How it works
1
Generate climate-connected events
DeepCyc recreates millions of climate-conditioned storm scenarios based on observed and projected environmental data.
2
Integrate with existing workflows
Integrate cat model output with the Climate-Based Risk Adjustment (CBRA) tool or compare to building design wind speed standards.
3
Generate climate-connected risk information
Use DeepCyc data to update return-period maps, assess evolving seasonal risk, stress-test portfolios, or quantify how climate variability may affect future losses and resilience planning.
Explore DeepCyc's features
What makes DeepCyc different
AI-driven probabilistic hazard simulation
DeepCyc uses a combination of ERA5 reanalysis data and NCAR-CESM ensemble members to simulate event characteristics beyond the limits of the historical record.
This allows DeepCyc to produce consistent stochastic catalogues of up to 100,000 years, providing insurers, reinsurers, and risk modellers with a robust view of hazard frequency and intensity, even in regions with limited observation data.
By learning from global teleconnection patterns, DeepCyc’s synthetic events remain sensitive to shifts in major climate drivers such as ENSO, AMO, or the Indian Ocean Dipole.
Terrain-corrected wind field model
To replicate the physical structure of storms, DeepCyc incorporates high-resolution boundary layer physics from Reask’s InCyc 1 km gust simulations.
The model estimates local 3-second gusts over actual terrain, correcting for roughness and topographic effects both at site and up to 3 km upwind, ensuring physically realistic wind fields across diverse landscapes.
DeepCyc product options
DeepCyc Maps
Return period hazard maps providing probabilistic wind risk metrics with global coverage.
DeepCyc Tracks
Stochastic tropical cyclone tracks and event characteristics for portfolio-level analysis.
DeepCyc Events
Gridded wind speed footprints for high-resolution risk quantification and scenario testing.
Access to our data is fast and frictionless
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