Publications

2019

Raanes PN, Bocquet M, Carrassi A. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. Quarterly Journal of the Royal Meteorological Society [Internet]. 2019;145(718):53 - 75. http://doi.wiley.com/10.1002/qj.2019.145.issue-718
Balibrea-Iniesta F, Xie J, García-Garrido VJ, Bertino L, Mancho AM, Wiggins S. Lagrangian transport across the upper Arctic waters in the Canada Basin. Quarterly Journal of the Royal Meteorological Society [Internet]. 2019;145(718):76 - 91. http://doi.wiley.com/10.1002/qj.2019.145.issue-718
Bocquet M, Brajard J, Carrassi A, Bertino L. Data assimilation as a deep learning tool to infer ODE representations of dynamical models. Nonlinear Processes in Geophysics Discussions [Internet]. 2019;Submitted:1 - 29. https://www.nonlin-processes-geophys-discuss.net/npg-2019-7/
Evensen G. Accounting for model errors in iterative ensemble smoothers. Computational Geosciences [Internet]. 2019;. http://link.springer.com/10.1007/s10596-019-9819-z
He X, Lucatero D, Ridler M-E, Madsen H, Kidmose J, Hole Ø, et al.. Real-time simulation of surface water and groundwater with data assimilation. Advances in Water Resources [Internet]. 2019;127:13 - 25. https://linkinghub.elsevier.com/retrieve/pii/S0309170818307735
Blyverket J, Hamer P, Bertino L, Albergel C, Fairbairn D, 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 [Internet]. 2019;11(5):478. http://www.mdpi.com/2072-4292/11/5/478

2018

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 [Internet]. 2018;25(3):633 - 648. https://www.nonlin-processes-geophys.net/25/633/2018/
Xie J, Bertino L, Cardellach E, Semmling M, 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 [Internet]. 2018;209:152 - 165. https://www.sciencedirect.com/science/article/pii/S0034425718300658
Aydoğdu A, Hoar TJ, Vukicevic T, Anderson JL, Pinardi N, Karspeck A, et al.. OSSE for a sustainable marine observing network in the Sea of Marmara. Nonlinear Processes in Geophysics [Internet]. 2018;25(3):537 - 551. https://www.nonlin-processes-geophys.net/25/537/2018/
Aalstad K, Westermann S, Schuler TVikhamar, Boike J, Bertino L. Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere [Internet]. 2018;12(1):247 - 270. https://www.the-cryosphere.net/12/247/2018/tc-12-247-2018.pdf
Kimmritz M, Counillon F, Bitz CM, Massonnet F, Bethke I, 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 [Internet]. 2018;70(1):1 - 23. https://www.tandfonline.com/doi/full/10.1080/16000870.2018.1435945
Rabatel M, Rampal P, Carrassi A, Bertino L, Jones CKRT. Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic. The Cryosphere [Internet]. 2018;12(3):935 - 953. https://www.the-cryosphere.net/12/935/2018/tc-12-935-2018.pdf
Shah A, Gharamti MEl, Bertino L. Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter. Quarterly Journal of the Royal Meteorological Society [Internet]. 2018;. http://doi.wiley.com/10.1002/qj.3381
Carrassi A, Bocquet M, Bertino L, Evensen G. Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change [Internet]. 2018;9(5):e535. http://doi.wiley.com/10.1002/wcc.2018.9.issue-5
Evensen G. Analysis of iterative ensemble smoothers for solving inverse problems. Computational Geosciences [Internet]. 2018;. http://link.springer.com/10.1007/s10596-018-9731-y
Xie J, Counillon F, Bertino L. Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis. The Cryosphere [Internet]. 2018;12(11):3671 - 3691. https://www.the-cryosphere.net/12/3671/2018/
Yu L, Fennel K, Bertino L, Gharamti MEl, Thompson KR. Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling. Ocean Modelling [Internet]. 2018;126:13 - 28. https://linkinghub.elsevier.com/retrieve/pii/S1463500318301318

2017

Ridler M-E, Zhang D, Madsen H, Kidmose J, Refsgaard JC, Jensen KH. Bias-aware data assimilation in integrated hydrological modelling. Hydrology Research [Internet]. 2017;:nh2017117. http://hr.iwaponline.com/lookup/doi/10.2166/nh.2017.117
Gharamti ME, Tjiputra J, Bethke I, Samuelsen A, Skjelvan I, Bentsen M, et al.. Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites. Ocean Modelling [Internet]. 2017;112:65 - 89. http://linkinghub.elsevier.com/retrieve/pii/S1463500317300215
Vira J, Carboni E, Grainger RG, 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 [Internet]. 2017;10(5):1985 - 2008. https://www.geosci-model-dev.net/10/1985/2017/
Xie J, Bertino L, Counillon F, Lisæter KA, Sakov P. Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013. Ocean Science [Internet]. 2017;13(1):123 - 144. https://www.ocean-sci.net/13/123/2017/
Gharamti ME, Samuelsen A, Bertino L, Simon E, Korosov A, 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 [Internet]. 2017;168:1 - 16. http://linkinghub.elsevier.com/retrieve/pii/S0924796316304407
Bocquet M, Carrassi A. Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus A: Dynamic Meteorology and Oceanography [Internet]. 2017;69(1):1304504. https://www.tandfonline.com/doi/full/10.1080/16000870.2017.1304504
Carrassi A, Bocquet M, Hannart A, Ghil M. Estimating model evidence using data assimilation. Quarterly Journal of the Royal Meteorological Society [Internet]. 2017;143(703):866 - 880. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2972
Bocquet M, Gurumoorthy KS, Apte A, Carrassi A, Grudzien C, Jones CKRT. Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace. SIAM/ASA Journal on Uncertainty Quantification [Internet]. 2017;5(1):304 - 333. http://epubs.siam.org/doi/10.1137/16M1068712http://epubs.siam.org/doi/pdf/10.1137/16M1068712
Gurumoorthy KS, Grudzien C, Apte A, Carrassi A, Jones CKRT. Rank Deficiency of Kalman Error Covariance Matrices in Linear Time-Varying System With Deterministic Evolution. SIAM Journal on Control and Optimization [Internet]. 2017;55(2):741 - 759. http://epubs.siam.org/doi/10.1137/15M1025839http://epubs.siam.org/doi/pdf/10.1137/15M1025839
Bertino L, Holland MM. Coupled ice-ocean modeling and predictions. Journal of Marine Research [Internet]. 2017;75(6):839 - 875. http://www.ingentaconnect.com/content/10.1357/002224017823524017
Wang Y, Counillon F, Bethke I, Keenlyside N, Bocquet M, Shen M-lin. Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling [Internet]. 2017;114:33 - 44. http://linkinghub.elsevier.com/retrieve/pii/S146350031730063X
Buehner M, Bertino L, Caya A, Heimbach P, Smith G. Sea Ice Data Assimilation [Internet]. Sea Ice Analysis and Forecasting - Towards an Increased Reliance on Automated Prediction Systems . Cambridge, USA: Cambridge University Press; 2017 pp. 109-143. Report No.: Chapter 4. https://www.cambridge.org/core/product/identifier/9781108277600/type/book

2016

Xie J, Counillon F, Bertino L, Tian-Kunze X, Kaleschke L. Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system. The Cryosphere [Internet]. 2016;10(6):2745 - 2761. http://www.the-cryosphere.net/10/2745/2016/
Zhang D, Madsen H, Ridler ME, Kidmose J, Jensen KH, Refsgaard JC. Multivariate hydrological data assimilation of soil moisture and groundwaterhead. Hydrology and Earth System Sciences [Internet]. 2016;20(10):4341 - 4357. http://www.hydrol-earth-syst-sci.net/20/4341/2016/
Wang Y, Counillon F, Bertino L. Alleviating the bias induced by the linear analysis update with an isopycnal ocean model. Quarterly Journal of the Royal Meteorological Society [Internet]. 2016;142(695):1064 - 1074. http://doi.wiley.com/10.1002/qj.2016.142.issue-695
Rasmussen J, Madsen H, Jensen KHøgh, Refsgaard JChristian. Data assimilation in integrated hydrological modelling in the presence of observation bias. Hydrology and Earth System Sciences [Internet]. 2016;20(5):2103 - 2118. http://www.hydrol-earth-syst-sci.net/20/2103/2016/
Raanes PNima. On the ensemble Rauch-Tung-Striebel smoother and its equivalence to the ensemble Kalman smoother. Quarterly Journal of the Royal Meteorological Society [Internet]. 2016;142(696):1259 - 1264. http://doi.wiley.com/10.1002/qj.2016.142.issue-696
Carrassi A, Guemas V, Doblas-Reyes FJ, Volpi D, Asif M. Sources of skill in near-term climate prediction: generating initial conditions. Climate Dynamics [Internet]. 2016;47(12):3693 - 3712. http://link.springer.com/10.1007/s00382-016-3036-4
Zaytsev V, Biver P, Wackernagel H, Allard D. Change-of-Support Models on Irregular Grids for Geostatistical Simulation. Mathematical Geosciences [Internet]. 2016;48(4):353 - 369. http://link.springer.com/10.1007/s11004-015-9614-x
Pazó D, Carrassi A, López JM. Data assimilation by delay-coordinate nudging. Quarterly Journal of the Royal Meteorological Society [Internet]. 2016;142(696):1290 - 1299. http://doi.wiley.com/10.1002/qj.2016.142.issue-696
Counillon F, Keenlyside N, Bethke I, Wang Y, Billeau S, Shen MLin, et al.. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model. Tellus A: Dynamic Meteorology and Oceanography [Internet]. 2016;68(1):32437. https://www.tandfonline.com/doi/full/10.3402/tellusa.v68.32437

2015

Vira J, Sofiev M. Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model. Geoscientific Model Development [Internet]. 2015;8(2):191 - 203. http://www.geosci-model-dev.net/8/191/2015/
Rasmussen J, Madsen H, Jensen KH, Refsgaard JC. 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 [Internet]. 2015;19(7):2999 - 3013. http://www.hydrol-earth-syst-sci.net/19/2999/2015/
Bocquet M, Raanes PN, Hannart A. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation. Nonlinear Processes in Geophysics [Internet]. 2015;22(6):645 - 662. http://www.nonlin-processes-geophys.net/22/645/2015/

2014

Ridler ME, van Velzen N, Hummel S, Sandholt I, Falk AKatrine, Heemink A, et al.. Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI). Environmental Modelling & Software [Internet]. 2014;57:76 - 89. http://linkinghub.elsevier.com/retrieve/pii/S1364815214000590
Gray M, Petit C, Rodionov S, Bocquet M, Bertino L, Ferrari M, et al.. Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results. Optics Express [Internet]. 2014;22(17):20894. https://www.osapublishing.org/oe/abstract.cfm?uri=oe-22-17-20894