Using Stochastic Data to Simulate and Communicate Alternative Multi-Hazard Weather Extreme Events

A stochastic weather generator (SWG) simulates realistic weather time series beyond the historical record by capturing the statistical properties of observed weather patterns. Here, we present a new spatiotemporal SWG, the MYRIAD Stochastic vIne-copula Model (MYRIAD-SIM), which simulates temperature, wind speed, and precipitation. MYRIAD-SIM captures both spatiotemporal and multivariate dependencies using conditional vine copulas. The simulated data enable new insights into compound climate and multi-hazard events by generating high-impact multivariate weather scenarios. For example, the triple storm sequence Dudley, Eunice, and Franklin, which impacted the UK and Europe in 2022, can be simulated as alternative triple-storm events, illustrating not only what happened but also what could have occurred under statistically plausible conditions, such as higher wind speeds or varying precipitation patterns. This study demonstrates how stochastic counterfactuals of historical events can support risk communication by framing hazards in a narrative, event-focused way rather than through abstract probabilities.