Laurent Bertino spends much of his time researching Climatology, Data assimilation, Sea ice, Ensemble Kalman filter and Sea ice growth processes. Laurent Bertino regularly ties together related areas like Arctic in his Climatology studies. His biological study spans a wide range of topics, including Salinity, Ocean current and Underwater.
His work carried out in the field of Data assimilation brings together such families of science as Sea surface temperature and Data science. His Ensemble Kalman filter study combines topics from a wide range of disciplines, such as Algorithm, Covariance and Applied mathematics. His Sea ice growth processes study incorporates themes from Pressure ridge and Lead, Geophysics.
His scientific interests lie mostly in Data assimilation, Climatology, Ensemble Kalman filter, Sea ice and Meteorology. The Data assimilation study combines topics in areas such as Altimeter, Covariance, Artificial intelligence and Sea surface temperature. As part of his studies on Climatology, Laurent Bertino frequently links adjacent subjects like Arctic.
Laurent Bertino interconnects Algorithm, Applied mathematics and Nonlinear system in the investigation of issues within Ensemble Kalman filter. His Arctic ice pack study in the realm of Sea ice interacts with subjects such as Attenuation. Laurent Bertino works mostly in the field of Meteorology, limiting it down to topics relating to Temperature salinity diagrams and, in certain cases, Argo, as a part of the same area of interest.
Laurent Bertino focuses on Data assimilation, Arctic, Climatology, Artificial intelligence and Sea ice. His Data assimilation study incorporates themes from Systems engineering, Data-driven, State and Ensemble Kalman filter. His research on Ensemble Kalman filter often connects related areas such as Sea ice thickness.
He has researched Arctic in several fields, including Snow and Salinity, Sea surface salinity. Laurent Bertino is involved in the study of Climatology that focuses on Argo in particular. His studies deal with areas such as Ice shelf, Ocean current and Weather forecasting as well as Argo.
Data assimilation, Artificial intelligence, Chaotic, Artificial neural network and Machine learning are his primary areas of study. His work carried out in the field of Data assimilation brings together such families of science as State, Systems engineering and Data products. His Systems engineering research incorporates elements of Altimeter, Reliability, Ocean observations and Robustness.
His study in Ocean observations is interdisciplinary in nature, drawing from both Observational study, Argo, Underwater glider, Weather forecasting and Ocean current. His Chaotic study combines topics in areas such as Surrogate model, Lyapunov function, Bayesian probability and Ensemble Kalman filter. His research integrates issues of Dynamical systems theory, Deep learning, Inference and Ode in his study of Bayesian probability.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Sequential Data Assimilation Techniques in Oceanography
Laurent Bertino;Geir Evensen;Hans Wackernagel.
International Statistical Review (2003)
TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic
P. Sakov;P. Sakov;F. Counillon;F. Counillon;L. Bertino;L. Bertino;K. A. Lisæter.
Ocean Science (2012)
Data assimilation in the geosciences: An overview of methods, issues, and perspectives
Alberto Carrassi;Marc Bocquet;Laurent Bertino;Geir Evensen.
Wiley Interdisciplinary Reviews: Climate Change (2018)
On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice
Yevgeny Aksenov;Ekaterina E. Popova;Andrew Yool;A.J. George Nurser.
Marine Policy (2017)
Relation between two common localisation methods for the EnKF
Pavel Sakov;Laurent Bertino.
Computational Geosciences (2011)
Wave-ice interactions in the marginal ice zone. Part 1: Theoretical foundations
Timothy D. Williams;Luke G. Bennetts;Vernon A. Squire;Dany Dumont.
Ocean Modelling (2013)
An Iterative EnKF for Strongly Nonlinear Systems
Pavel Sakov;Dean S. Oliver;Laurent Bertino.
Monthly Weather Review (2012)
Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model
Ehouarn Simon;Laurent Bertino.
Journal of Marine Systems (2012)
Asynchronous data assimilation with the EnKF
Pavel Sakov;Geir Evensen;Geir Evensen;Laurent Bertino.
Tellus A (2010)
A wave‐based model for the marginal ice zone including a floe breaking parameterization
D. Dumont;D. Dumont;A. Kohout;L. Bertino.
Journal of Geophysical Research (2011)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: