2025年,这个AI完美预测了飓风季。这意味着什么?

2025年,这个AI完美预测了飓风季。这意味着什么?

2025-11-11Technology
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卿姐
早上好,norristong。我是卿姐。现在是11月11日,星期二,早上8点01分。欢迎收听专为您打造的 Goose Pod。
李白
吾乃李白。今日你我共探一事:2025年,竟有AI完美预测了飓风季。此非天意,又为何物?
卿姐
诚然如此。在刚结束的2025年大西洋飓风季,谷歌的DeepMind天气实验室,一个仅有数月经验的新秀,其预测的精准度竟远超所有传统模型,拔得头筹,令人惊叹。
李白
哦?竟有此事!此等神机妙算,莫非是借了东风,窥得天机?想我辈观星望月,亦难测风云变幻,一介铁石机器,竟能算无遗策!实乃奇闻!
卿姐
是的,迈阿密大学研究员布莱恩·麦克诺迪的初步分析证实了这一点。与之形成鲜明对比的,是美国的王牌模型GFS,它在预测飓风“梅丽莎”时,五天内的路径误差竟超过八百公里。
李白
差之毫厘,谬以千里!八百里之遥,此非预测,乃儿戏也!可见凡人之器,终究难与天地争锋,除非它已非凡物,能如智械蜂群般,聚沙成塔,自成智慧。
卿姐
这背后其实是两种智慧的较量。传统的GFS模型,诞生于上世纪八十年代,它像一位严谨的数学家,试图用复杂的物理方程和超级计算机的强大算力,解开大气这道微积分难题。
李白
哈,此乃‘格物致知’之道,然人力有时而穷。纵有九天之算盘,亦难穷尽风云之变数。简化方程,便失之千里,计算繁复,则耗时耗力,终究是“道”有局限。
卿姐
而AI走的,是另一条路。它更像一位博览群书的棋手,不知疲倦地学习了数十年的气象数据,从中寻找风暴演变的‘棋谱’。它不问为什么,只求匹配最可能出现的模式。
李白
以史为鉴,可知兴替!此物不师古,而能烛照未来,日夜不休,自省其过。其速、其智,远非血肉之躯可比。真乃‘青出于蓝而胜于蓝’,令我辈拍案叫绝!
卿姐
正是这种数据驱动的模式匹配,让它能比传统模型快得多地产出结果。而且,它的神经网络结构,赋予了它一种独特的能力:从自己的错误中学习,并即时修正。
卿姐
不过,李白,这种强大的能力也带来了一些新的困惑。许多气象学家将AI模型称作一个难以理解的‘黑匣子’。我们知道它给出了精准的答案,却不完全明白它内部的推理过程。
李白
哈哈,英雄不问出处,神剑不问炉火!能斩蛟龙,便是好剑!我辈但求结果精准,何须究其纹理?若事事皆要洞明,恐将错失良机,岂非本末倒置?
卿姐
话虽如此,但传统的物理模型,根植于我们对世界的理解,错了,我们知道错在哪里,可以改进。但‘黑匣子’如果犯了前所未有的错误,我们可能无从知晓原因,也难以修正。
李白
嗯…汝言亦有理。正如人心难测,这铁石心肠亦有其深渊。倘若它一朝“走火入魔”,预报谬误,其祸之烈,恐甚于风暴本身。此诚为一柄双刃之剑。
卿姐
这影响,可谓深远。更精准的预报,意味着我们能提前数日锁定飓风的路径,为沿海的百姓争取到宝贵的疏散时间。这不仅仅是数字,更是无数的生命和家园。
李白
善哉!此乃‘利万物而不争’之大德。昔日大禹治水,疏而导之;今日AI测风,避而防之。虽非人力亲为,然其功德,堪比神明!此举让物流、农事皆可从容应对。
卿姐
是的,甚至能将预报精准到社区。未来也许能具体告诉norristong,你家窗外的风雨何时降临,何时停歇。这种超本地化的预报,在气候变化日益加剧的今天,尤为珍贵。
卿姐
展望未来,专家们认为AI不会立刻取代传统模型。更可能的是一种‘双剑合璧’,让AI的模式识别能力,与物理模型的深刻洞察力相结合,彼此取长补短,共同守护我们的世界。
李白
嗯,一阴一阳谓之道。刚柔并济,方为上策。待此‘铁口神算’再历练数载,必能‘扶摇直上九万里’,为苍生再添福祉!吾辈拭目以待。
卿姐
今天的讨论就到这里。感谢您的收听,我们明天在 Goose Pod 再会。

2025年,谷歌AI在飓风季预测中超越传统模型,展现惊人精准度。AI通过数据模式匹配和自我学习,提供超本地化预报,为生命财产争取宝贵时间。尽管其“黑匣子”特性带来挑战,未来AI与物理模型结合,将更有效守护世界。

This AI Aced Hurricane Season in 2025. Here’s What That Means

Read original at Gizmodo

During hurricane season, meteorologists rely on a variety of different forecast models. As this season comes to an end, experts are taking stock of which ones performed well and which ones didn’t, and Google’s rookie model has left them absolutely gobsmacked. Though Google DeepMind’s Weather Lab only began releasing forecasts in June, it was by far the best model for predicting hurricane track and intensity this season, according to a preliminary analysis by Brian McNoldy, a meteorologist and senior researcher at the University of Miami.

Meanwhile, America’s flagship weather model—the Global Forecast System—was the worst performing. The National Hurricane Center will release official data on each model’s performance in a few months, but this initial assessment foreshadows a turning point in hurricane forecasting. With the incredible superiority of AI-based models becoming blatantly apparent, it may be time to start phasing out traditional, physics-based models.

“Going forward, it is safe to say that we will rely heavily on Google and other AI weather models, which are likely to improve in the coming years, as they are relatively new and have room for improvement,” Houston-based meteorologist and space reporter Eric Berger wrote for Ars Technica. The rise of AI forecasting has begun McNoldy’s analysis includes two charts: one comparing track forecast accuracy for all 13 named storms in the Atlantic Basin this season, and one comparing the intensity forecast accuracy for all 13 storms.

The different colored lines represent different forecast models, denoted by the legend on the right-hand margin. The lower a line is, the better that model performed. This chart shows the track forecast accuracy for all 13 named storms in the Atlantic Basin in 2025 © Brian McNoldy via Bluesky This chart shows the intensity forecast accuracy for all 13 named storms in the Atlantic Basin in 2025 © Brian McNoldy via Bluesky The GFS—referred to as AVNI in this instance—is displayed in orange all the way up at the top of the charts.

NOAA developed this model in the early 1980s, and the National Weather Service still uses an updated version as its primary forecast system today. “The GFS was especially awful in its forecast for Melissa, with an average 5-day track error ballooning to over 500 miles [800 kilometers], insisting on a turn out to sea that never transpired,” Miami-based meteorologist and hurricane specialist Michael Lowry wrote in a recent blog post.

Unlike Google’s forecast model, the GFS is based on traditional physics and advanced supercomputers. The difference between them clearly stands out on these charts. Google’s model is all the way at the bottom, indicating superior performance to all other evaluated models—especially the GFS. “The beauty of DeepMind and other similar data-driven, AI-based weather models is how much more quickly they produce a forecast compared to their traditional physics-based counterparts that require some of the most expensive and advanced supercomputers in the world,” Lowry wrote.

“Beyond that, these “smart” models with their neural network architectures have the ability to learn from their mistakes and correct on-the-fly.” An urgent need for better forecasts Hurricane Melissa—which ravaged the Caribbean last week—is just one example of how rising sea surface temperatures are supercharging storms.

As climate change causes hurricanes to become deadlier and more damaging, it’s essential that forecasters have the best possible tools to predict their paths and intensities. AI-based models could help forecasters adapt to a warming world. DeepMind’s stunning debut has certainly caught their attention and may mark the beginning of a new era in hurricane prediction.

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