{"id":7415,"date":"2023-11-15T09:23:17","date_gmt":"2023-11-15T09:23:17","guid":{"rendered":"https:\/\/dailyai.com\/?p=7415"},"modified":"2023-11-15T11:28:59","modified_gmt":"2023-11-15T11:28:59","slug":"googles-graphcast-model-predicts-weather-better-than-the-rest","status":"publish","type":"post","link":"https:\/\/dailyai.com\/sv\/2023\/11\/googles-graphcast-model-predicts-weather-better-than-the-rest\/","title":{"rendered":"Googles GraphCast-modell f\u00f6rutsp\u00e5r v\u00e4der b\u00e4ttre \u00e4n alla andra"},"content":{"rendered":"<p><strong>Google DeepMinds maskininl\u00e4rningsmodell GraphCast slog traditionella metoder f\u00f6r v\u00e4derprognoser med r\u00e5ge i tidiga tester.<\/strong><\/p>\n<p>Att f\u00f6rutsp\u00e5 v\u00e4dret exakt \u00e4r en sv\u00e5r uppgift som blir exponentiellt mer komplex ju fler dagar in i framtiden vi vill f\u00f6rutsp\u00e5.<\/p>\n<p>Det \u00e4r d\u00e4rf\u00f6r som meteorologiska organisationer f\u00f6rlitar sig p\u00e5 v\u00e4rldens mest kraftfulla superdatorer f\u00f6r att r\u00e4kna ut de komplexa prediktiva algoritmer som ber\u00e4ttar f\u00f6r oss om det kommer att regna i morgon eller inte.<\/p>\n<p>IFS-modellen (Integrated Forecasting System) som anv\u00e4nds av ECMWF (European Centre for Medium-Range Weather Forecasts) anses vara den mest exakta europeiska v\u00e4dermodellen. Och GraphCast slog den just med h\u00e4stl\u00e4ngder.<\/p>\n<p><a href=\"https:\/\/deepmind.google\/discover\/blog\/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting\/\" target=\"_blank\" rel=\"noopener\">GraphCast<\/a> kunde f\u00f6rutsp\u00e5 v\u00e4dret 10 dagar i f\u00f6rv\u00e4g snabbare och mer exakt \u00e4n ECMWF i mer \u00e4n 90% av 1.380 verifieringsm\u00e5l.<\/p>\n<p>Att veta om man ska ta med sig paraplyet till jobbet \u00e4r praktiskt, men att exakt f\u00f6rutse extrema v\u00e4derh\u00e4ndelser \u00e4r d\u00e4r GraphCast skulle kunna g\u00f6ra en betydande inverkan.<\/p>\n<p>I september, n\u00e4r orkanen Lee var 9 dagar fr\u00e5n att n\u00e5 land, arbetade v\u00e4derkontoren h\u00e5rt f\u00f6r att f\u00f6ruts\u00e4ga vilken stad som skulle drabbas h\u00e5rdast av stormen.<\/p>\n<p>IFS-modellen gav en grov f\u00f6ruts\u00e4gelse om att orkanen skulle tr\u00e4ffa n\u00e5gra nord\u00f6stra st\u00e4der eller eventuellt missa dem helt. Under tiden f\u00f6rutsp\u00e5dde GraphCast att orkanen Lee skulle tr\u00e4ffa Nova Scotia.<\/p>\n<p>Det var f\u00f6rst 3 dagar senare som IFS kom fram till samma slutsats, som s\u00e5 sm\u00e5ningom visade sig vara korrekt. F\u00f6rest\u00e4ll er den f\u00f6rb\u00e4ttring av resursanv\u00e4ndningen och r\u00e4ddningstj\u00e4nsten som 3 extra dagar skulle m\u00f6jligg\u00f6ra.<\/p>\n<p>Pushmeet Kohli, VP f\u00f6r forskning p\u00e5 Google DeepMind, sa: \"V\u00e4derprognoser \u00e4r ett av de mest utmanande problem som m\u00e4nskligheten har arbetat med under l\u00e5ng, l\u00e5ng tid. Och om man tittar p\u00e5 vad som har h\u00e4nt under de senaste \u00e5ren med klimatf\u00f6r\u00e4ndringarna \u00e4r det h\u00e4r ett otroligt viktigt problem.\"<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\" style=\"text-align: center;\">Vi presenterar GraphCast: v\u00e5r toppmoderna AI-modell som levererar 10-dagars v\u00e4derprognoser med o\u00f6vertr\u00e4ffad noggrannhet p\u00e5 mindre \u00e4n en minut. \ud83c\udf26\ufe0f<\/p>\n<p>Den kan till och med hj\u00e4lpa till att f\u00f6ruts\u00e4ga cykloners potentiella banor l\u00e4ngre fram i tiden.<\/p>\n<p>S\u00e5 h\u00e4r fungerar det. \ud83e\uddf5 <a href=\"https:\/\/t.co\/ygughpkdeP\">https:\/\/t.co\/ygughpkdeP<\/a> <a href=\"https:\/\/t.co\/0Y6DyBXDow\">pic.twitter.com\/0Y6DyBXDow<\/a><\/p>\n<p style=\"text-align: center;\">- Google DeepMind (@GoogleDeepMind) <a href=\"https:\/\/twitter.com\/GoogleDeepMind\/status\/1724443443873624115?ref_src=twsrc%5Etfw\">14 november 2023<\/a><\/p>\n<\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>Hur fungerar det?<\/h2>\n<p>Traditionella modeller som IFS anv\u00e4nder sig av \"numerisk v\u00e4derprognos\" (NWP). Detta inneb\u00e4r att man samlar in stora m\u00e4ngder data fr\u00e5n v\u00e4dersensorer som sedan matas in i komplexa ekvationer som bearbetas av superdatorer.<\/p>\n<p>Ekvationerna finjusteras av expertgrupper och har blivit alltmer exakta, men de kr\u00e4ver fortfarande mycket datorkraft och det tar l\u00e5ng tid att komma fram till en prognos.<\/p>\n<p>NWP f\u00f6rs\u00f6ker i huvudsak anv\u00e4nda algoritmer f\u00f6r att modellera hur atmosf\u00e4ren fungerar f\u00f6r att f\u00f6ruts\u00e4ga vad som kommer att h\u00e4nda h\u00e4rn\u00e4st.<\/p>\n<p>Det GraphCast g\u00f6r \u00e4r mycket enklare, men kan inte g\u00f6ras med traditionella ekvationer.<\/p>\n<p>GraphCast-modellen anv\u00e4nder maskininl\u00e4rningsbaserad v\u00e4derprognos (MLWP) och \u00e4r utbildad p\u00e5 cirka 50 \u00e5rs historiska v\u00e4derdata.<\/p>\n<p>Den tar sedan dessa data och i st\u00e4llet f\u00f6r att skapa en modell av v\u00e5r atmosf\u00e4r letar den efter m\u00f6nster i data. En AI-modell \u00e4r bra p\u00e5 att hitta subtila m\u00f6nster som \u00e4r om\u00f6jliga f\u00f6r fasta ekvationer att uppt\u00e4cka.<\/p>\n<p>GraphCast tittar i princip p\u00e5 aktuella v\u00e4derdata och s\u00e4ger: \"F\u00f6rra g\u00e5ngen jag s\u00e5g de h\u00e4r f\u00f6rh\u00e5llandena var n\u00e4sta sak som h\u00e4nde ...\" och g\u00f6r sedan en f\u00f6ruts\u00e4gelse.<\/p>\n<p>Detaljniv\u00e5n, eller uppl\u00f6sningen, i GraphCasts f\u00f6ruts\u00e4gelser \u00e4r inte lika bra som i traditionella modeller, men den \u00e4r mycket mer exakt och mycket snabbare.<\/p>\n<p>D\u00e4r en superdator skulle beh\u00f6va bearbeta v\u00e4derdata i timmar f\u00f6r att g\u00f6ra en f\u00f6ruts\u00e4gelse, kan GraphCast producera ett resultat p\u00e5 mindre \u00e4n en minut p\u00e5 en enda Google TPU v4-maskin.<\/p>\n<p>MLWP \u00e4r starkt beroende av historiska data som produceras av traditionella NWP-modeller, s\u00e5 det kommer inte att ers\u00e4tta dem riktigt \u00e4n.<\/p>\n<p>Att anv\u00e4nda ett verktyg som GraphCast tillsammans med nuvarande metoder kommer dock att bidra till att f\u00f6ruts\u00e4ga extrema v\u00e4derh\u00e4ndelser snabbare och mer exakt.<\/p>\n<p>Google DeepMind g\u00f6r GraphCast till \u00f6ppen k\u00e4llkod och det anv\u00e4nds redan av ECMWF.<\/p>","protected":false},"excerpt":{"rendered":"<p>Google DeepMinds maskininl\u00e4rningsmodell GraphCast slog traditionella v\u00e4derprognosmetoder med h\u00e4stl\u00e4ngder i tidiga tester. Att f\u00f6ruts\u00e4ga v\u00e4dret exakt \u00e4r en sv\u00e5r uppgift som blir exponentiellt komplex ju fler dagar in i framtiden vi vill f\u00f6ruts\u00e4ga. Det \u00e4r d\u00e4rf\u00f6r som meteorologiska organisationer f\u00f6rlitar sig p\u00e5 v\u00e4rldens mest kraftfulla superdatorer f\u00f6r att r\u00e4kna ut de komplexa prognosalgoritmer som ber\u00e4ttar f\u00f6r oss om det kommer att regna i morgon eller inte. IFS-modellen (Integrated Forecasting System) som anv\u00e4nds av ECMWF (European Centre for Medium-Range Weather Forecasts) anses vara den mest exakta europeiska v\u00e4dermodellen. Och GraphCast slog den just med h\u00e4stl\u00e4ngder.<\/p>","protected":false},"author":6,"featured_media":7417,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[83],"tags":[102,105],"class_list":["post-7415","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product","tag-google","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google\u2019s GraphCast model predicts weather better than the rest | DailyAI<\/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:\/\/dailyai.com\/sv\/2023\/11\/googles-graphcast-model-predicts-weather-better-than-the-rest\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s GraphCast model predicts weather better than the rest | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google DeepMind\u2019s machine learning model, GraphCast, beat traditional weather forecasting methods by some distance in early testing. Predicting the weather accurately is a difficult task that becomes exponentially complex the more days into the future we want to predict. This is why meteorological organizations rely on the world\u2019s most powerful supercomputers to crunch the complex predictive algorithms that tell us if it will rain tomorrow or not. The Integrated Forecasting System (IFS) model that the European Centre for Medium-Range Weather Forecasts (ECMWF) uses is considered the most accurate European weather model. And GraphCast just beat it by a long way.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/sv\/2023\/11\/googles-graphcast-model-predicts-weather-better-than-the-rest\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-15T09:23:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-15T11:28:59+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/weather-prediction.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"373\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta 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