S101 Method for Automated Hail Pad Processing

Sunday, 6 January 2013
Exhibit Hall 3 (Austin Convention Center)
Peter J. Marinescu, Colorado State University, Fort Collins, CO; and B. Windschitl

Handout (330.7 kB)

This study will focus on a method to automate hail pad analysis using image processing programming. Hail pads are Styrofoam ® blocks that are used to measure the characteristics of hail that strikes and leaves dents in the pad. Community Collaborative Rain, Hail & Snow Network (CoCoRaHS), an extensive volunteer network of weather observers in the United States and Canada, has increased the number of hail pads used and recorded. Furthermore, CoCoRaHS generates hail reports regardless of the size of hail. Most standard hail reports from other organizations solely focus on severe hail, which the National Weather Service defines as greater than one inch (2.54 centimeters) in diameter. CoCoRaHS provides an additional data set that includes both severe and non-severe hail observations.

Currently, hail pads are processed by a CoCoRaHS staff member. Markings considered to be caused by hail are categorized by size (small, medium, large or jumbo). The counts from each size category for sample regions are averaged and scaled up to the size of the hail pad to provide an estimate of the total number of small, medium, large and jumbo hail stones that impacted the hail pad. This process takes into account numerous assumptions, including that the sample regions are accurate representations of the entire hail pad and that human judgment can objectively determine hail markings from one pad to the next.

Various research articles have attempted to calibrate hail pad data and have created adjustments for some of the errors in hail pad processing. However, limited efforts have been undertaken to automate the current subjective and manual task of processing hail pads, especially with respect to the CoCoRaHS hail pad network. In this study, an algorithm was developed using MATLAB ® that aims to detect circular hail markings on hail pads images. Simple photography techniques using multiple illumination sources and angles were initially used to determine whether photography can improve the algorithm's results. The algorithm counts hail markings and categorizes these markings into size groupings similar to the CoCoRaHS processing method to compare the accuracy of the algorithm. Initial results using four hail pads with varying hail characteristics provide promise in the development of an automated algorithm that can objectively and accurately detect, count and measure hail dents from hail pads images. From these four pads, there was no clear benefit from using a specific photography technique. Therefore, sunlit images were used for algorithm development, with the hopes that volunteers from across the widespread CoCoRaHS network can submit photographs of hail pads. The results using these four initial hail pads demonstrate the algorithm's high accuracy in detecting small and medium size dents, which are associated with markings less than 0.75 inch (1.9 centimeter) in diameter. However, the algorithm displayed less accurate results for large and jumbo size dents. Further testing of the algorithm on additional hail pads in the upcoming months will lead to more information on how to improve the algorithm's results.

The benefit of such an algorithm is two-fold. First, automated hail pad processing will provide CoCoRaHS with an efficient and objective method of analyzing hail pads, which with the growth of hail pad use will be important for CoCoRaHS operations. Second, the capability to create accurate hail size distributions with high frequency over expansive regions will benefit the scientific and operational meteorological community in the form of a new set of observational data regarding hailfall characteristics. A few examples of the uses of this data may include better understanding hail formation in convective thunderstorms, creating hail climatological studies and improving hydrometeor classification algorithms that may aid hail forecasting efforts.

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