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.
This year, the Summer School will focus on the underlying physical framework for Ocean Remote Sensing Synergies interpretation. Lectures by invited speakers will provide both a broad coverage of oceanic modeling (e.g., surface quasi geostrophy, Lagrangian methods) and applications to multi-sensor/multi-tracer ocean sensing data (e.g., data assimilation, missing data interpolation). Each lecture will comprise a Python practical session with applications to multi-sensor ocean remote sensing data, especially satellite-derived Sea Surface Salinity (SSS), Temperature (SST) and Height (SSH).
Fabrice Collard - René Garello - Cristina Gonzalez Haro - Pierre Tandeo - Ronan Fablet - Bertrand Chapron