Most of the processes studied by geoscientists are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modeling, and forecasting complex spatio-temporal systems through the use of stochastics non-linear models.
These issues are particularly relevant in the field of performance evaluations of Earth Systems Science Prediction (ESSP) systems. A central issue in this domain deals with the representativeness of observational data used for evaluation, which are often not representative of the physical structures that are being predicted. While many large spatial and temporal observations datasets can help provide this information, adequate tools to integrate these large datasets to provide meaningful physical insights on the strengths and weaknesses of predicted fields are required. Other challenges deal with the large storage volumes to handle model simulations, large spatio-temporal datasets, and verification statistics which are difficult to maintain.
This session aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal geostatistics or data mining on topics that include, but are not limited to:
- meaningful and informative model evaluation frameworks and platforms for ESSP;
- advances in spatio-temporal modeling using geostatistics and machine learning;
- uncertainty quantification and representation;
- innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
The main applications will be closely related to the research in Earth system science and quantitative geography. A non-complete list of possible applications includes:
- weather and climate (e.g. numerical weather prediction, hydrologic prediction, climate prediction and projection);
- natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; air pollution);
- interaction between geosphere and anthroposphere (e.g. land degradation; urban sprawl);
- socio-economic sciences (e.g. census data; transport; commuter traffic).
This is a merged session of “Spatio-temporal data science: theoretical advances and applications in computational geosciences” and “Innovative Evaluation Frameworks and Platforms for Weather and Climate Research”.
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