A machine learning-powered Digital Twin for extreme weather events analysis

In recent years, Climate Change has been leading to an exacerbation of Extreme Weather Events (EWEs), such as storms and wildfires, raising major concerns in terms of their increase of their intensity, frequency and duration. Detecting and predicting EWEs is challenging due to the rare occurrence of these events and consequently the lack of related historical data. Additionally, gathering of data when the event manifests is not a straightforward process, due to the intrinsic difficulty of positioning and using acquisition systems. Advances in Machine Learning (ML) can provide cutting-edge modeling techniques to deal with EWE detection and prediction tasks, offering cost-effective and fast-computing solutions which are strongly required by policy makers for taking timely and informed actions in the presence of EWEs.

Solutions based on ML could, thus, support studies of such extreme events, providing scientists, policy makers and also the general public with powerful and innovative data-driven tools. However, from an infrastructural point of view, supporting such types of applications requires a wide set of integrated software components including data gathering and harmonisation pipelines, data pre-processing and augmentation modules, computing platforms for model training, results visualization tools, etc.

A Digital Twin for the analysis of extreme weather events, focusing on storms and wildfires, is being developed in the context of the EU-funded InterTwin project. The InterTwin project aims at defining a Digital Twin Engine for supporting scientific applications from different fields. In particular, for the EWEs, neural networks are being adopted as modeling tools capable of learning the underlying mapping between drivers and outcomes from past data and generalizing it to future projection data. This contribution will present the early concept behind the design of this machine learning-powered Digital Twin for EWE studies.