Laurent Bertino

Job Position: 
Research Leader/Senior Researcher
Research Group: 
E-mail: 
Phone: 
+47 934 56 215
Laurent Bertino Staff Picture

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.

CV

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cv-laurent_bertino_2p_2018.pdf90.59 KB

Project leader of the following projects

  1. ARC MFC: Copernicus Arctic Marine Services
  2. SWARP: Ships and Waves Reaching Polar Regions
  3. ES-PhD: Ensemble Smoother - Patrick Raanes

NERSC Principal Investigator of the following projects

  1. EVA: Earth system modelling of climate Variations in the Anthropocene
  2. SANGOMA: Stochastic Assimilation for the Next Generation Ocean Model Applications
  3. SeaFINE: Eddy-resolving South China Sea reanalysis
  4. CarboChange: Changes in carbon uptake and emissions by oceans in a changing climate

Project deputy leader of the following projects

  1. EmblA: Ensemble-based data assimilation for environmental monitoring and prediction
  2. OC-CCI: The ESA Ocean Colour Climate Change Initiative
  3. MyOcean: Development and Pre-operational validation of GMES Marine Core Services
  4. eVITA-EnKF: Forecasting non-linear systems using the ensemble Kalman filter and related methods
  5. MERSEA: Marine Environment and Security for the European Area

Publications

My Publications in CRIStin

Peer Review Publications and Books

111 total publications.

2021

  1. Mangini F, Chafik L, Madonna E, Li C, Bertino L, Nilsen JEØ. The relationship between the Eddy-Driven Jet Stream and Northern European Sea Level variability. Tellus. Series A, Dynamic meteorology and oceanography. 2021;73(1).
  2. Brajard J, Carrassi A, Bocquet M, Bertino L. 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

  1. Bocquet M, Brajard J, Carrassi A, Bertino L. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS). 2020;2(1).
  2. Brajard J, Carrassi A, Bocquet M, Bertino L. 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.
  3. Chatterjee S, Raj RP, Bertino L, Mernild JSH, Murukesh N, Ravichandran M. The impact of atmospheric and oceanic circulations on the Greenland Sea iceconcentration. The Cryosphere Discussions. 2020;
  4. Raj RP, Andersen OB, Johannessen JA, Benjamin G, Chatterjee S, stine rose, et al. Arctic sea level budget assessment during the GRACE/Argo time period. Remote Sensing. 2020;12(17).
  5. Xie J, de Vos M, Bertino L, Zhu J, Counillon F. Impact of assimilating altimeter data on eddy characteristics in the South China Sea. Ocean Modelling. 2020;155.
  6. Cheng S, Aydoğdu A, Rampal P, Carrassi A, Bertino L. Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion. Oceans. 2020;
  7. von Schuckmann K, Traon P-YL, Smith N, Pascual A, Djavidnia S, Gattuso J-P, et al. Copernicus Marine Service Ocean State Report, issue 4. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology. 2020;13(1).
  8. Aalstad K, Westermann S, Bertino L. Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment. 2020;239.
  9. Bertino L, Xie J. Operational Forecasting of Sea Ice in the Arctic Using TOPAZ system. In: Sea Ice in the Arctic, Past, Present and Future. 2020.

2019

  1. Brajard J, Bocquet M, Carrassi A, Bertino L. 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.
  2. Blyverket J, Hamer PD, Bertino L, Albergel C, Fairbairn D, Lahoz WA. 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. 2019;11(5).
  3. Moore AM, Martin MJ, Santha A, Arango HG, Balmaseda M, Bertino L, et al. Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean. Frontiers in Marine Science. 2019;6.
  4. Ali A, Christensen KH, Breivik Ø, Bertino L, Malila MP, Raj RP, et al. A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans. Ocean Modelling. 2019;137.
  5. Bertino L, Traon P-YL. From Observation to Information and Users: The Copernicus Marine Service Perspective. Frontiers in Marine Science. 2019;
  6. Fujii Y, Remy E, Hao Z, Oke P, Halliwell G, Gasparin F, et al. 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. Frontiers in Marine Science. 2019;
  7. Smith GC, Allard R, Babin M, Bertino L, Chevallier M, Corlett G, et al. 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;
  8. Shah A. Stochastic data assimilation of observations with a detection limit. 2019.
  9. Bocquet M, Brajard J, Carrassi A, Bertino L. 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

  1. Smith GC, Allard R, Babin M, Bertino L, Chevallier M, Corlett G, et al. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. 2019;6:429.
  2. Traon P-YL, Reppucci A, Alvarez Fanjul E, Aouf L, Behrens A, Belmonte M, et al. From observation to information and users: The Copernicus Marine Service Perspective. 2019;6:234.
  3. Fujii Y, Rémy E, Hao Z, Oke P, Halliwell G, Gasparin F, et al. Observing system evaluation based on ocean data assimilation and prediction systems: On-going challenges and future vision for designing/supporting ocean observational networks [Internet]. 2019;6:417. Available from: http://hdl.handle.net/1956/21986

2014

  1. Renkl C, Counillon F, Bertino L. Validation of the 20-year TOPAZ4 Reanalysis. 2014.

2013

  1. Finck N, Counillon F, Bertino L, Bouillon S, Rampal P. Validation of sea ice quantities of TOPAZ for the period 1990-2010. 2013.

2012

  1. Simon E, Samuelsen A, Bertino L. Report on error quantified hindcast with data assimilation (GreenSeas). 2012.
  2. Demchev D, Aleksandrov V, Volkov V, Wehde H, Zakhvatkina N, Bertino L, et al. Quality Information Document MyOcean V1 System WP05– ARC-MFC Reanalysis [Internet]. 2012. Available from: ftp://ftp.nersc.no/pub/Laurent/MyOcean/MYO_QUID-ARC-MFC-RA_NERSC_TechRep_final.pdf

2011

  1. Bertino L, Evensen G, Sakov P, Counillon F, Simon E, Bojarova J, et al. Forecasting non-linear systems with the Ensemble Kalman Filter and related data assimilation methods (eVITA-EnKF). 2011.
  2. Bertino L. Challenges and capabilities of data assimilation. Nansen-Tutu Centre Scientific Opening Symposium. 2011;
  3. Keghouche I, Bertino L, Johannessen OM. Ocean weather and ecosystem. 2011.
  4. Bertino L, Sakova I, Samuelsen A. Ocean weather and ecosystem, progress report. 2011.

2010

  1. Counillon F, Bentsen M, Bertino L. Implementing the EnKF into NorESM. 2010.
  2. Simon E, Bertino L. 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
  3. Haugen SA, Sagen H, Sandven S, Bertino L, Sakov P, Counillon F. Use of acoustic Travel Times for ocean model validation, and assimilation. 2010;
  4. Bertino L. Assimilation of ice and ocean observations for the operational forecast and reanalysis with TOPAZ. 2010;
  5. Sagen H, Sandven S, Skarsoulis E, Worcester PF, Beszczynska-Möller A, Bertino L, et al. The fram Strait tomography system for ocean model validation, assimilation, and inversion. IPY - Oslo Science Conference. 2010;
  6. Nondal G. A study of the high and mid latitude biogeochemistry in the Atlantic Ocean: The influence of surface processes. 2010.
  7. Seiler A. Reservoir structural model updating using the Ensemble Kalman Filter. 2010.
  8. Keghouche I. Modeling the dynamics and drift of icebergs in the Barents Sea. 2010.
  9. Backeberg B. Modelling the mesoscale variability in the greater Agulhas Current System using a Hybrid Coordinate Ocean Model. 2010.
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