Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland

Abstract

Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland's National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail on the ground (POH) to assess the presence of hail and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (Density-Based Spatial Clustering of Applications with Noise, ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the hit rate, false-alarm ratio (FAR), critical success index (CSI), and Heidke skill score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained using a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained using a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports.