Identifying Lightning Structures and Predicting Cloud Properties

This third talk in the DEEP-IN series focuses on using unsupervised machine learning to identify and predict patterns in atmospheric phenomena. We begin by demonstrating how clustering and dimensionality reduction techniques can uncover coherent lightning patterns from high-dimensional LOFAR (LOw Frequency ARray) data, offering insight into large-scale organization. We then show how generative diffusion models enable super-resolution retrieval of cloud properties for all day from satellite observations.

This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.