Estimates with the Assimilation of CryoSat-2 Summer Observations

Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model

The Impacts of Optimizing Model‐Dependent Parameters on the Antarctic Sea Ice Data Assimilation

Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter

An ensemble Kalman filter for the time-dependent analysis of the geomagnetic field

Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

On the influence of model nonlinearity and localization on ensemble Kalman smoothing

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