Renew Risk Launches Severe Storm Model for US Market

Why It Matters

High‑frequency convective storms now account for over half of U.S. natural catastrophe losses, making precise solar risk modeling essential for underwriting profitability. The model gives insurers a competitive edge in pricing and capital allocation as solar capacity expands into catastrophe‑prone areas.

Key Takeaways

• Renew Risk's model targets severe convective storms for U.S. solar farms
• Storm risk now drives 51% of U.S. natural catastrophe losses
• Machine‑learning integrates hail, tornado, and straight‑line wind impacts
• Asset‑first approach includes glass thickness, stow angle, system reliability
• Monthly Industry Exposure Database updates improve insured value calculations

Pulse Analysis

The United States is witnessing a rapid expansion of utility‑scale solar installations, many of which sit in regions historically prone to severe convective storms. While insurers have traditionally focused on low‑frequency, high‑severity perils such as hurricanes and earthquakes, data from 2025 show that these storms now account for 51 % of natural catastrophe losses, amounting to roughly $46 billion. This shift forces underwriters to reassess their risk models, as conventional catastrophe tools often overlook the localized, high‑frequency nature of hail, tornadoes and straight‑line wind that threaten solar farms.

Renew Risk’s new U.S. severe convective storm model tackles this gap with an asset‑first methodology that evaluates each solar farm at the component level. By feeding a monthly‑updated Industry Exposure Database into a physics‑AI engine built with Vāyuh, the model generates granular loss estimates for glass thickness, panel stow angle and system reliability. A machine‑learning layer calibrates storm‑scale dynamics where historical claims are sparse, while simultaneously modeling hail, tornadoes and straight‑line wind as interrelated hazards. The result is a more precise insured value and business interruption forecast for insurers and reinsurers.

For insurers and brokers, the model translates into sharper pricing and more resilient portfolio construction. By quantifying localized storm exposure, underwriters can set premiums that reflect true risk, allocate capital more efficiently, and meet regulatory capital requirements. The granular insights also enable reinsurers to design bespoke treaty structures, reducing basis risk for cedants. As solar continues to dominate new capacity additions, tools like Renew Risk’s model are likely to become industry standards, prompting broader adoption of AI‑driven, asset‑centric catastrophe modeling across renewable energy lines.