July 2017 Monday 3 Tuesday 4 Wednesday 5 Thursday 6 Friday 7
9h-12h Course 1 Session 1 Session 2/3 Session 4 Course 3
14h-17h Course 2 Session 2 Session 3 Session 5 Course 4





All the presentations and posters are respectively available here and here.



Session 1: "Oceanographic data & data science methods"

  • P.Y. Le Traon, “The Copernicus Marine Environment Monitoring Service”

  • E. Le Pennec, “A gentle introduction to Data Science”

  • J. Runge, “Causal inference methods for large-scale geoscientific time series datasets”

  • T. Romary, “Covariance decomposition for the kriging of large datasets”


Session 2: "Applications to Climate"

  • A. Hannart, "A few problems in climate research that data science may help tackle"

  • D. Hammerling, "Compression and conditional emulation of climate model output"

  • V. Martin Gomez, "Climate networks and atmospheric connectivity"

  • D. Nychka, "Large and non-stationary spatial fields: Quantifying uncertainty in the pattern scaling of climate models"

  • L. Terray, “Can Data Science help in the attribution of the southeastern United States Warming Hole?”

  • P. Naveau, “Revising return periods for record events in a climate event attribution context


Session 3: "Application to atmosphere and ocean sciences"

  • O. Mestre, “Calibration of numerical weather forecasts using machine learning algorithms”

  • E. Szekely, "Data-driven kernel methods for dynamical systems with application to atmosphere ocean science"

  • G. Maze, "Applications of ocean profile classification modelling"

  • A. Sykulski, “Stochastic lagrangian modelling of ocean surface drifter trajectories”

  • N. Raillard, “Spatial modeling of extreme sea-states”

  • M. Lopez, “Non negative decomposition for oceanographic data”


Session 4: "Data assimilation & high dimensionality"

  • T. Miyoshi, “Big Data Assimilation for 30-second-update 100-m-mesh Numerical Weather Prediction”

  • I. Hoteit, “Gaussian-mixture filtering high dimensional systems with small ensembles”

  • S. Ricci, “Environmental risk prediction using reduced-cost ensemble Kalman filter based on polynomial chaos surrogate”

  • M. Bocquet, “Dynamics-based reduction of data assimilation for chaotic models”


Session 5: "Analog methods & ensemble methods"

  • C. Wikle, “Recent advances in quantifying uncertainty in nonlinear spatio-temporal statistical models”

  • T. Penduff, “Probabilistic analysis of the OCCIPUT global ocean simulation ensemble”

  • R. Fablet, “Analog assimilation for high-dimensional geophysical dynamics”

  • P. McDermott, “A hierarchical spatio-temporal analog forecasting model for count data”


Poster session: Wednesday 5, 24 presented posters


Summer school

Course 1: "Oceanographic data" (in situ, satellite, simulations)

Course 2: "Data Science methods" (regression, clustering, classification)

Course 3: "Big Data for oceanography" (Google Clood Platform)

Course 4: "Practice: challenge for Data Science & Environment" (prediction of ENSO index)