ORSS Summer school: statistical methods for ocean remote sensing
27 Mars 2015
Catégorie : Ecole thématique
Ocean Remote Sensing Synergy, focus on statistical approaches, Brest, July 6 to 10, 2015
We are glad to announce the second "Ocean Remote Sensing Synergy" summer school at Telecom Bretagne, Brest, France, from July 6 to 10, 2015. The focus will be given to statistical methods for ocean remote sensing synergies (see the detailed program below). The participants will benefit from direct interaction and discussions with the lecturers. Participants will also have the possibility to present the results of their research, and to interact with their scientific peers, in a friendly and constructive environment. The school is targeted towards scientists involved in spatial oceanography science with interest in statistics and vice-versa. The school fees are 250 euros for the week, all inclusive (single room, breakfasts, lunches, dinners, gala dinner). The school will be open to at most 25 candidates. Graduate students, doctoral students, post-docs, young researchers, senior researchers and practitioners are encouraged to apply.
More information and registration details are given here:
The organizing committee,
Fabrice Collard, René Garello, Pierre Tandeo, Ronan Fablet and Bertrand Chapron.
Ocean Remote Sensing Synergy, focus on statistical approaches
- Prof. Marc Genton (King Abdullah University of Science and Technology, Saudi Arabia)
- Dr. Juan Ruiz (University of Buenos Aires, Argentina)
- Dr. Ying Sun (King Abdullah University of Science and Technology, Saudi Arabia)
- Dr. Bertrand Chapron (IFREMER, France)
- Dr. Jean Tournadre (IFREMER, France)
- Dr. Fabrice Collard (OceanDataLab, France)
- Dr. Clément Le Goff (Telecom Bretagne, France)
- Prof. Ronan Fablet (Telecom Bretagne, France)
- Dr. Pierre Tandeo (Telecom Bretagne, France)
During the last decade, the ocean community witnessed worldwide the launch of over 30 new ocean-related satellite missions. Plans for new satellites, to improve the spatial-temporal sampling, are already laid well into the foreseeable future, and today, we are already talking Petabytes of data to download, analyze, transform into accessible information. Increasing computer power and understandings of relevant physical processes are also rapidly evolving, and contribute to advances in model accuracy and resolution refinement. The different satellite sensors can only be combined to provide the required high spatio-temporal sampling using physically or statistically based merging approaches.
Aim of the summer school
This year, the Summer School will focus on statistical and Bayesian methods for Ocean Remote Sensing Synergies. Lectures by invited speakers will provide both a broad coverage of statistical tools and models (e.g., geostatistics, regression, machine learning, state-space models) and applications to multi-sensor/multi-tracer ocean sensing data (e.g., data assimilation, missing data interpolation, statistical downscaling,...). Each lecture will comprise a Matlab practical session with applications to multi-sensor ocean remote sensing data, especially satellite-derived Sea Surface Temperature (SST) and Sea Surface Height (SSH).