Hailstorms pose a significant risk to exposed assets, necessitating accurate estimates for insurance pricing and risk mitigation.
Severe thunderstorms with intense updraft cores create an ideal setting for the formation of large hail. These storms draw moisture from the lower troposphere and carry it to altitudes with sub-freezing temperatures, supporting ice particle formation and hail growth.
The impact of large hail can be highly destructive and costly, contributing significantly to natural disaster losses worldwide.
In regions like South Africa, hail has a long history of causing extensive damage to various exposed assets, including buildings, crops, infrastructure, and vehicles. One of the most extensively documented and devastating hailstorms took place on November 28, 2013, in Gauteng province. The reinsurance industry estimated losses of ZAR 1.4 billion1 as a result of this event. Consequently, it is crucial to comprehend when, where, and under which conditions such incidents might transpire for insurance pricing and implementing preventive measures.
Challenges in Hail Hazard Quantification
The sporadic occurrence and highly localized effects of hail make it challenging to quantify the hazard accurately, posing a unique obstacle to risk modeling. Reliable records of hailfall, including size information, are often limited to volunteer observer networks, databases, and hailpad networks, which have inherent limitations and may not provide comprehensive data for risk assessment.
Hail formation is not currently well-represented in numerical weather prediction or climate models due to the significant computational costs involved. Besides, our understanding of the involved microphysical and dynamical processes is still incomplete.
While remote sensing data from radar, along with convection-related parameters from numerical models, show promise in determining hailstorm spatial extent and better understanding of related atmospheric conditions, they also come with limitations, such as limited radar coverage, limited duration of reliable historical radar data, signal attenuation, and simplified model microphysics.
The Role of Geostationary Data
Geostationary satellites offer a broader coverage area compared to radar and provide more homogeneous data, making them valuable tools for the analysis of hailstorms. While they may be less accurate in hailstorm detection than polar-orbiting satellites with microwave-wavelength sensors2, they excel in identifying severe convective storm clusters at high temporal resolution (e.g., every 15 minutes) through the detection of overshooting cloud tops. Overshooting cloud tops are transient intrusions of tropospheric air into the lower stratosphere, and therefore serve as indicators or proxies of intense updrafts within a thunderstorm cloud that can generate hail. Geostationary satellites utilize infrared cloud-top temperature data to identify these features, thereby inferring the potential for hail formation. Infrared temperature is also used to estimate storm severity and potential hail size, based on recent research that shows that the largest hailstones are most often generated by the coldest overshooting tops3.
The availability of such data enables not only the global analysis of hail occurrence but also the identification of prevailing atmospheric conditions during individual hailstorms. By examining the characteristics of hailstorms on a global scale, researchers can gain insights into the geographical distribution, frequency, and intensity of hail events.
As discussed in a recent WTW Research Network's insight piece on Earth Observation and Machine Learning4, the use of satellite imagery has the advantage of being able to cover large areas as well as provide ever-increasing resolution on local scales, and allows for observation even in remote locations.
In addition, high-performance computing, AI, and machine learning techniques give the ability to process and identify features from large quantities of satellite images without the need for significant resources. However, unlike with geohazard assessment, ground-based observation of phenomena such as overshooting cloud tops is extremely limited, so advances in remote Earth Observation continues to have profound impacts on the ability to accurately monitor, interpret, and assess hazard and risk from hailstorms.
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Co-authored by Kristopher M. Bedka, NASA Langley Research Center and Michael Kunz, Karlsruhe Institute of Technology
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