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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model
Submitted by webadmin on 30. June 2020 - 14:02
250711
BFS2018TMT0
Brajard, J.
,
Carrassi, A.
,
Bocquet, M.
&
Bertino, L.
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model
.
Journal of Computational Science
44
, (2020).
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Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
Submitted by webadmin on 27. March 2020 - 17:10
250711
nn2993k
Bocquet, M.
,
Brajard, J.
,
Carrassi, A.
&
Bertino, L.
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
.
Foundations of Data Science (FoDS)
2
, (2020).
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Improving weather and climate predictions by training of supermodels
Submitted by Alberto Carrassi on 22. October 2019 - 14:48
250711
Data Assimilation
Data Assimilation
Schevenhoven, F.
,
Selten, F.
,
Carrassi, A.
&
Keenlyside, N.
Improving weather and climate predictions by training of supermodels
.
Earth System Dynamics
(2019).
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Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error
Submitted by webadmin on 18. October 2018 - 20:04
250711
Grudzien, C.
,
Carrassi, A.
&
Bocquet, M.
Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error
.
SIAM/ASA Journal on Uncertainty Quantification (JUQ)
6
, (2018).
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Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
Submitted by webadmin on 27. September 2018 - 13:11
250711
Pulido, M.
,
Tandeo, P.
,
Bocquet, M.
,
Carrassi, A.
&
Lucini, M.
Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
.
Tellus. Series A, Dynamic meteorology and oceanography
70
, (2018).
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Impact of rheology on probabilistic forecasts of sea ice trajectories: Application for search and rescue operations in the Arctic
Submitted by webadmin on 27. September 2018 - 11:42
250711
Rabatel, M.
,
Rampal, P.
,
Carrassi, A.
,
Bertino, L.
&
Jones, C.K.R.T.
Impact of rheology on probabilistic forecasts of sea ice trajectories: Application for search and rescue operations in the Arctic
.
The Cryosphere
12
, (2018).
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Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
Submitted by webadmin on 26. September 2018 - 15:10
250711
Grudzien, C.
,
Carrassi, A.
&
Bocquet, M.
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
.
Nonlinear processes in geophysics
25
, (2018).
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Data assimilation in the geosciences - An overview of methods, issues and perspectives
Submitted by Alberto Carrassi on 19. June 2018 - 12:15
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Data Assimilation
Mohn-Sverdrup Center for Global Ocean Studies and Operational Oceanography
Data Assimilation
Carrassi, A.
,
Bocquet, M.
,
Bertino, L.
&
Evensen, G.
Data assimilation in the geosciences - An overview of methods, issues and perspectives
.
WIREs Climate Change
(2018).doi:doi: 10.1002/wcc.535
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Stochastic parametrization identification using ensemble Kalman filtering combined with expectation-minimization and Newton-Raphson maximum likelihood methods
Submitted by Alberto Carrassi on 14. February 2018 - 16:41
250711
Data Assimilation
Mohn-Sverdrup Center for Global Ocean Studies and Operational Oceanography
Data Assimilation
Stochastic parametrization identification using ensemble Kalman filtering combined with expectation-minimization and Newton-Raphson maximum likelihood methods
.
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Scientific challenges of convective-scale numerical weather prediction
Submitted by webadmin on 24. January 2018 - 14:29
250711
Yano, J.-I.
, et al.
Scientific challenges of convective-scale numerical weather prediction
.
Bulletin of The American Meteorological Society - (BAMS)
(2018).doi:10.1175/BAMS-D-17-0125.1
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