Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data

arXiv:2404.08613v1 Announce Type: cross
Abstract: Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network …

abstract artificial artificial intelligence arxiv blocking class climate climate models data events explainable ai explainable artificial intelligence however intelligence maintenance physics.ao-ph stat.ml systems transfer transfer learning type weather