Superior Data In. Superior Results Out.
Thunderstorms are some of the most dynamic phenomenon on earth, and result in a variety of hail sizes from the size of a pea, to larger than softballs. In addition to the inconsistency in size, the hail fall often varies over short geographic distances. Every year, hail damage is a significant contributor of insurance claims and losses in the Continental United States. Furthermore, hail damage can go undetected for months, causing considerable collateral damage which can result in very long claim tails, ultimately having a significant impact on customer satisfaction.
Ground reports and weather radar observations are both current methods used to estimate hail fall, yet both traditional approaches still have substantial limitations when it comes to forensic verification. Public reports often yield unreliable locations and safety concerns prevent the immediate assessment of hailstones, resulting in varied amounts of melting prior to the measurement taking place. In addition, as there is no one person measuring all the stones, inconsistencies occur through measurements performed by people with a diverse education on how to appropriately measure. In combination, they can cause the reported hailstone size to be inconsistent from observer to observer, even within a few miles from each other.
To get around the limitations of public reports, weather radars operated by the National Weather Service (NWS) have been used to estimate the hail-size footprint. However, the energy returned to the radar, or reflectivity, depends on the size and number of rain drops and hailstones. This means that a few large hailstones could look like many small hailstones or even moderate rainfall, causing a misrepresentation of what happened. The estimate is further complicated when rain and hail exist in the same area, which is a common occurrence. A few of the methods that algorithms use to avoid these limitations, are utilizing data above the freezing level in addition to a new dual polarization technology. Unfortunately, even with these methods, weather radar data by itself is insufficient to relate what the radar sees in the storm, to the hailstones impacting property on the ground.
By combining radar data, public and social media reports, and the expertise of experienced meteorologists, each approach can be taken advantage of for its positive aspects. To achieve property level accuracy, preserving the granularity of radar data is critical, and can be accomplished via sophisticated, physics-based morphing algorithms that continuously work to fill in the reflectivity data gaps every few seconds. This comprehensive approach consistently provides 500-meter resolution with neighborhood-level detail. Recent upgrades to the NWS radar network has tripled the amount of information that can be leveraged to further understand what the radar is seeing in the storm. The new data allows for better differentiation between regions of heavy rain and hail, increasing the confidence and the granularity of the hail footprint. This approach also takes better advantage of the expert meteorologists that examine the available ground observations.
The ability to understand the specific impact of each storm can be challenging for carriers who are looking to enable a more proactive approach rather than a reactive storm response. Thus, forensic hail verification technologies exist so that carriers can compare detailed storm maps and reports with their books of business. The ability to perform this analysis leads to a better understanding of the impact of each unique storm. With this new ability to accurately verify and pinpoint affected areas, more targeted response plans can be developed to improve customer satisfaction, catastrophe response efficiency, and confidently detect fraudulent claims.
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