Liberty Mutual Reinsurance and Celest.Science are exploring a fallback methodology for parametric tropical cyclone insurance designed to maintain event assessment when key weather data is unavailable or delayed. The collaboration will investigate how alternative data sources and climate modelling could help insurers and reinsurers characterise tropical cyclones when traditional observational inputs are disrupted.

LM Re and Celest.Science will combine expertise in parametric risk transfer and climate modelling to develop and test a methodology drawing on multiple independent data streams alongside physical and statistical modelling. The proposed approach aims to reduce reliance on individual data sources, helping maintain continuity and confidence in event assessments when information gaps occur. The companies said it could support faster, more transparent claims settlement following tropical cyclones.

Any future use of the methodology in an LM Re product would be subject to successful technical validation, relevant governance approvals and the terms of the applicable insurance or reinsurance contract.

Jean-Christophe Garaix, head of parametrics and agriculture, LM Re, said: “As the risk landscape evolves and the availability of critical observational data becomes increasingly uncertain, LM Re continues to explore innovative approaches intended to enhance the robustness of its parametric products. When a tropical cyclone strikes, our clients need timely access to funds so they can focus on recovery. Our collaboration with Celest.Science enables us to explore options that could provide that extra layer of resilience, even if a primary data source becomes unavailable.”

Léo Lemordant, co-founder and CEO of Celest.Science, added: “Extreme weather often unfolds where observations are fragmented or incomplete. By combining climate science, physics-based modelling and AI, we aim to explore how we can reconstruct a robust picture of what happened. Working with Liberty Mutual Re allows us to explore how that capability could support greater resilience in parametric insurance for end users.”

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