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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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A consistent interpretation of the stochastic version of the Ensemble Kalman Filter

Quarterly journal of the Royal Meteorological Society, 2020-07, Vol.146 (731), p.2815-2825

Leeuwen, Peter Jan

2020

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Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem

1st Edition 2022, 2022

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Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP

Journal of the Meteorological Society of Japan. Ser. II, 2023, Vol.101(4), pp.233-253

2023

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Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

Tellus. Series A, Dynamic meteorology and oceanography, 2014-01, Vol.66 (1), p.23493-18

2014

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Observation impact in data assimilation: the effect of non-Gaussian observation error

Tellus. Series A, Dynamic meteorology and oceanography, 2013-01, Vol.65 (1), p.20035-16

2013

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A weak-constraint 4DEnsembleVar. Part II: experiments with larger models

Tellus. Series A, Dynamic meteorology and oceanography, 2017-01, Vol.69 (1), p.1271565

2017

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Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity

Tellus. Series A, Dynamic meteorology and oceanography, 2015-01, Vol.67 (1), p.26928-13

2015

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Online state and time‐varying parameter estimation using the implicit equal‐weights particle filter

Quarterly journal of the Royal Meteorological Society, 2024-04, Vol.150 (761), p.2087-2103

2024

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When can we expect extremely high surface temperatures?

Geophysical research letters, 2008-07, Vol.35 (14), p.L14703-n/a

2008

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Why Do the Maximum Intensities in Modeled Tropical Cyclones Vary Under the Same Environmental Conditions?

Geophysical research letters, 2020-02, Vol.47 (3), p.n/a

2020

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Nonlinear Data Assimilation

1st ed. 2015 edition., 2015

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A systematic method of parameterisation estimation using data assimilation

Tellus. Series A, Dynamic meteorology and oceanography, 2016-12, Vol.68 (1), p.29012-10

2016

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A particle flow filter for high‐dimensional system applications

Quarterly journal of the Royal Meteorological Society, 2021-04, Vol.147 (737), p.2352-2374

2021

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A non‐parametric way to estimate observation errors based on ensemble innovations

Quarterly journal of the Royal Meteorological Society, 2024-04, Vol.150 (761), p.2296-2315

2024

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A framework for causal discovery in non-intervenable systems

Chaos (Woodbury, N.Y.), 2021-12, Vol.31 (12), p.123128

2021

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Effects of mis‐specified time‐correlated model error in the (ensemble) Kalman Smoother

Quarterly journal of the Royal Meteorological Society, 2021-01, Vol.147 (734), p.573-588

2021

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Representation errors and retrievals in linear and nonlinear data assimilation

Quarterly journal of the Royal Meteorological Society, 2015-07, Vol.141 (690), p.1612-1623

van Leeuwen, Peter Jan

2015

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On time‐parallel preconditioning for the state formulation of incremental weak constraint 4D‐Var

Quarterly journal of the Royal Meteorological Society, 2021-10, Vol.147 (740), p.3521-3529

2021

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A Tempered Particle Filter to Enhance the Assimilation of SAR‐Derived Flood Extent Maps Into Flood Forecasting Models

Water resources research, 2022-08, Vol.58 (8), p.e2022WR031940-n/a

2022

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Data Assimilation in the Solar Wind: Challenges and First Results

Space Weather, 2017-11, Vol.15 (11), p.1490-1510

2017

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Randomised preconditioning for the forcing formulation of weak‐constraint 4D‐Var

Quarterly journal of the Royal Meteorological Society, 2021-10, Vol.147 (740), p.3719-3734

2021

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Implicit equal‐weights particle filter

Quarterly journal of the Royal Meteorological Society, 2016-07, Vol.142 (698), p.1904-1919

2016

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Measures of observation impact in non-Gaussian data assimilation

Tellus. Series A, Dynamic meteorology and oceanography, 2012-12, Vol.64 (1), p.17192-16

2012

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Using the (Iterative) Ensemble Kalman Smoother to Estimate the Time Correlation in Model Error

Tellus. Series A, Dynamic meteorology and oceanography, 2023-02, Vol.75 (1), p.108-128

2023

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Particle filters for high‐dimensional geoscience applications: A review

A consistent interpretation of the stochastic version of the Ensemble Kalman Filter

Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem

Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

Observation impact in data assimilation: the effect of non-Gaussian observation error

A weak-constraint 4DEnsembleVar. Part II: experiments with larger models

Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity

Online state and time‐varying parameter estimation using the implicit equal‐weights particle filter

When can we expect extremely high surface temperatures?

Why Do the Maximum Intensities in Modeled Tropical Cyclones Vary Under the Same Environmental Conditions?

Nonlinear Data Assimilation

A systematic method of parameterisation estimation using data assimilation

A particle flow filter for high‐dimensional system applications

A non‐parametric way to estimate observation errors based on ensemble innovations

A framework for causal discovery in non-intervenable systems

Effects of mis‐specified time‐correlated model error in the (ensemble) Kalman Smoother

Representation errors and retrievals in linear and nonlinear data assimilation

On time‐parallel preconditioning for the state formulation of incremental weak constraint 4D‐Var

A Tempered Particle Filter to Enhance the Assimilation of SAR‐Derived Flood Extent Maps Into Flood Forecasting Models

Data Assimilation in the Solar Wind: Challenges and First Results

Randomised preconditioning for the forcing formulation of weak‐constraint 4D‐Var

Implicit equal‐weights particle filter

Measures of observation impact in non-Gaussian data assimilation

Using the (Iterative) Ensemble Kalman Smoother to Estimate the Time Correlation in Model Error

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