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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Atmospheric Predictability Beyond 30 Days with Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2504.20238/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2504.20238/feed.xml" rel="self" type="application/rss+xml" /><item><title>Atmospheric Predictability Beyond 30 Days with Machine Learning — mentioned by catlifeonmars</title><link>https://news.ycombinator.com/item?id=45296463</link><guid isPermaLink="false">urn:hackerpapers:mention:2504.20238:45296463</guid><pubDate>Thu, 18 Sep 2025 23:58:58 GMT</pubDate><description>&lt;p&gt;I don’t see how disagreeing with you constitutes “a complete and utter lack of grasp of the subject”. That’s hyperbole. So your claim is that the state of the art weather models are accurate at somewhere between 24h and 2 weeks (unclear based on the other sub threads) and continually improving. Based on this you extrapolate that given enough compute and sensors it would be possible to predict the weather with the same accuracy a month or more out. I think that’s a reasonable claim assuming that (1) the behavior of the system is deterministic and (2) the system behaves the same at both time…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2504.20238/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.ao-ph</category></item></channel></rss>
