Hail detection and sizing for storm objects: A random forest framework using Swiss C-band radar and crowdsourced reports

Hail detection and sizing for storm objects: A random forest framework using Swiss C-band radar and crowdsourced reports | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 January 2026 V1 Latest version Share on Hail detection and sizing for storm objects: A random forest framework using Swiss C-band radar and crowdsourced reports Authors : Martin Aregger 0000-0001-6741-0707 [email protected] , Olivia Martius , Urs Germann , and Alessandro Hering Authors Info & Affiliations https://doi.org/10.22541/au.176980392.26711625/v1 216 views 96 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Hail poses a substantial economic threat in Switzerland, where both property and agricultural insurers report it as the leading source of storm-related losses in recent years. Accurate detection and sizing are critical for effective risk assessment and warning systems. This study presents a machine learning approach to predict hail presence and size at the ground for storm objects. We used Swiss C-band polarimetric radar data and a large, crowdsourced hail report database to train two random forest models on an extensive set of radar-derived predictors: a binary model for hail detection and a regression model for size estimation. Model development followed a rigorous pipeline based on repeated recursive predictor elimination to identify final models that balance performance, robustness, stability, and meteorological interpretability. The final hail detection model outperforms the operational POH baseline (Peirce Skill Score 0.91 vs. 0.81) and relies on a stable, physically coherent set of nine predictors. The final hail size regression model achieves better error statistics than the operational MESHS algorithm (RMSE 7.5 mm vs. 13.9 mm); however, it substantially underpredicts large hail (≥32 mm), a bias likely exacerbated by the dataset’s strong imbalance towards smaller hail sizes. Overall, this study demonstrates that a data-driven approach with a focus on model stability can improve hail detection and size estimation for storm objects compared to currently operationally used radar-based hail detection algorithms. Importantly, it delivers robust and physically inter pretable models rather than opaque ”black boxes,” making them promising candidates for further validation and application. Supplementary Material File (2025_aies_hail_preprint.pdf) Download 1.85 MB Information & Authors Information Version history V1 Version 1 30 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords convective storms crowdsourcing hail machine learning radar Authors Affiliations Martin Aregger 0000-0001-6741-0707 [email protected] Institute of Geography, University of Bern Oeschger Centre for Climate Change Research, University of Bern View all articles by this author Olivia Martius Institute of Geography, University of Bern Oeschger Centre for Climate Change Research, University of Bern View all articles by this author Urs Germann Federal Office of Meteorology and Climatology MeteoSwiss View all articles by this author Alessandro Hering Federal Office of Meteorology and Climatology MeteoSwiss View all articles by this author Metrics & Citations Metrics Article Usage 216 views 96 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Martin Aregger, Olivia Martius, Urs Germann, et al. Hail detection and sizing for storm objects: A random forest framework using Swiss C-band radar and crowdsourced reports. Authorea . 30 January 2026. DOI: https://doi.org/10.22541/au.176980392.26711625/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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