Parametric insurance payments depend on reliable event data. In a collaboration announced on October 26, 2026, Liberty Mutual Reinsurance, known as LM Re, and French climate-tech startup Celest.Science are developing a fallback methodology to help characterise tropical cyclones when primary observations are unavailable or delayed. The work remains developmental, with any future product use subject to technical validation and approvals.
Key Takeaways
- LM Re and Celest.Science plan to combine alternative data sources with physical and statistical modelling.
- The proposed framework targets observation outages and reporting delays during tropical cyclones.
- The collaboration does not yet establish a validated method or a change to insurance contract terms.
Table of Contents
- Building a backup for cyclone observations
- Why parametric insurance needs dependable data
- Validation remains the key condition
- Conclusion
Building a backup for cyclone observations
The partners aim to reduce reliance on any single source of tropical cyclone data. Their proposed framework would combine independent secondary data streams with modelling to characterise an event even if primary observation inputs go offline.
LM Re brings reinsurance risk-structuring expertise, while Celest.Science contributes climate intelligence using artificial intelligence, climate physics and advanced modelling. The teams will evaluate whether the approach can strengthen operational continuity and support rapid, transparent claims processing.
Jean-Christophe Garaix, LM Re’s head of parametrics and agriculture, said the collaboration would explore an additional layer of resilience when a primary data source becomes unavailable. Celest.Science co-founder and CEO Léo Lemordant said the work would examine how climate science, physics-based modelling and AI could reconstruct events where observations are fragmented or incomplete.
| Item | Details |
|---|---|
| Risk in scope | Tropical cyclone risk transfer. |
| Disruptions targeted | Data outages and reporting delays during extreme weather. |
| Proposed technical approach | Independent secondary data combined with physical and statistical modelling. |
| Conditions for product use | Technical validation, governance approvals and relevant contract terms. |
Why parametric insurance needs dependable data
Unlike traditional insurance that pays by reference to assessed losses, parametric cover pays when an agreed event trigger is met. For tropical cyclones, that trigger can relate to a storm’s track, intensity or footprint. Missing observations can therefore complicate the process of establishing whether the contractual conditions have been satisfied.
LM Re’s 2019 explanation of parametric insurance describes the need for an agreed data source that is transparent, consistent and supplied by a trusted third party. That background explains the importance of data continuity; it does not validate the new collaboration or its proposed methodology.
The practical aim is to preserve confidence in event measurement when normal data channels are disrupted, rather than to replace agreed triggers with an assessment of each policyholder’s actual loss.
Validation remains the key condition
LM Re said any future use of the methodology in one of its products would require successful technical validation, applicable governance approvals and compliance with the relevant insurance or reinsurance contract.
Faster processing and greater resilience are therefore objectives of the research, not demonstrated outcomes. The announcement does not establish that the fallback framework is already available to clients.
Conclusion
The next step is technical evaluation of whether the fallback approach can reliably characterise cyclones during data disruptions. Any use in LM Re’s parametric insurance products remains conditional on validation, approvals and contract terms.
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