1) Storm measurements and you
Earlier this year the National Weather Service’s Storm Prediction Center in Oklahoma shared a new means of indicating storm intensity along with the storm likeliness data that was already part of their regular output.
The new presentation offers improved visibility into not just the places where storms are likely to strike, but also the power of the storm.
Does an improved prediction model actually allow for greater safety and prediction of risk levels? New sources of data are still in the evaluation phase for many insurers, but results from early adopters point toward improvements in disaster preparedness practices to come for many homeowners.
2) Is there anything new in the world of weather?
What has changed in the space of weather forecasting recently? The answer may impact your everyday life more than you’d expect. With the use of new Conditional Intensity Groups by the national Storm Prediction Center in Oklahoma, forecasts now offer a more complete picture of approaching weather. Forecasts of this type use a system of notation that defines the likely intensity of a storm, not just the likelihood of it happening in a specific area.
This additional layer of prediction detail is presented along with traditional likelihood notations. These additional groups are presented as new numerical scales to score each major peril: wind, hail, and tornado. The measurements capture wind speed and size of hailstones, as appropriate for each type of storm.
Weather Peril | Intensity Scale per Peril |
Wind storm | Sub-75 m.p.h. – 95+ m.p.h.+ (derecho-level winds) |
Hail storm | Sub-2” hailstones up to 3.5”+ (softball-sized) |
Tornado | No level (equal to EF-0) up to Level 3 (equal to EF-4+) |
Historically an area predicted to see tornado activity was given no warning if the approaching storm was more likely an EF-1 or an EF-5, a significantly more deadly storm. That unknown detail is provided by this new level of reporting. This enhanced notation also gives better visibility into the size of hailstones sighted in an area, as there is likely be a different level of reaction to 3” hail than there would be to something the size of a ball bearing, or smaller.
This notation method is part of an early-warning system of prediction, with the intent of allowing people in the threatened area the maximum amount of time to prepare their homes or leave to seek shelter. The forecasted storm intensities are not intended to be used as part of historical storm records for risk-assessment purposes.
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3) How does storm data help me?
Storm forecasting provides a look forward to define the events that are most likely to affect a home in that area. Because weather events are rarely specific to a single house or address, this type of prediction is based on general areas or regions.
Data collected in this way plays an important role, but has its weaknesses. Being human-collected and noted, the observations can be inconsistent and biased toward more populous areas (Walker, 2026). The long-run aggregation of the data may obscure hyper-local or outlier events when viewed as part of a whole. This type of observation also excludes any structure-specific details that could provide additional context.
Any improvements made to weather forecasting methods should consider any potential weaknesses of the source data unless practices have already changed to accommodate them. The improved methods could still prove beneficial.
4) Risk mapping vs. weather forecasting
Risk mapping looks back at events that have occurred in that location in the past, takes into consideration each instance of a certain type, and uses that data to assign a risk score to the home or location for future reference.
With a recorded history of the frequency of weather events around a region, a risk catalog can be created, serving as both a warning to residents of the types of weather events they might experience in the area, and as a key part of the shared knowledge that drives insurance prices, home values, and other aspects of the local economy.
Each use for weather data collected in a specific area can be combined for the greater good, benefitting anyone who lives in an area that has been studied.
5) How is risk mapping used?
Today, risk mapping is a key part of every decision made about buying or selling across the country. The observations that inform these decisions are collected in a variety of ways but the most common, a property-level approach that provides relevant information to both property owners and insurance providers, is the main tool of the risk analyst.
This approach combines on-site forensic observation of weather patterns with satellite imagery to give a complete picture of the weather on a per-parcel basis, allowing for the most accurate record-keeping and forecasting possible.
The practices that facilitate this type of data collection and analysis include noting historically important signs of home roof damage, such as the condition of the roof structure, visible damage to the shingles, and areas missing shingles.
To get the best understanding of modern risk assessment, consider its components.
The Three Components of Risk:
Hazard: includes the weather conditions or storm types that can or have impacted the area.
Vulnerability: includes locational factors specific to an address. This could include the materials used in construction, the placement of trees and other landscaping, or the age and wear of the roof.
Claims History: is the historical element that completes the ‘big picture’ of a location’s risk level. This includes a record of claims made on insurance for a property or structure in a specific location.
With these elements combined it is possible to create a clear image of the likelihood of weather effects at a specific location, but getting the data together is the challenge in many cases. The reality is that the data is collected and then owned by a variety of organizations, both governmental and third-party, across the U.S. and beyond. This fragmentation of data sources is one of the most significant obstacles to the creation of a nationwide network of weather risk data.
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6) How both sciences benefit homeowners
Risk data allows insurers to underwrite policies more accurately. Underwriting involves considering the factors that endanger or strengthen homes to provide fair pricing across a range of clients and policies. Homes with hail resistant roofs or other damage-reducing improvements made can be recognized and credited for these strengths or weaknesses and priced accordingly.
More accurate data allows for faster, surer claims processing. When data indicates with clarity that damaging hail impacted a property, the claims process can be accelerated.
Also important for both insurers and homeowners, improved risk data fills out the after-incident portion of the risk timeline, with storm forecast data populating the pre-storm part of the cycle.
7) What’s left to learn from the weather?
The use of Conditional Intensity Groups by weather tracking entities, both commercial and governmental, is still a new practice that is gaining users and support with each new storm predicted. Insurance providers may follow their traditional pace and update their practices and methods after a considerable delay to allow for complete internal alignment and adoption.
Considering this gradual pace of adoption for CIGs, there isn’t yet sufficient data to illustrate with certainty whether or not this improvement to forecasting will result in significantly improved risk models that use the data in the years to come.
As new sources of risk data are refined and structured for use as part of the risk assessment process by established insurers, it is likely that the result will be improved risk models across the industry, meaning fairer pricing and less confusion for the homeowner whose top priority is simply the safety of their home and family.
Last Updated: August 05, 2026
Sources:
Atienza, Julian. “NOAA's Storm Prediction Center debuts revamp to severe weather outlooks this season” FOX Weather, Mar. 03, 2026, https://www.foxweather.com/learn/noaa-storm-prediction-center-debuts-new-conditional-intensity-groups
“Assessing Hail Risk: Why Insurers Need More Than a Storm Report” Moody’s, Mar. 07, 2024, https://capeanalytics.com/resources/hail-risk-insurers-storm-report/
“Understanding Hail Risk: How Data & Science Transform Insurance and Recovery for Businesses” Descartes Insights, https://descartesunderwriting.com/insights/understanding-hail-risk
Sedlar, Matt. “Climate Change and the Future of Catastrophe Insurance Programs” Center for Economic and Policy Research, Oct.17, 2023, https://cepr.net/publications/climate-change-and-the-future-of-catastrophe-insurance-programs/
Walker, Megan. “Population Bias in NOAA Storm Events Database Reports of Severe Wind and Hail: Implications for Risk and Impacts Assessment in Rural Areas” American Meteorological Society, Jan. 26, 2026, https://ams.confex.com/ams/106ANNUAL/meetingapp.cgi/Paper/468156
Gray is the Senior Content Specialist at Sola Insurance, working with the Sales and Marketing teams to provide helpful, valuable content for homeowners and agents. Gray has worked previously in finance, logistics, and advertising.
