“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 | 37 | Founded | 9 months ago |
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Number of Listeners | Categories | ScienceEarth Sciences |
XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledgeAuthors: Wuxin Wang, Weicheng Ni, Lilan Huang, Tao Han, Ben Fei, Shuo Ma, Taikang Yuan, Yanlai Zhao, Kefeng Deng, Xiaoyong Li, Boheng Duan,... more
A data-to-forecast machine learning system for global weather
Xiuyu Sun et al. (2025). A data-to-forecast machine learning system for global weather. Nature Communications, doi.org/10.1038/s41467-025-62024-1
• FuXi Weather is introduced as... more
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, ... more
Can AI weather models predict out-of-distribution gray swan tropical cyclones?by Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand, Ashesh Chattopadhyay, Jonathan Weare, and Dorian S. Abbot
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Inability to Extrapolate to Gray Swans Globally: AI weathe... more
How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.
Apple Podcasts | #60 | |
Apple Podcasts | #92 | |
Apple Podcasts | #61 |
Listeners, social reach, demographics and more for this podcast.
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Targeting an audience interested in the intersection of artificial intelligence and climate science, the content emphasizes recent advancements in machine learning techniques applied to weather forecasting and environmental monitoring. Each episode typically dives into a specific research paper, unpacking its findings and significance to equip listeners with the knowledge required to engage with the latest scientific literature. The podcast highlights innovative frameworks and models that promise to enhance predictive capabilities in climate science, thus offering listeners insights into the future of AI in understanding Earth systems.
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Earthly Machine Learning launched 9 months ago and published 37 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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