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 |
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"
M. Barreiro, "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”
M. Rochoux, “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”
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)