Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground

Lainer, Martin; Brennan, Killian P.; Hering, Alessandro M.; Kopp, Jérôme; Monhart, Samuel; Portmann, Jannis; Wolfensberger, Daniel; Germann, Urs (1 March 2024). Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground (Unpublished). In: 4th European Hail Workshop. Karslruhe. 05-07.03.2024.

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Hail is a major threat associated with severe thunderstorms and estimating the hail size is important for issuing warnings to the public. For the validation of existing, operational, radarderived hail estimates, ground-based observations are necessary. Automatic hail sensors, as for example within the Swiss hail network, record the kinetic energy of hailstones to estimate the hail sizes. Due to the small size of the observational area of these sensors (0.2m2), the full hail size distribution (HSD) cannot be retrieved. To address this issue, we apply a state-of-the-art custom trained deep-learning object detection model to drone-based aerial photogrammetric data to identify hailstones and estimate the HSD. We present the results of a single hail event on 20June2021. Thesurvey area suitable for hail detection within the created 2D orthomosaic model is 750m2. The final HSD, composed of 18’209 hailstones, is compared with nearby automatic hail sensor observations, the operational weather radar based hail product MESHS (Maximum Expected Severe Hail Size) and crowdsourced hail reports. Based on the retrieved data set, a statistical assessment of sampling errors of hail sensors is carried out and five repetitions of the drone-based photogrammetry mission within 18.65min after the hail fall give the opportunity to investigate the hail melting process on the ground. Finally, we give an outlook to future plans and possible improvements of drone-based hail photogrammetry.

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