{"id":4747,"date":"2021-09-24T03:31:20","date_gmt":"2021-09-24T03:31:20","guid":{"rendered":"https:\/\/www.ifabfoundation.org\/?p=4747"},"modified":"2021-11-10T16:29:30","modified_gmt":"2021-11-10T16:29:30","slug":"artificial-intelligence-and-machine-learning-to-improve-surface-weather-forecasts-ifab-and-ecmwf-webinar-9-november-2021","status":"publish","type":"post","link":"https:\/\/www.ifabfoundation.org\/en\/2021\/09\/24\/artificial-intelligence-and-machine-learning-to-improve-surface-weather-forecasts-ifab-and-ecmwf-webinar-9-november-2021\/","title":{"rendered":"\u2018Artificial Intelligence and Machine Learning to improve surface weather forecasts&#8217; &#8211; webinar &#8211; November 9th, 2021"},"content":{"rendered":"<section class=\"l-section wpb_row height_medium\"><div class=\"l-section-h i-cf\"><div class=\"g-cols vc_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><h6>On <strong>November 9th<\/strong> from<strong> 3.30 pm <\/strong>to 5 pm (CET), IFAB, in collaboration with ECMWF, will present the research project \u201cEmpirical modeling of 2m temperature and 10m wind\u201d.<\/h6>\n<h4>PROGRAM:<strong><br \/>\n<\/strong><\/h4>\n<p><strong><em>INSTITUTIONAL GREETINGS<br \/>\n<\/em><\/strong><b>Francesco Ubertini, <\/b>IFAB President<\/p>\n<p><strong><em>INTRODUCTION<br \/>\n<\/em><\/strong><b>Sanzio Bassini, <\/b>IFAB Technical-Scientific Coordinator<br \/>\n<b>Florence Rabier, <\/b>ECMWF Director-General<br \/>\n<b>Tiziana Paccagnella,<\/b> Member of the Steering Committee of Italiameteo<\/p>\n<p><strong><em>PRESENTATION OF THE PROJECT AND ITS RESULTS<br \/>\n<\/em><\/strong><b>Peter Dueben, <\/b>ECMWF Coordinator for Machine Learning and AI Activities<br \/>\n<b>Fenwick Cooper, <\/b>ECMWF Visiting scientist<\/p>\n<p><strong><em>Q&amp;A<\/em><\/strong><\/p>\n<p><strong><em>Live stream event. The event will be held in English.<\/em><\/strong><\/p>\n<\/div><\/div><div class=\"w-separator size_small\"><\/div><div class=\"w-btn-wrapper align_none\"><a class=\"w-btn us-btn-style_4\" href=\"#\"><span class=\"w-btn-label\">Sign up now<\/span><\/a><\/div><div class=\"w-separator size_medium\"><\/div><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><h3>Rewatch the event:<\/h3>\n<p><iframe class=\"lazyload\" title=\"YouTube video player\" data-src=\"https:\/\/www.youtube-nocookie.com\/embed\/Tir-Jl1cpiI\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<\/div><\/div><div class=\"w-separator size_medium\"><\/div><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><h5><strong>About the project:<\/strong><\/h5>\n<p>The research project, <strong>realised by ECMWF<\/strong> (European Centre for Medium-Range Weather Forecasts) and <strong>supported by IFAB<\/strong> (International Foundation Big Data and Artificial Intelligence for Human Development) aims to <strong>reduce errors in the surface weather forecasts<\/strong>. Three machine learning algorithms are applied to model errors in the ECMWF operational 0-48 hour forecasts of temperature at 2m above the surface and wind at 10m above the surface. <strong>A reduction in global root-mean-squared error of between 5 and 15%,<\/strong> depending upon season, is observed. 1 year of training data and 3 months of verification data between December 2019 and May 2021 is used for each of four seasons. The three machine learning algorithms used are linear regression (of cubic polynomial functions), the random forest and a 3-layer neural network. Each algorithm returns similar error reduction perhaps indicating that they are all approximating the same physics. Examining errors at individual stations indicates large scale regions where error reduction is robust. Large regions where error reduction was non-existent or increased and individual stations where the error substantially increased also exist. Atmospheric variability, excessive model extrapolation and reduced station data quality are identified as three possible explanations.<\/p>\n<\/div><\/div><div class=\"wpb_text_column\" id=\"register_btn\"><div class=\"wpb_wrapper\"><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"On November 9th from 3.30 pm to 5 pm (CET), IFAB, in collaboration with ECMWF, will present the research project \u201cEmpirical modeling of 2m temperature and 10m wind\u201d. PROGRAM: INSTITUTIONAL GREETINGS Francesco Ubertini, IFAB President INTRODUCTION Sanzio Bassini, IFAB Technical-Scientific Coordinator Florence Rabier, ECMWF Director-General Tiziana Paccagnella, Member of the Steering Committee of Italiameteo PRESENTATION...","protected":false},"author":1,"featured_media":5994,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"class_list":["post-4747","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u2018Artificial Intelligence and Machine Learning to improve surface weather forecasts&#039; - webinar - November 9th, 2021 - IFAB International Foundation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ifabfoundation.org\/en\/2021\/09\/24\/artificial-intelligence-and-machine-learning-to-improve-surface-weather-forecasts-ifab-and-ecmwf-webinar-9-november-2021\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u2018Artificial Intelligence and Machine Learning to improve surface weather forecasts&#039; 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