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?

Today’s paper, NeuralGCM, 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, Dragon Ball Z, and the strange future of primitive equations.

Featured paper

Neural general circulation models for weather and climate

Chapters

• 00:00 Intro


• 01:31 Weather report & books


• 18:01 Paper time


• 25:26 A theory of compression


• 48:07 Closing thoughts: Resolution, interpretability, & the future of primitive equations

Recommended reading

• Chaos: Making a New Science — James Gleick


• Landmarks — Robert McFarlane


• Dandelion Wine — Ray Bradbury


• Webster’s 1913 Dictionary


• “The Bitter Lesson” — Rich Sutton


• MC-LSTM: Mass-Conserving LSTM


• Charisma and Disenchantment — Max Weber