A Summer School on Climate Intelligence

The Climate Intelligence Summer School (17-20 June) offered a succinct education on sound research in the field of detection, causation, and attribution of extreme events. The summer school was organized by the CLINT project.

About 50 PhD students and researchers in any area of science or engineering participated to learn and discuss recent advances in climate science, machine learning, impact, and climate services.

Driven by the scientific developments in the CLINT project, the invited speakers focused on different topics, including:

Climate Intelligence: designing new Machine Learning algorithms to process big climatological data sets;

AI-enhanced Climate Sciences: advancing the state-of-the-art on detection, causation, and attribution of extreme climate events.

AI-enhanced Climate Services: developed at the EU continental scale across the water, energy, and food nexus and on selected climate change hotspots.

Climate Services Information Systems: deployed as web processing services based on advanced open software and data standards and as a commercial demo.

My presentation was entitled "Forecast skill as the «first» mile: From forecast value to enhanced prediction - Navigating services, challenges, and scientific advancements" and can be found below: