interTwin Use Case

A Digital Twin for projecting the occurrence of tropical cyclones due to climate change

Designing Deep neural networks (DNNs) to create Digital Twins capable of detecting tropical cyclones on climate change future projections

Background

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.

Challenge

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.

Solution

In interTwin, a team of climate and computer scientists from CMCC, the University of Trento, and IPSL is developing a set of use cases related to the analysis of climate projections of extreme events.

The DTs rely on harnessing knowledge from the huge amount of historical climate and environmental data to train deep neural networks (DNN) capable of predicting and detecting extreme events like wildfires and tropical cyclones. Different DNN architectures will be evaluated to address the various use cases, including Convolutional and Graph networks. The trained models will be used as a base for understanding how different climate scenarios can affect extreme events frequency, locations, probability, etc. Future projection data from large experiments, like the Climate Model Intercomparison Project 6th phase (CMIP6), will be used as input for the trained models.

A set of thematic modules implemented within the project will provide the backbone of the DTs and allow developers to set up additional DTs targeting different applications. On the other side, end-users (e.g., domain scientists and policy makers) will be able to run the DTs for the analysis of extreme events under different climate scenarios. The results will be produced as NetCDF files or as customizable visualizations through easy-to-use solutions like dashboards or Jupyter Notebooks.

The DTs for climate change future projections of extreme events will provide end-users with novel analysis solutions exploiting cutting-edge data-driven approaches. The InterTwin project will support the execution of complex workflows for data management, pre/post-processing and ML model training/inference as well as increase automation of the overall process from data gathering to result production.

Donatello Elia, (Scientist, CMCC)

Related publications

Scientific article

Accarino, G., Donno, D., Immorlano, F., Elia, D., & Aloisio, G. (2023). An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data. Earth and Space Science, 10. https://doi.org/10.1029/2023EA003106