Biblio

Found 43 results
2019
G. Evensen, “Accounting for model errors in iterative ensemble smoothers”, Computational Geosciences, 2019.
P. N. Raanes, Bocquet, M., and Carrassi, A., “Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures”, Quarterly Journal of the Royal Meteorological Society, vol. 145, no. 718, pp. 53 - 75, 2019.
M. Bocquet, Brajard, J., Carrassi, A., and Bertino, L., “Data assimilation as a deep learning tool to infer ODE representations of dynamical models”, Nonlinear Processes in Geophysics Discussions, vol. Submitted, pp. 1 - 29, 2019.
J. Blyverket, Hamer, P., Bertino, L., Albergel, C., Fairbairn, D., and Lahoz, W., “An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US”, Remote Sensing, vol. 11, no. 5, p. 478, 2019.
F. Balibrea-Iniesta, Xie, J., García-Garrido, V. J., Bertino, L., Mancho, A. M., and Wiggins, S., “Lagrangian transport across the upper Arctic waters in the Canada Basin”, Quarterly Journal of the Royal Meteorological Society, vol. 145, no. 718, pp. 76 - 91, 2019.
X. He, Lucatero, D., Ridler, M. - E., Madsen, H., Kidmose, J., Hole, Ø., Petersen, C., Zheng, C., and Refsgaard, J. Christian, “Real-time simulation of surface water and groundwater with data assimilation”, Advances in Water Resources, vol. 127, pp. 13 - 25, 2019.
2018
G. Evensen, “Analysis of iterative ensemble smoothers for solving inverse problems”, Computational Geosciences, 2018.
A. Shah, Gharamti, M. El, and Bertino, L., “Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter”, Quarterly Journal of the Royal Meteorological Society, 2018.
C. Grudzien, Carrassi, A., and Bocquet, M., “Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error”, Nonlinear Processes in Geophysics, vol. 25, no. 3, pp. 633 - 648, 2018.
A. Carrassi, Bocquet, M., Bertino, L., and Evensen, G., “Data assimilation in the geosciences: An overview of methods, issues, and perspectives”, Wiley Interdisciplinary Reviews: Climate Change, vol. 9, no. 5, p. e535, 2018.
K. Aalstad, Westermann, S., Schuler, T. Vikhamar, Boike, J., and Bertino, L., “Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites”, The Cryosphere, vol. 12, no. 1, pp. 247 - 270, 2018.
J. Xie, Counillon, F., and Bertino, L., “Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis”, The Cryosphere, vol. 12, no. 11, pp. 3671 - 3691, 2018.
M. Rabatel, Rampal, P., Carrassi, A., Bertino, L., and 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, vol. 12, no. 3, pp. 935 - 953, 2018.
L. Yu, Fennel, K., Bertino, L., Gharamti, M. El, and Thompson, K. R., “Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling”, Ocean Modelling, vol. 126, pp. 13 - 28, 2018.
M. Kimmritz, Counillon, F., Bitz, C. M., Massonnet, F., Bethke, I., and Gao, Y., “Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model”, Tellus A: Dynamic Meteorology and Oceanography, vol. 70, no. 1, pp. 1 - 23, 2018.
J. Xie, Bertino, L., Cardellach, E., Semmling, M., and Wickert, J., “An OSSE evaluation of the GNSS-R altimetry data for the GEROS-ISS mission as a complement to the existing observational networks”, Remote Sensing of Environment, vol. 209, pp. 152 - 165, 2018.
A. Aydoğdu, Hoar, T. J., Vukicevic, T., Anderson, J. L., Pinardi, N., Karspeck, A., Hendricks, J., Collins, N., Macchia, F., and Özsoy, E., “OSSE for a sustainable marine observing network in the Sea of Marmara”, Nonlinear Processes in Geophysics, vol. 25, no. 3, pp. 537 - 551, 2018.
2017
M. - E. Ridler, Zhang, D., Madsen, H., Kidmose, J., Refsgaard, J. C., and Jensen, K. H., “Bias-aware data assimilation in integrated hydrological modelling”, Hydrology Research, p. nh2017117, 2017.
L. Bertino and Holland, M. M., “Coupled ice-ocean modeling and predictions”, Journal of Marine Research, vol. 75, no. 6, pp. 839 - 875, 2017.
M. Bocquet, Gurumoorthy, K. S., Apte, A., Carrassi, A., Grudzien, C., and Jones, C. K. R. T., “Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace”, SIAM/ASA Journal on Uncertainty Quantification, vol. 5, no. 1, pp. 304 - 333, 2017.
M. E. Gharamti, Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., and Bertino, L., “Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites”, Ocean Modelling, vol. 112, pp. 65 - 89, 2017.
A. Carrassi, Bocquet, M., Hannart, A., and Ghil, M., “Estimating model evidence using data assimilation”, Quarterly Journal of the Royal Meteorological Society, vol. 143, no. 703, pp. 866 - 880, 2017.
M. Bocquet and Carrassi, A., “Four-dimensional ensemble variational data assimilation and the unstable subspace”, Tellus A: Dynamic Meteorology and Oceanography, vol. 69, no. 1, p. 1304504, 2017.
M. E. Gharamti, Samuelsen, A., Bertino, L., Simon, E., Korosov, A., and Daewel, U., “Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic”, Journal of Marine Systems, vol. 168, pp. 1 - 16, 2017.
Y. Wang, Counillon, F., Bethke, I., Keenlyside, N., Bocquet, M., and Shen, M. -lin, “Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation”, Ocean Modelling, vol. 114, pp. 33 - 44, 2017.

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