{"id":5096,"date":"2026-08-06T18:00:24","date_gmt":"2026-08-06T18:00:24","guid":{"rendered":"https:\/\/silvybrand.com\/?p=5096"},"modified":"2026-08-06T18:00:24","modified_gmt":"2026-08-06T18:00:24","slug":"deepmind-ai-model-can-predict-hurricanes-earlier","status":"publish","type":"post","link":"https:\/\/silvybrand.com\/?p=5096","title":{"rendered":"DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div>\n<p><span class=\"lead-in-text-callout\">In October 2025,<\/span> a storm brewed over the Caribbean Sea. Weather models differed on its trajectory. Would it remain weak and end up in Haiti, or would it intensify and head to Jamaica? Artificial intelligence model WeatherNext, developed by <a href=\"https:\/\/www.wired.com\/tag\/deepmind\/\" class=\"text link\">Google\u2019s DeepMind<\/a> and Google Research, went with the latter. Five days before landfall, <a data-offer-url=\"https:\/\/deepmind.google\/blog\/how-weathernext-helped-the-national-hurricane-center-better-predict-hurricane-melissas-historic-landfall-in-jamaica\/\" class=\"external-link text link\" data-event-click=\"{&quot;element&quot;:&quot;ExternalLink&quot;,&quot;outgoingURL&quot;:&quot;https:\/\/deepmind.google\/blog\/how-weathernext-helped-the-national-hurricane-center-better-predict-hurricane-melissas-historic-landfall-in-jamaica\/&quot;}\" href=\"https:\/\/deepmind.google\/blog\/how-weathernext-helped-the-national-hurricane-center-better-predict-hurricane-melissas-historic-landfall-in-jamaica\/\" rel=\"nofollow noopener\" target=\"_blank\">it predicted<\/a> with 80 percent confidence that the storm system would hit Jamaica as a Category 5 hurricane.<\/p>\n<p class=\"paywall\"><a href=\"https:\/\/www.wired.com\/story\/hurricane-melissa-has-meteorologists-terrified\/\" class=\"text link\">Hurricane Melissa<\/a> was catastrophic, causing flooding and landslides across Jamaica. But the AI model helped forecasters give an earlier warning to communities in its path, so they could better prepare.<\/p>\n<p class=\"paywall\">In a paper published on Thursday in <em>Nature<\/em>, researchers show the <a href=\"https:\/\/www.wired.com\/story\/google-deepmind-ai-weather-forecast\/\" class=\"text link\">WeatherNext AI model<\/a> can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models\u2019 predictions two days out. On the ground, that extra day can mean a lot.<\/p>\n<p class=\"paywall\">\u201cEven a few hours can make a difference,\u201d says Mike Brennan, director of the US National Hurricane Center. Organizing <a href=\"https:\/\/www.wired.com\/2016\/10\/move-2-million-people-hurricane-matthews-way\/\" class=\"text link\">evacuations<\/a>, staging supplies, and moving resources to respond to a hurricane risk are all time-sensitive tasks\u2014and making the wrong decision can have big consequences. \u201cTime is really golden when it comes to those types of decisions, so the ability to push forecast accuracy out as much as a day beyond what we&#8217;ve previously been able to do is really valuable,\u201d he says.<\/p>\n<p class=\"paywall\">Historically, bringing forecasts forward by a day would take a decade of work, the researchers say.<\/p>\n<p class=\"paywall\">Modeling extreme events can be challenging for AI. Machine learning requires ample training data in order to make future predictions, but extreme events are by nature rare occurrences. \u201cWe don\u2019t have that much cyclone data, but we have a lot of weather data,\u201d says Ferran Alet, a research scientist at Google DeepMind and one of the paper\u2019s lead authors. \u201cSo what we did was train a model to be both good at weather as well as cyclones.\u201d<\/p>\n<p class=\"paywall\"><a href=\"https:\/\/www.wired.com\/tag\/hurricanes\/\" class=\"text link\">Hurricanes<\/a> are particularly difficult to predict because they operate at multiple spatial scales, says Kate Musgrave, tropical cyclone group lead at the Cooperative Institute for Research in the Atmosphere, and an author on the paper. Predicting a storm\u2019s track\u2014which direction it\u2019s traveling \u2014requires data about weather on a global scale, taking in information such as the location of cold fronts and prevailing winds. Predicting a storm\u2019s intensity, however, requires much smaller-scale data focused specifically on the local atmospheric and ocean conditions.<\/p>\n<p class=\"paywall\">\u201cThat\u2019s something we just don\u2019t get from these global models,\u201d Musgrave says. While earlier AI models have done well at predicting a storm\u2019s track, \u201cintensity they could not do well at all.\u201d<\/p>\n<p class=\"paywall\">It\u2019s critical to predict both: A change in intensity can mean the difference between a relatively weak storm and a major hurricane. Sometimes\u2014as in the case of Hurricane Melissa\u2014a storm system can intensify rapidly, developing into an emergency situation overnight. Melissa marked the first time the National Hurricane Centre was able to predict a Category 5 hurricane when the storm was only at a Category 1 stage.<\/p>\n<p class=\"paywall\">Before the WeatherNext model was used in live forecasts, researchers tested it on retrospective data. \u201cThe results were so good that we were skeptical that we would actually see that in the real-time demonstration,\u201d Musgrave says. But when forecasters started adopting the model into their operations, this performance held true. \u201cI think everybody was surprised at just how well it did,\u201d Musgrave says.<\/p>\n<p class=\"paywall\">Even the DeepMind researchers working on the model don\u2019t fully understand how the AI model produces such accurate predictions, given it uses much lower-resolution atmospheric data than traditional models require to forecast storm intensity. \u201cWhen we told the community that our model was only using relatively coarse resolution, they were shocked, because that means that the lower-resolution inputs capture more signal about what\u2019s going to happen than previously believed,\u201d Alet says.<\/p>\n<\/div>\n<p><br \/>\n<br \/><a href=\"https:\/\/www.wired.com\/story\/deepmind-ai-model-can-predict-hurricanes-earlier\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In October 2025, a storm brewed over the Caribbean Sea. Weather models differed on its trajectory. Would it remain weak and end up in Haiti, or would it intensify and head to Jamaica? Artificial intelligence model WeatherNext, developed by Google\u2019s DeepMind and Google Research, went with the latter. Five days before landfall, it predicted with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":5097,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[1112,83,426,239,110,49,32,222],"class_list":["post-5096","post","type-post","status-publish","format-standard","has-post-thumbnail","category-gadgets","tag-alphabet","tag-artificial-intelligence","tag-climate","tag-data","tag-deepmind","tag-environment","tag-google","tag-weather"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DeepMind Says Its AI Can Predict Hurricanes Earlier Than Everyone Else - Silvybrand Lifestyle Blog<\/title>\n<meta name=\"description\" content=\"Its WeatherNext model, which will be open-sourced, can accurately predict both a storm\u2019s track and intensity using lower-resolution weather data. 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