S2S-AI

Climate change is affecting extreme weather – we identify how and when society is at risk

Seasonal to sub-seasonal (S2S) predictability could provide societies with valuable information on weather-related risk, allowing decision-makers to initiate early warning action plans and to optimize resource management. Using Artificial Intelligence (AI), we are developing a rigorous data-driven framework that enables automatic detection of interpretable physical drivers on S2S timescales. Combining expert knowledge, causal inference and a variety of machine learning techniques, our tool has worked for science. Now we aim for societal impact by making predictions for stakeholders (e.g. NGO’s).

Read also the article at te VU website in the exhibition ‘A better World @ VU’ Artificial Intelligence for Long-term Weather Predictions – Meer over – Vrije Universiteit Amsterdam (vu.nl)