In recent years, Climate Change has been already manifesting widespread effects on the environment; the exacerbation of extreme weather events is raising major concerns in terms of increased intensity, frequency and duration.
Extreme events such as tropical storms and wildfires represent significant natural hazards that can disrupt ecosystems, and cause economic losses and casualties. Phenomena like these often reach their full destructive potential by leveraging other natural processes. An example of this was Katrina, a tropical cyclone that entered the Gulf of Mexico and rapidly intensified into a Category 5 hurricane due to the presence of a warm eddy that was stationed in the gulf.
Tropical cyclones (TCs), also known as hurricanes or typhoons, are among the most lethal and expensive natural catastrophes. They are warm-core, large-scale cyclones, originating over tropical or subtropical waters, with organized deep convection and a closed surface wind circulation about a well-defined low pressure center. Detecting, localizing and tracking such phenomena is not an easy task but it is essential to reduce their impact on people. Moreover, it is very important to understand how climate change is affecting these phenomena.
Oceanic mesoscale eddies are fundamental components of the ocean circulation, responsible for the transportation of heat, salt, and nutrients. They are circular or spiral-like flow patterns that occur at a relatively small spatial scale, typically ranging from tens of kilometers to a few hundred kilometers in diameter. Despite being relatively small, they can influence the activity of tropical cyclones, and mapping them can help make considerations about possible correlations.
Detecting and predicting extreme events is challenging due to the rare occurrence of these events, and the consequent lack of related historical data. Recent advances in Machine Learning (ML) provide cutting-edge modeling techniques to deal with detection and prediction tasks, offering also cost-effective and fast-computing solutions.
Digital twins, based on data-driven models, for the analysis of extreme and natural events on climate projections, will help understand how climate change is affecting the frequency and location of extreme events, according to different future projection scenarios based on different socioeconomic changes.
