{"id":1071,"date":"2014-10-14T14:49:48","date_gmt":"2014-10-14T12:49:48","guid":{"rendered":"http:\/\/www.h-its.org\/?p=1071"},"modified":"2019-08-05T11:07:26","modified_gmt":"2019-08-05T09:07:26","slug":"erc-sciencefore-2","status":"publish","type":"hits-project","link":"https:\/\/www.h-its.org\/de\/projects\/erc-sciencefore-2\/","title":{"rendered":"ERC Advanced Grant ScienceFore – Die Wissenschaft der Vorhersage"},"content":{"rendered":"\n

Im Angesicht unvermeidbarer Unsicherheiten sollten Vorhersagen probabilistisch sein, d.h., Prognosen sollten die Form von Wahrscheinlichkeitsverteilungen \u00fcber zuk\u00fcnftige Ereignisse und Gr\u00f6\u00dfen annehmen.  Dementsprechend erleben wir einen transdisziplin\u00e4ren Paradigmenwechsel von deterministischen oder Punktvorhersagen hin zu probabilistischen Vorhersagen.  Das vom Europ\u00e4ischen Forschungsrat (ERC) gef\u00f6rderte ScienceFore Projekt sucht diese Entwicklungen nachhaltig zu unterst\u00fctzen, indem es theoretische Grundlagen f\u00fcr wissenschaftlich fundierte Vorhersagen begr\u00fcndet, eine Vorreiterrolle in der Entwicklung entsprechender statistischer Methoden einnimmt und diese in wichtigen Anwendungsproblemen zum Einsatz bringt.<\/p>\n\n\n\n

Auf der theoretischen Seite untersuchen wir Aggregationsverfahren f\u00fcr die Kombination von probabilistischen Vorhersagen und besch\u00e4ftigen uns mit der Entwicklung und der Struktur von G\u00fctema\u00dfen, welche wahrheitsgetreue und sorgf\u00e4ltige Vorhersagen f\u00f6rdern, wie etwa korrekte Bewertungsregeln.  Methodisch und in Anwendungen entwickeln wir Verfahren zur statistischen Nachbearbeitung des THORPEX Interactive Grand Global Ensemble (TIGGE), welches die international f\u00fchrenden globalen numerischen Wetterprognose-Modelle umfa\u00dft.  Dabei gilt es, physikalisch realistische und koh\u00e4rente Abh\u00e4ngigkeitsstrukturen zwischen den Wettervariablen, \u00fcber Kontinente und Ozeane und \u00fcber Vorhersagehorizonte hinweg zu erhalten.  Ferner untersuchen wir den Einsatz statistischer Nachbearbeitungsverfahren in makro\u00f6konomischen Expertenbefragungen. <\/p>\n\n\n\n

Theorie und Anwendungen erg\u00e4nzen und unterst\u00fctzen sich dabei gegenseitig. So haben die Eigenschaften von Aggregationsverfahren Auswirkungen auf die Entwicklung von Nachbearbeitungsverfahren f\u00fcr numerische Wetterprognose-Modelle, und entscheidungstheoretisch fundierte Ans\u00e4tze f\u00fcr G\u00fctema\u00dfe lassen die Praxis von Punktvorhersagen in neuem Licht erscheinen.<\/p>\n\n\n\n

\"European_Union\"<\/a><\/figure>\n\n\n\n

This project has received funding from the European Union\u2019s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 290976.<\/em><\/p>\n","protected":false},"author":6,"featured_media":38869,"template":"","hits-research-group":[1285],"hits-project-category":[1396],"class_list":["post-1071","hits-project","type-hits-project","status-publish","has-post-thumbnail","hentry","hits-research-group-cst-de","hits-project-category-previous-projects-de"],"acf":[],"yoast_head":"\nERC Advanced Grant ScienceFore - Die Wissenschaft der Vorhersage - HITS gGmbH<\/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.h-its.org\/de\/projects\/erc-sciencefore-2\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ERC Advanced Grant ScienceFore - 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