
“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google NotebookLM, each episode distils the essence of a standout paper, helping you decide if it’s worth a deeper look. Stay updated on the ML innovations shaping our understanding of Earth. It may contain hallucinations.
| Publishes | Weekly | Episodes | 44 | Founded | a year ago |
|---|---|---|---|---|---|
| Number of Listeners | Categories | ScienceEarth Sciences | |||

Differentiable and accelerated spherical harmonic and Wigner transforms
Matthew A. Price, Jason D. McEwen
*Journal of Computational Physics (2024)*
* This work introduces novel algorithmic structures for the **accelerated and differentiable comput... more
Score-based diffusion nowcasting of GOES imagery
*Randy J. Chase, Katherine Haynes, Lander Ver Hoef, Imme Ebert-Uphoff, a Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, b Electrical and Computer En... more
FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
*Qiusheng Huang, Yuan Niu, Xiaohui Zhong, Anboyu Guo, Lei Chen, Dianjun Zhang, Xuefeng Zhang, Hao Li*
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* **First Data-Driven Sub-Daily Global Forecast:** FuXi-Ocean is the ... more
Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
*By Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, and Veronika Eyring*
* This paper presents a **successful proof-of-conce... more
How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.
Apple Podcasts | #113 | |
Apple Podcasts | #79 | |
Apple Podcasts | #43 | |
Apple Podcasts | #58 |
Listeners, social reach, demographics and more for this podcast.
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This podcast offers engaging discussions focused on the intersection of machine learning and climate science. By breaking down significant research papers, episodes provide valuable insights into the latest innovations and methodologies that enhance predictive modeling in weather and climate contexts. Topics frequently include advanced machine learning architectures, the integration of AI with traditional climate models, and methodologies that improve forecasting accuracy and efficiency. The podcast is unique in its emphasis on turning complex scientific discussions into digestible insights, making it an excellent resource for anyone interested in the role of technology in environmental science.
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Earthly Machine Learning launched a year ago and published 44 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.
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