<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Foundation Model for the Earth System · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2405.13063/</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/2405.13063/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Foundation Model for the Earth System — mentioned by wesselb</title><link>https://news.ycombinator.com/item?id=41593339</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.13063:41593339</guid><pubDate>Thu, 19 Sep 2024 16:10:02 GMT</pubDate><description>&lt;p&gt;Hello HN! We are a research team at Microsoft Research. We are pleased to share that our project, Aurora, has very recently been made fully open source, including model weights. We&amp;#x27;ve attempted to package the model and weights in a way that makes it as easy as possible for anyone to get started. On medium-term weather prediction, it sets a state-of-the-art amongst operational AI models outperforming GraphCast significantly. However, this is not what&amp;#x27;s most exciting. Aurora has been pretrained on a large variety of data sets, unlike other AI weather models, which makes Aurora ideal to be…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.13063/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.ao-ph</category></item><item><title>A Foundation Model for the Earth System — mentioned by nikhil-shankar</title><link>https://news.ycombinator.com/item?id=41556945</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.13063:41556945</guid><pubDate>Mon, 16 Sep 2024 15:18:37 GMT</pubDate><description>&lt;p&gt;Hi, Nikhil here. We haven&amp;#x27;t done a head-to-head comparison of GFT vs GraphCast, but our internal metrics show GFT improves on Aurora and published metrics show Aurora improves on GraphCast. You can see some technical details in section 6 of the Aurora paper (https://arxiv.org/pdf/2405.13063)&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.13063/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.ao-ph</category></item><item><title>A Foundation Model for the Earth System — HN submission</title><link>https://news.ycombinator.com/item?id=41556519</link><guid isPermaLink="false">urn:hackerpapers:submission:2405.13063:41556519</guid><pubDate>Mon, 16 Sep 2024 14:32:38 GMT</pubDate><description>&lt;p&gt;Launch HN: Silurian (YC S24) – Simulate the Earth&lt;/p&gt;&lt;p&gt;Hey HN! We’re Jayesh, Cris, and Nikhil, the team behind Silurian (https://silurian.ai). Silurian builds foundation models to simulate the Earth, starting with the weather. Some of our recent hurricane forecasts can be visualized at https://hurricanes2024.silurian.ai/. What is it worth to know the weather forecast 1 day earlier? That’s not a hypothetical question, traditional forecasting systems have been improving their skill at a rate of 1 day per decade. In other words, today’s 6-day forecast is as accurate as the 5-day forecast ten years ago. No one expects this rate of improvement to hold…&lt;/p&gt;&lt;p&gt;338 points; 141 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2405.13063/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>physics.ao-ph</category></item><item><title>A Foundation Model for the Earth System — mentioned by counters</title><link>https://news.ycombinator.com/item?id=40587131</link><guid isPermaLink="false">urn:hackerpapers:mention:2405.13063:40587131</guid><pubDate>Wed, 05 Jun 2024 16:41:25 GMT</pubDate><description>&lt;p&gt;The evaluation of the air pollution / chemistry forecasts in the arxiv paper [1] is really threadbare and entirely disconnected from the actual literature of atmospheric chemical transport modeling and air quality forecasting. The highlighted success case - a sandstorm, where PM10 is the key parameter being analyzed - is probably entirely explained by adequate performance of the underlying &amp;quot;weather&amp;quot; portion of the model (e.g. there is obviously a very strong correlation between intense winds and sandstorms in a desert), but the authors don&amp;#x27;t provide a more nuanced discussion of the forecast…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2405.13063/"&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>
