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Combining data assimilation and machine learning to infer unresolved scale parametrization
Submitted by webadmin on 17. February 2021 - 17:18
250711
270733
Brajard, J.
,
Carrassi, A.
,
Bocquet, M.
&
Bertino, L.
Combining data assimilation and machine learning to infer unresolved scale parametrization
.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
379
, (2021).
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Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
Submitted by webadmin on 8. December 2020 - 16:07
250711
nn2993k
ns2993k
Cheng, S.
,
Aydoğdu, A.
,
Rampal, P.
,
Carrassi, A.
&
Bertino, L.
Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
.
Oceans
(2020).doi:10.3390/oceans1040022
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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 - 15:02
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 - 18:10
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 - 15:48
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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Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean
Submitted by webadmin on 6. March 2019 - 11:43
250711
56801
Moore, A.M.
, et al.
Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean
.
Frontiers in Marine Science
6
, (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 - 21:04
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 - 14:11
250711
Pulido, M.
,
Tandeo, P.
,
Bocquet, M.
,
Carrassi, A.
&
Lucini, M.
Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
.
Tellus 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 - 12:42
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 - 16:10
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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