Convective Storm Modeling: The Difference is in the Details

You’re likely already familiar with catastrophe (CAT) modeling, a method of determining risk exposure in hurricane- or earthquake-prone regions. But look out your window right now. Do you spy dark gray rainclouds on the horizon? If so, you might just be looking at a convective storm, and this common weather pattern is changing the face of today’s insurance and risk management industry models. With convective storm modeling added to your risk assessment tools, you create a multi-tiered approach to ensure accurate coverage at the best rate.

What is a convective storm?

According to NOAA’s National Severe Stores Laboratory (NSSL), convective storms are storms that are formed when surface heat causes moisture and other particles to rise into the atmosphere. Basically, they’re thunderstorms. And this regular weather phenomenon is so regular, it happens up to 100,000 times a year in the U.S. alone, with about ten percent of the storms reaching a level considered “severe.” To be considered a severe convective storm, it must contain one or more of the following:

  • Hail one inch or greater
  • Winds gusting in excess of 50 knots (57.5 mph)
  • A tornado1

In the insurance and risk management industries, hail, straight-line winds and tornadoes are “sub-perils” of convective storms— dangerous off-shoots of the weather system that can rack up serious property damage costs in a short period of time. In fact, in 2018 alone, the U.S. insurance industry experienced $18.8 billion in losses due to severe convective storms.2

Clearly, overlooking this unique and important set of variables in risk calculations not only significantly reduces corporate profitability but fails to capture the key datapoints needed for more holistic and accurate property assessments. That’s where convective storm modeling comes in.

What is convective storm modeling?

Convective storm modeling calculates and simulates the ways severe thunderstorms and associated sub-perils create property damage based on specific criteria. While the exact modeling algorithms may vary between solution vendors, models generally leverage a mix of historical data, radar information, impact and windspeed calculations, geographic data, and frequency statistics to help model the most likely storm scenarios over the broadest regions.

With convective storm modeling, sub-perils are calculated based on their individual risk factors. For example, hail damage might be modeled on the size and weight of a hailstone, its potential fall speed, the horizontal wind speed during the storm, as well as the type of building surface it impacts. This allows modelers to better predict the different levels of damage that a certain size hail stone will create when striking different materials, such as the glass of a large unsheltered window, aluminum siding, or clay roof tiles. With each sub-peril comes different risks and different potential damage scenarios.

This severe thunderstorm activity is then simulated into 10,000 year, 50,000 year and 100,000 year stochastic catalogs to assist with underwriting and rate generation. Stochastic here means “random,” as in involving a random chance or probability. So a “stochastic catalog” shows the probability of certain severe thunderstorm activity happening over a set number of years. With literally tens of thousands of years of simulation data in hand, insurers and underwriters can feel pretty confident about the risk possibilities.

How Convective Storm Modeling and COPE Data Work Together

The important thing to keep in mind about convective storm modeling is, in order to accurately simulate severe convective storm damage and gain a better view into potential property risk, you must first have accurate building data to use in your model.

This data is called COPE data. COPE is an acronym that stands for Construction, Occupancy, Protection and Exposure. There are two kinds of COPE data to collect: primary and secondary COPE data. Primary COPE data includes information on a property’s square footage, the construction material used, available fire protection, and location. Secondary COPE data drills down even further, detailing building structure and how it can interact with adverse weather conditions, including sub-perils like straight-line winds, tornado activity and hail. Examples of secondary COPE characteristics for a convective storm might include a window’s level of wind-resistance, or types of roof framing and attachment methods and how they hold up to high winds.

When little to no COPE data is provided in these areas, insurance underwriters and risk managers have little alternative but to assign risk at the highest level. This is done because, while the unknown property might be very sub-peril-resistant, it might also be of a type that’s easily damaged and, thus, a high risk. With no way of knowing which it could be, underwriters and risk managers must always err on the side of caution. And caution may mean higher insurance rates.

Thorough COPE data, on the other hand, means that convective storm modeling can predict risk with a much greater accuracy. And the more accurately the level of risk can be estimated, the greater opportunity there is for potential cost savings and the ability to take action against potential hazards before they happen. For example, you might decide to replace high-risk materials for more durable ones on a particular building, or you might double-check that your sprinkler system inspections are up-to-date. So when that wind comes through, or that fire starts, you’ve already taken the precautions that improve risk outcomes. That’s where COPE data can be invaluable.

How to Get the COPE Data You Need

If you’re looking to improve your risk profile (and secure the best insurance rates!), gathering and organizing the detailed COPE data you need might seem like a monumental task. But it doesn’t have to be.

There are software applications available, specifically designed to help organizations manage large amounts of property data with both granular detail and consistency. Applications like these allow you the flexibility to add fresh data details over time, or to make batch updates as a part of a more sweeping COPE data initiative.

And if the act of gathering the data itself poses the challenge, support is available here, too. A quality professional appraisal firm understands how accurate COPE data improves risk profiles and should be able to collect the information you need, quickly and efficiently.

At AssetWorks, for example, we unite risk management software with expert appraisal services, preparing organizations with the valuable COPE data that can make a difference when severe convective storms blanket the skies. Contact us to learn more about how we can help you reduce property risk and improve your risk profile.


  1. National Severe Storms Laboratory at NOAA, “Severe Weather 101: Thunderstorms.”
  2. Facts and Statistics about Tornadoes and Thunderstorms. 2019 Munich Re, NatCatSERVICE; Property Claim Services (PCS®)*, a Verisk Analytics® business. As of March 2019.

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