{"id":24743,"date":"2025-05-23T16:27:04","date_gmt":"2025-05-23T16:27:04","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=24743"},"modified":"2025-05-23T16:27:05","modified_gmt":"2025-05-23T16:27:05","slug":"can-ai-predict-extreme-weather-events","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/can-ai-predict-extreme-weather-events\/","title":{"rendered":"Can AI Predict Extreme Weather Events?"},"content":{"rendered":"\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">A new study highlights how current AI models fall short in predicting unprecedented weather events, underscoring the need for integrating more physics and mathematical tools to improve forecasting.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>Researchers from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, have revealed the limitations of AI-powered weather prediction. Their findings, <a href=\"https:\/\/www.pnas.org\/doi\/10.1073\/pnas.2420914122\" target=\"_blank\" rel=\"noopener\" title=\"\">published<\/a> in the Proceedings of the National Academy of Sciences, indicate that while neural networks excel at routine weather forecasting, they struggle with predicting unprecedented extreme weather events.<\/p>\n\n\n\n<p>AI models have fundamentally transformed the field of meteorology, making accurate short-term forecasts with significantly less computational power compared to traditional models. <\/p>\n\n\n\n<p>&#8220;AI weather models are one of the biggest achievements in AI in science,&#8221; co-corresponding author Pedram Hassanzadeh, an associate professor of geophysical sciences at UChicago, said in a news release. &#8220;What we found is that they are remarkable, but not magical.&#8221;<\/p>\n\n\n\n<p>However, these models fall short when tasked with predicting &#8220;gray swan&#8221; events \u2014 extreme weather occurrences that surpass the scope of the training data. <\/p>\n\n\n\n<p>Neural networks rely on past weather patterns, which means their predictive capabilities are limited by the historical data they are fed. For instance, they would struggle to foresee a once-in-a-2000-year event like the floods caused by Hurricane Harvey in 2017.<\/p>\n\n\n\n<p>To test the limits of current models, the researchers trained a neural network on weather data while excluding instances of Category 3 or stronger hurricanes. When asked to predict a Category 5 hurricane, the model could not do so.<\/p>\n\n\n\n<p>&#8220;It always underestimated the event. The model knows something is coming, but it always predicts it\u2019ll only be a Category 2 hurricane,&#8221; added co-corresponding author Yongqiang Sun, a research scientist at UChicago.<\/p>\n\n\n\n<p>This issue points to a significant flaw \u2014 predicting false negatives \u2014 whereby the model fails to identify the severity of extreme weather, potentially underestimating the risks and causing disastrous consequences.<\/p>\n\n\n\n<p>Unlike traditional models that incorporate mathematical and physical principles governing atmospheric conditions, neural networks operate solely based on previously observed patterns. This limitation has significant implications as the use of AI extends to operational weather forecasting and early warning systems.<\/p>\n\n\n\n<p>The research suggests that integrating mathematical tools and the principles of atmospheric physics into AI models may address these deficiencies.<\/p>\n\n\n\n<p>\u201cThe hope is that if AI models can really learn atmospheric dynamics, they will be able to figure out how to forecast gray swans,\u201d Hassanzadeh added.\u00a0<\/p>\n\n\n\n<p>One promising method is &#8220;active learning,&#8221; where AI can guide traditional physics-based models to simulate more extreme events, thus improving the AI\u2019s training data.<\/p>\n\n\n\n<p>&#8220;Longer simulated or observed datasets aren&#8217;t going to work. We need to think about smarter ways to generate data,&#8221; added co-author Jonathan Weare, a professor at NYU&#8217;s Courant Institute of Mathematical Sciences. \u201cIn this case, that means answering the question &#8216;where should I place my training data to achieve better performance on extremes?&#8217; Fortunately, we think AI weather models themselves, when paired with the right mathematical tools, can help answer this question.\u201d<\/p>\n\n\n\n<div style=\"height:12px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Source:\u00a0<\/strong><a href=\"https:\/\/news.uchicago.edu\/story\/ai-good-weather-forecasting-can-it-predict-freak-weather-events\" target=\"_blank\" rel=\"noopener\" title=\"\">University of Chicago<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, have revealed the limitations of AI-powered weather prediction. Their findings, published in the Proceedings of the National Academy of Sciences, indicate that while neural networks excel at routine weather forecasting, they struggle with predicting unprecedented extreme [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[302,352],"class_list":["post-24743","post","type-post","status-publish","format-standard","hentry","category-ai","tag-uc-santa-cruz","tag-university-of-chicago"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"Researchers from the University of Chicago, in collaboration with New York University and the University of California Santa Cruz, have revealed the limitations of AI-powered weather prediction. Their findings, published in the Proceedings of the National Academy of Sciences, indicate that while neural networks excel at routine weather forecasting, they struggle with predicting unprecedented extreme&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/24743","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=24743"}],"version-history":[{"count":10,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/24743\/revisions"}],"predecessor-version":[{"id":24819,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/24743\/revisions\/24819"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=24743"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=24743"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=24743"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}