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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

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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).

Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods

Submitted by webadmin on 27. September 2018 - 13:11

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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).

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

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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).

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

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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).

Data assimilation in the geosciences - An overview of methods, issues and perspectives

Submitted by Alberto Carrassi on 19. June 2018 - 12:15

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

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

Stochastic parametrization identification using ensemble Kalman filtering combined with expectation-minimization and Newton-Raphson maximum likelihood methods.

Scientific challenges of convective-scale numerical weather prediction

Submitted by webadmin on 24. January 2018 - 14:29

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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

Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution

Submitted by webadmin on 4. April 2017 - 16:10

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Gurumoorthry, K., Grudzien, C., Apte, A., Carrassi, A. & Jones, C.K.R.T. Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution. SIAM Journal of Control and Optimization 55, (2017).

Degenerate Kalman filter error covariances and their convergence onto the unstable subspace

Submitted by webadmin on 4. April 2017 - 16:07

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Bocquet, M., et al. Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 5, (2017).

Four-dimensional ensemble variational data assimilation and the unstable subspace

Submitted by webadmin on 4. April 2017 - 14:41

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Bocquet, M. & Carrassi, A. Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus. Series A, Dynamic meteorology and oceanography 69, (2017).

Nansen Environmental and Remote Sensing Center (Bergen, Norway)	Nansen International Environmental and Remote Sensing Center (St. Petersburg, Russia)	Nansen-Bangladesh International Centre for Coastal, Ocean and Climate Studies (Dhaka, Bangladesh)
Nansen Environmental Research Center – India (Cochin, India)	Nansen–Zhu International Research Centre (Beijing, China)	Nansen-Tutu Centre for Marine Environmental Research (Cape Town, South Africa)