[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123146-en":3,"doc-seo-123146-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},123146,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving forecasts of precipitation extremes over northern and central Italy using machine learning","Accurate prediction of intense precipitation events is a core goal of operational weather services, made more urgent by global warming that intensifies extremes. While numerical weather prediction models provide uncertainty through dynamical ensembles, direct quantitative precipitation forecasting remains difficult due to precipitation’s intermittent, complex, and highly variable nature. This work presents a machine-learning hybrid postprocessing chain, MaLCoX, using a random-forest pipeline to identify favorable synoptic conditions and classify extreme types. Trained with high-resolution precipitation targets and ECMWF reforecast predictors, it improves medium-range probabilistic skill and offers a diagnostic physical storyline for forecasters.","Received: 29 June 2023 Revised: 3 April 2024 Accepted: 22 April 2024  \nDOI: 10.1002/qj.4755  \nRESEAR CH ARTICLE  \nImproving forecasts of precipitation extremes over northern and central Italy using machine learning  \nFederico Grazzini1,2  Joshua Dorrington3  Christian M. Grams3 George C. Craig1  Linus Magnusson4  Frederic Vitart4  \n1 Ludwig-Maximilians-Universität, Meteorologisches Institut, Munich, Germany  \n2Arpae-SIMC, Regione Emilia-Romagna, Bologna, Italy  \n3 Department Troposphere Research, Institute of Meteorology and Climate Research (IMKTRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany  \n4 ECMWF, Reading, UK  \nCorrespondence  \nFederico Grazzini, Meteorologisches Institut, Ludwig-Maximilians-Universität, München, 80333, Germany.  \nEmail: federico.grazzini@lmu.de  \nFunding information  \nDeutsche Forschungsgemeinschaft; Helmholtz Young Investigator Group‘Sub-Seasonal Predictability:  \nUnderstanding the Role of Diabatic Outflow (SPREADOUT)’, Grant/Award Number: VH-NG-1243  \nAbstract  \nThe accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine-learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user-oriented postprocessing. Here we describe a specific chain, based on a random-forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time-scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded high-resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium-Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger-scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.  \nKEYW O RDS  \nextreme precipitation, hybrid forecast model, large-scale precursors, machine learning, northern Italy, predictability, warning chain  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2024 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.  \n3168  \nGRAZZINI et al.  \n1  INTRODUCTION  \nItaly is among the European nations most exposed to torrential rainfall and flash flooding, due to its geographical conformation and climatological characteristics (Grazzini, 2021) . Every year these weather hazards produce huge costs and deadly consequences. The correct prediction of intense meteorological phenomena isone of the main objectives of operational weather services, and this task is even more relevant today with the rapid progression of global warming which amplifies extremes (Seneviratne et al., 2021; Tramblay & Somot, 2018) . Numerical weather prediction (N","cbCaic25c3XeecGQ","https://ap.wps.com/l/cbCaic25c3XeecGQ","pdf",13420585,1,15,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the MaLCoX system target in operational forecasting?\",\"answer\":\"It targets the challenge of directly predicting intense precipitation extremes by combining machine learning with physical-model outputs and event statistics in a hybrid forecasting workflow.\"},{\"question\":\"How does MaLCoX make its forecasts?\",\"answer\":\"It uses a random-forest (RF) pipeline to recognize favorable synoptic conditions and then classify precipitation extremes into predefined types.\"},{\"question\":\"Why is a hybrid approach described as beneficial for medium-range extreme precipitation forecasts?\",\"answer\":\"With a sufficiently long training period, blending larger-scale information with direct model output improves probabilistic forecast accuracy in the medium range.\"}]","Improving forecasts of precipitation extremes over northern and central Italy using machine learning | 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problem does the MaLCoX system target in operational forecasting?","Question",{"text":75,"@type":76},"It targets the challenge of directly predicting intense precipitation extremes by combining machine learning with physical-model outputs and event statistics in a hybrid forecasting workflow.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does MaLCoX make its forecasts?",{"text":80,"@type":76},"It uses a random-forest (RF) pipeline to recognize favorable synoptic conditions and then classify precipitation extremes into predefined types.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is a hybrid approach described as beneficial for medium-range extreme precipitation forecasts?",{"text":84,"@type":76},"With a sufficiently long training period, blending larger-scale information with direct model output improves probabilistic forecast accuracy in the medium 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