Environmental sciences have experienced a data deluge with the explosion in the amount of data produced by sensors and models that monitor, measure and forecast the Earth system. This exponential trend in data availability is expected to continue in the future thereby creating many new opportunities, needs and challenges. On the other hand, data science has emerged as a wide multidisciplinary dynamic which addresses challenges associated to large and complex data and encompasses diverse fields in applied mathematics and computer science.
Diverse themes will be treated:
- data acquisition and visualization
- in situ monitoring, remote sensing data, numerical simulations
- physical modeling (parameterizations and model selection)
- data analysis (machine learning, statistical models and stochastic processes)
- data assimilation (high dimensionality, error covariance estimation)
- climate (detection/attribution, extremes)
A special focus will be given to oceanographic data and related problems. Other fields of interest related to environment like meteorology, climate, biogeochemistry, geographic information system, are also welcome.