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KCC releases US winter storm reference model

13th January 2021 - Author: Charlie Wood

Catastrophe risk modeller Karen Clark & Company has announced the release of its new US Winter Storm Reference Model.

The model leverages a physics-based methodology to capture winter storm loss potential from snow and ice, freezing temperatures, and high winds.

“Winter storms are challenging to model, both due to their meteorological complexity and the different perils possible within each storm,” said Glen Daraskevich, KCC Senior Vice President.

“KCC implemented a multi-peril approach when developing the Winter Storm Reference Model that includes three sub-perils: wind, freeze, and snow and ice accumulation.

“On average winter storms cause nearly $3 billion in insured loss annually and can cause over $20 billion in extreme events, so it’s critical to capture the impacts accurately.”

Liberty Mutual Reinsurance

The KCC US Winter Storm Reference Model is implemented on the RiskInsight® open loss modeling platform, and all components are transparent to model users.

In addition to winter storm, KCC has developed catastrophe reference models for hurricanes, earthquakes, and severe convective storms in over 20 countries.

“The model incorporates different classifications of winter storms, not just your typical Nor’easters, but also Arctic high-pressure systems because freezing is known to be incredibly damaging,” said Dr. Sara Sienkiewicz, KCC Senior Meteorologist.

“Each meteorological system has unique storm characteristics with key differences in the location and intensity of expected perils. To model winter storms accurately, KCC scientists combined advanced numerical weather prediction (NWP) data and techniques with historical winter storm climatology.”

“The benefit of using NWP is that we can capture the specific atmospheric conditions that lead to each sub-peril,” said Dr. Daniel Ward, KCC Senior Meteorologist.

“We’ve reproduced 35 historical events and generated over 19,000 additional events with NWP techniques to create a stochastic event set that represents potential future winter storms that are meteorologically feasible but have not yet occurred. For each event, three high resolution footprints are generated: one for snow and ice accumulation, one for wind, and one for freeze.”

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