Deep Learning-Based Prediction of Severe Convective Storm Perils

As a leading commercial insurer specializing in property and infrastructure - including the rapidly expanding renewables sector - FM faces growing exposure to the impacts of severe convective storms (SCS). These high-impact weather events, which encompass hail, tornadoes, straight-line winds, lightning, and heavy precipitation, pose significant risks to insured assets. In this presentation, we introduce a suite of in-house predictive models developed to estimate both the frequency and intensity of these SCS perils. Our approach integrates observational data with state-of-the-art deep learning techniques to process and interpret high-dimensional meteorological inputs.

The models are trained on a comprehensive set of predictors derived internally from ERA5 reanalysis data, ensuring consistency and scalability across spatial and temporal domains. Deep learning models are particularly well-suited for this task, as they efficiently capture spatial dependencies and patterns within gridded atmospheric fields, enabling robust identification of conditions conducive to severe weather events.

These predictive tools are used internally to support underwriting activities, risk pricing, and accumulation management. In addition to peril-specific risk metrics, we introduce cross-peril correlation maps—particularly between hail and straight-line wind—that are instrumental in supporting FM’s growing Renewables business.

By combining physical understanding with data-driven modeling, our framework offers a scalable and interpretable solution for SCS risk assessment. The presentation highlights model architecture, training methodology, and selected case studies demonstrating operational relevance and performance.