[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119480-en":3,"doc-seo-119480-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119480,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Probabilistic rainfall nowcasting with Machine Learning models - PhD Thesis","Nowcasting models use real-time measurements to forecast rainfall within a very short lead time, typically minutes to six hours, supporting decisions across hydrological, agricultural, and economic domains. They enable safer transportation operations, improve flight guidance, and reduce human and environmental impacts through rainfall alerts. Short-term prediction remains difficult because meteorological variables interact strongly and evolve rapidly during events, while computational cost and limited resolution restrict operational use. This PhD thesis develops a probabilistic machine-learning framework for reliable, fast rainfall nowcasting, training multiple feed-forward neural networks on hundreds of events and validating performance against continuous and categorical reliability indicators, plus benchmark methods.","Probabilistic rainfall nowcasting with Machine Learning models  \nDina Pirone  \nDipartimento di Ingegneria Civile, Edile e Ambientale Università degli Studi di Napoli Federico II  \nThesis submitted for the degree of PhD in Civil Systems Engineering  \nNapoli, March 2023  \nSupervisors  \nProf. Giuseppe Del Giudice, Università degli Studi di Napoli Federico II, Prof. Domenico Pianese, Università degli Studi di Napoli Federico II  \nCo-Supervisors  \nProf. Patrick Willems, Katholieke Universiteit Leuven, Belgium Prof. Luigi Cimorelli, Università degli Studi di Napoli Federico II  \nPhD Programme Coordinator-XXXV Cycle  \nProf. Andrea Papola, Università degli Studi di Napoli Federico II  \nThe Reading Committee  \nProf. Claudia Teutschbein, Uppsala Universitet, Sweden  \nProf. Maurizio Mazzoleni, Vrije Universiteit Amsterdam, Netherlands  \nCopyright © 2023 by Dina Pirone  \nAll rights reserved. No part of this material may be copied or reproduced in any way without the author's prior permission.  \nAbstract  \nNowcasting models use real-time data to predict rainfall with short lead times-from a few minutes up to six hours. They influence many aspects of daily life in hydrological, agricultural, and economic sectors. For example, they facilitate drivers by predicting road conditions, enhance flight safety by providing weather guidance, and prevent casualties by issuing rainfall alerts which can affect human life and cause environmental issues. However, short-term prediction is challenging because meteorological variables are strongly interconnected and rapidly change during events. In addition, the long computational times and low spatial and temporal resolution of nowcasting models do not often suit the short-term prediction requirements. This thesis focuses on developing an approach for probabilistic rainfall nowcasting with machine learning. Since machine learning does not require any previous physical assumption, this research investigates their ability to provide reliable and quick forecasts. A machine learning model for probabilistic rainfall nowcasting for short lead times-from a few minutes up to 6 hours-is proposed. The model employs cumulative rainfall fields from station data as inputs for feed-forward neural networks to predict rainfall intervals and the corresponding probabilities of occurrence. Using cumulative rainfall depths from station data overcomes the lack of temporal memory of the feed-forward neural networks. In this way, using only the current rain field as input, the model exploits pattern recognition techniques combining temporal-cumulative rainfall depth-and spatial-cumulative rainfall field – information. Several feed-forward neural networks were independently trained and tested on almost 360 rainfall events over the study area – one of the eight warning zones of the Campania Region. First, comprehensive nowcasts verifications were performed to analyze probabilistic nowcasts' reliability using continuous and categorical indicators. The performance of the models was also compared with the results of two different benchmarks: Eulerian Persistence and Pysteps. Then, to assess  \nthe extendibility of the procedure to other regions, the model was applied to another study area that differed from southern Italy one: the Flanders Region of Belgium. Results showed that using temporal and spatial information enables the model to predict short-term rainfall using only the current measurements as input, resulting ina rapid, easily replicable, and convenient nowcasting approach. Therefore, the procedure effectively predicts multi-step rainfall fields and is suitable for operational early warning systems.  \nKeywords: Precipitation nowcasting; Multi-step predictions; Rain-gauge measurements; Pattern recognition; Feed-forward neural networks; Cumulative rainfall fields.  \nAbstract (Ita)  \nI modelli di nowcasting forniscono previsioni meteorologiche a breve termine, da pochi minuti fino a un massimo di sei ore. Essi influenzano molti ","cbCaiqGwY7YwLz15","https://ap.wps.com/l/cbCaiqGwY7YwLz15","pdf",4102846,1,122,"English","en",105,"# Abstract\n## Method and model design\n## Training and validation\n## Benchmarks and transferability","[{\"question\":\"What timeframe does the nowcasting model target?\",\"answer\":\"The approach is designed for short lead times, from a few minutes up to six hours.\"},{\"question\":\"How does the model use station data to make probabilistic forecasts?\",\"answer\":\"It uses cumulative rainfall fields derived from station measurements as inputs to feed-forward neural networks, predicting rainfall intervals and the probabilities of occurrence.\"},{\"question\":\"How is the model evaluated and compared with other approaches?\",\"answer\":\"The thesis performs comprehensive probabilistic nowcast verification using continuous and categorical indicators, and compares results with Eulerian Persistence and Pysteps benchmarks. It also tests extendibility by applying the model to a different region.\"}]","Probabilistic rainfall nowcasting with Machine Learning models - PhD Thesis | PDF",1785724535,307,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"probabilistic-rainfall-nowcasting-with-machine-learning-models-phd-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/probabilistic-rainfall-nowcasting-with-machine-learning-models-phd-thesis/119480/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What timeframe does the nowcasting model target?","Question",{"text":76,"@type":77},"The approach is designed for short lead times, from a few minutes up to six hours.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the model use station data to make probabilistic forecasts?",{"text":81,"@type":77},"It uses cumulative rainfall fields derived from station measurements as inputs to feed-forward neural networks, predicting rainfall intervals and the probabilities of occurrence.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the model evaluated and compared with other approaches?",{"text":85,"@type":77},"The thesis performs comprehensive probabilistic nowcast verification using continuous and categorical indicators, and compares results with Eulerian Persistence and Pysteps benchmarks. 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