Laurent Bertino

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2017
J. Xie, Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P., “Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013”, Ocean Science, vol. 13, no. 1, pp. 123 - 144, 2017.
K. S. Gurumoorthy, Grudzien, C., Apte, A., Carrassi, A., and Jones, C. K. R. T., “Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution”, SIAM Journal on Control and Optimization, vol. 55, no. 2, pp. 741 - 759, 2017.
M. Buehner, Bertino, L., Caya, A., Heimbach, P., and Smith, G., “Sea Ice Data Assimilation”, Cambridge University Press, Cambridge, USA, Chapter 4, 2017.
J. Vira, Carboni, E., Grainger, R. G., and Sofiev, M., “Variational assimilation of IASI SO2 plume height and total column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model”, Geoscientific Model Development, vol. 10, no. 5, pp. 1985 - 2008, 2017.
2016
Y. Wang, Counillon, F., and Bertino, L., “Alleviating the bias induced by the linear analysis update with an isopycnal ocean model”, Quarterly Journal of the Royal Meteorological Society, vol. 142, no. 695, pp. 1064 - 1074, 2016.
J. Xie, Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L., “Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system”, The Cryosphere, vol. 10, no. 6, pp. 2745 - 2761, 2016.
V. Zaytsev, Biver, P., Wackernagel, H., and Allard, D., “Change-of-Support Models on Irregular Grids for Geostatistical Simulation”, Mathematical Geosciences, vol. 48, no. 4, pp. 353 - 369, 2016.
D. Pazó, Carrassi, A., and López, J. M., “Data assimilation by delay-coordinate nudging”, Quarterly Journal of the Royal Meteorological Society, vol. 142, no. 696, pp. 1290 - 1299, 2016.
J. Rasmussen, Madsen, H., Jensen, K. Høgh, and Refsgaard, J. Christian, “Data assimilation in integrated hydrological modelling in the presence of observation bias”, Hydrology and Earth System Sciences, vol. 20, no. 5, pp. 2103 - 2118, 2016.
P. Nima Raanes, “On the ensemble Rauch-Tung-Striebel smoother and its equivalence to the ensemble Kalman smoother”, Quarterly Journal of the Royal Meteorological Society, vol. 142, no. 696, pp. 1259 - 1264, 2016.
F. Counillon, Keenlyside, N., Bethke, I., Wang, Y., Billeau, S., Shen, M. Lin, and Bentsen, M., “Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model”, Tellus A: Dynamic Meteorology and Oceanography, vol. 68, no. 1, p. 32437, 2016.
D. Zhang, Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., and Refsgaard, J. C., “Multivariate hydrological data assimilation of soil moisture and groundwaterhead”, Hydrology and Earth System Sciences, vol. 20, no. 10, pp. 4341 - 4357, 2016.
A. Carrassi, Guemas, V., Doblas-Reyes, F. J., Volpi, D., and Asif, M., “Sources of skill in near-term climate prediction: generating initial conditions”, Climate Dynamics, vol. 47, no. 12, pp. 3693 - 3712, 2016.
2015
J. Vira and Sofiev, M., “Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model”, Geoscientific Model Development, vol. 8, no. 2, pp. 191 - 203, 2015.
J. Rasmussen, Madsen, H., Jensen, K. H., and Refsgaard, J. C., “Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance”, Hydrology and Earth System Sciences, vol. 19, no. 7, pp. 2999 - 3013, 2015.
M. Bocquet, Raanes, P. N., and Hannart, A., “Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation”, Nonlinear Processes in Geophysics, vol. 22, no. 6, pp. 645 - 662, 2015.
2014
M. E. Ridler, van Velzen, N., Hummel, S., Sandholt, I., Falk, A. Katrine, Heemink, A., and Madsen, H., “Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI)”, Environmental Modelling & Software, vol. 57, pp. 76 - 89, 2014.
M. Gray, Petit, C., Rodionov, S., Bocquet, M., Bertino, L., Ferrari, M., and Fusco, T., “Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results”, Optics Express, vol. 22, no. 17, p. 20894, 2014.

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