Laurent Bertino

Research interests
My aim is to improve the stochastic framework for performing data assimilation, other interests include numerical ocean modelling (especially in the hybrid coordinates ocean model HYCOM) and statistics applied to environmental problems. My PhD has borrowed the method of "Gaussian anamorphosis" from the weaponry of geostatistics for further applications of sequential data assimilation into coupled physical-ecosystem models.
2001: PhD in geostatistics from the Ecole des Mines de Paris.
2002: Post-doc at NERSC
2003: Group leader for the Modeling and Data Assimilation group.
2004: Co-director of the Mohn-Sverdrup Center.
2010: Research Director at NERSC.
Project leader of the following projects
NERSC Principal Investigator of the following projects
Project deputy leader of the following projects
Publications
Peer Review Publications and Books
2021
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The relationship between the Eddy-Driven Jet Stream and Northern European Sea Level variability. Tellus. Series A, Dynamic meteorology and oceanography. 2021;73(1)..
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Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021;.
2020
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Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS). 2020;2(1)..
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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Journal of Computational Science. 2020;44..
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The impact of atmospheric and oceanic circulations on the Greenland Sea iceconcentration. The Cryosphere Discussions. 2020;.
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Arctic sea level budget assessment during the GRACE/Argo time period. Remote Sensing. 2020;12(17).
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Impact of assimilating altimeter data on eddy characteristics in the South China Sea. Ocean Modelling. 2020;155..
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Copernicus Marine Service Ocean State Report, issue 4. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology. 2020;13(1).
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Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment. 2020;239..
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Operational Forecasting of Sea Ice in the Arctic Using TOPAZ system. In: Sea Ice in the Arctic, Past, Present and Future. 2020..
2019
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Connections between data assimilation and machine learning to emulate a numerical model. In: Proceedings of the 9th International Workshop on Climate informatics: CI 2019. 2019..
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From Observation to Information and Users: The Copernicus Marine Service Perspective. Frontiers in Marine Science. 2019;.
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Polar Ocean Observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Frontiers in Marine Science. 2019;
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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics. 2019;26(3)..
Other Publications
2019
2014
2012
2011
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Challenges and capabilities of data assimilation. Nansen-Tutu Centre Scientific Opening Symposium. 2011;.
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Ocean weather and ecosystem. 2011..
2010
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Joint state-parameter estimation in a 3-D coupled physical-ecosystem model of the North Atlantic: assimilation of SeaWiFS data with a non-Gaussian extension of an ESRF [Internet]. ESA Living Planet Symposium. 2010;Available from: http://www.esa.int/SPECIALS/Living_Planet_Symposium_2010/index.html.
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The fram Strait tomography system for ocean model validation, assimilation, and inversion. IPY - Oslo Science Conference. 2010;