{"id":38532,"date":"2025-10-12T20:23:38","date_gmt":"2025-10-12T18:23:38","guid":{"rendered":"https:\/\/kinit.sk\/publication\/do-echo-top-heights-improve-deep-learning-rainfall-nowcasts-a-case-study-in-the-netherlands\/"},"modified":"2026-04-23T15:50:32","modified_gmt":"2026-04-23T13:50:32","slug":"do-echo-top-heights-improve-deep-learning-rainfall-nowcasts-a-case-study-in-the-netherlands","status":"publish","type":"publication","link":"https:\/\/kinit.sk\/sk\/publikacia\/do-echo-top-heights-improve-deep-learning-rainfall-nowcasts-a-case-study-in-the-netherlands\/","title":{"rendered":"Do Echo Top Heights Improve Deep Learning Rainfall Nowcasts? A Case Study in\u00a0the\u00a0Netherlands"},"content":{"rendered":"
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Pavl\u00edk, P., Schleiss, M.1<\/sup>, Bou Ezzedine, A., Rozinajov\u00e1, V.<\/strong><\/strong><\/p>\n<\/div>\n\n

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1<\/sup> Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands<\/em><\/p>\n<\/div>\n\n

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Precipitation nowcasting \u2013 the short-term prediction\u00a0of rainfall using recent radar observations \u2013 is critical\u00a0for weather-sensitive sectors such as transportation, agriculture,\u00a0and disaster mitigation. While recent deep learning models have\u00a0shown promise in improving nowcasting skill, most approaches rely solely on 2D radar reflectivity fields, discarding valuable vertical information available in the full 3D radar volume. In this work,\u00a0we explore the use of echo top height (ETH), a 2D projection indicating the maximum altitude of radar reflectivity above a given threshold, as an auxiliary input variable for deep learning-based nowcasting. We examine the relationship between ETH and radar reflectivity, confirming its relevance for predicting rainfall intensity.\u00a0We implement a single-pass 3D U-Net that processes both the\u00a0radar reflectivity and ETH as separate input channels. While our models are able to leverage ETH to improve skill at low rain-rate thresholds, results are inconsistent at higher intensities and\u00a0the models with ETH systematically underestimate precipitation intensity. Three case studies are used to illustrate how ETH\u00a0can help in some cases, but also confuse the models and increase\u00a0the error variance. Nonetheless, the study serves as a foundation\u00a0for critically assessing the potential contribution of additional variables to nowcasting performance.<\/p>\n<\/div>\n\n

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Cite: Pavl\u00edk, P., Schleiss, M., Bou Ezzedine, A., Rozinajov\u00e1, V., Do Echo Top Heights Improve Deep Learning Rainfall Nowcasts? A Case Study in\u00a0the\u00a0Netherlands. Transactions on Large-Scale Data- and Knowledge-Centered Systems LVIII. https:\/\/doi.org\/10.1007\/978-3-662-72116-2_3<\/a><\/p>\n<\/div>\n\n\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":true,"footnotes":""},"categories":[81,520],"class_list":["post-38532","publication","type-publication","status-publish","hentry","category-data-analytics-for-green-energy-sk","category-2025-sk"],"acf":[],"yoast_head":"\nDo Echo Top Heights Improve Deep Learning Rainfall Nowcasts? 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