<p>Machine learning dominates the conversation, but what will happen to the centuries-old physical equations that built our understanding of the atmosphere? How are the two approaches at odds? How might they coexist?</p>
<p>Today’s paper, <em>NeuralGCM</em>, sheds light on how physics-based and AI approaches might be a powerful pairing for weather forecasting. For the first truly hybrid model we’ve discussed, it’s only fitting that we’ve also taken a hybrid approach in this conversation. So join us for hybrid models, data compression, <em>Dragon Ball Z</em>, and the strange future of primitive equations.</p>
<hr>
<h2>Featured paper</h2>
<p><strong><a href="https://www.nature.com/articles/s41586-024-07744-y">Neural general circulation models for weather and climate</a></strong></p>
<hr>
<h2>Chapters</h2>
<ul>
<li>00:00 Intro</li>
<li>01:31 Weather report & books</li>
<li>18:01 Paper time</li>
<li>25:26 A theory of compression</li>
<li>48:07 Closing thoughts: Resolution, interpretability, & the future of primitive equations</li>
</ul>
<hr>
<h2>Recommended reading</h2>
<ul>
<li><em>Chaos: Making a New Science</em> — <strong>James Gleick</strong></li>
<li><em>Landmarks</em> — <strong>Robert McFarlane</strong></li>
<li><em>Dandelion Wine</em> — <strong>Ray Bradbury</strong></li>
<li><a href="https://www.google.com/url?q=https://www.websters1913.com/words/Wind"><em>Webster’s 1913 Dictionary</em></a></li>
<li><a href="http://www.incompleteideas.net/IncIdeas/BitterLesson.html">“<strong>The Bitter Lesson</strong>”</a> — <strong>Rich Sutton</strong></li>
<li><a href="https://arxiv.org/pdf/2101.05186">MC-LSTM: Mass-Conserving LSTM</a></li>
<li><em>Charisma and Disenchantment</em> — <strong>Max Weber</strong></li>
</ul>