What is he best known for?
The fields of study he is best known for:
- Oceanography
- Statistics
- Artificial intelligence
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 most cited work include:
- TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic (212 citations)
- TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic (212 citations)
- Sequential Data Assimilation Techniques in Oceanography (207 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Data assimilation (48.86%)
- Climatology (44.32%)
- Ensemble Kalman filter (30.11%)
What were the highlights of his more recent work (between 2018-2021)?
- Data assimilation (48.86%)
- Arctic (20.45%)
- Climatology (44.32%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- From Observation to Information and Users: The Copernicus Marine Service Perspective (38 citations)
- Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks (26 citations)
- Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model (25 citations)
In his most recent research, the most cited papers focused on:
- Oceanography
- Statistics
- Artificial intelligence
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.
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