HeyJaredEarthly Machine Learning
“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.
- Indexed episodes, last 90 days
- 5
- Latest publication
- Sep 20, 2026
- Audience
- Checking…
- Earliest in this view
- Aug 27, 2026
Latest episodes
Machine learning is revolutionizing weather forecasting – the next step is a change in how we work (opens the original)
Read excerpt
Citation: Dueben, P., Bauer, P., Fuhrer, O., Koldunov, N., & Kristiansen, J. (2026). Machine learning is revolutionizing weather forecasting – the next step is a change in how we work. arXiv preprint arXiv:2606.25076v1 . Key Takeaways: A Shift in the Forecasting Value Chain: While machine learning has rapidly achieved competitive skill in weather predictions, the next critical phase is a complete evolution of working practices and operating models. This transition will fundamentally reshape how
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling—Experiment Design and Overview (opens the original)
Read excerpt
Citation : Rampal, N., González-Abad, J., Addison, H., Baño-Medina, J., Bettolli, M. L., Blasone, V., Booth, B., Coppola, E., Di Gioia, S., Oldham-Dorrington, J., Doury, A., Engelbrecht, F., Fuentes-Franco, R., Gibson, P. B., Glawion, L., Hardy, C., Ivanov, M., Lee, H. K., Legasa, M. N., Olmo, M., Orr, A., Polz, J., Rogers, M. S. J., Schillinger, M., Sharma, S., Soares, P. M. M., Sobolowski, S., Steinkopf, J., Tang, W., Tian, J.-B., Tomé, R., Wang, K.-C., Wang, Y.-C., Watson, P. A. G., Wetherell
AIMIP Phase 1: Systematic Evaluations of AI Weather and Climate Models (opens the original)
Read excerpt
Citation: Henn, B., Bretherton, C. S., Kodunov, N., Lessig, C., Molina, M. J., Arcomano, T., Watt-Meyer, O., Couairon, G., Singh, R., Brunstein, R., Hasson, Y., Jost, A., Brenowitz, N., Manshausen, P., Cresswell-Clay, N., Durran, D., Hall, K. J. C., Yuval, J., Kochkov, D., Hoyer, S., & Lopez-Gomez, I. (2026). AIMIP Phase 1: systematic evaluations of AI weather and climate models. arXiv preprint . A New Benchmarking Era for AI Climate Models: AIMIP Phase 1 establishes the first systematic interco
WV-Net: A Foundation Model for SAR Ocean Satellite Imagery (opens the original)
Read excerpt
Citation: Glaser, Y., Stopa, J. E., Wolniewicz, L. M., Foster, R., Vandemark, D., Mouche, A., Chapron, B., & Sadowski, P. (2025). WV-Net: A Foundation Model for SAR Ocean Satellite Imagery. Artificial Intelligence for the Earth Systems , e250003. DOI: 10.1175/AIES-D-25-0003.1 Key Takeaways First Foundation Model for Open-Ocean SAR Imagery: WV-Net represents the first-ever foundation model designed specifically for open-ocean sea surface images, utilizing a massive dataset of nearly 10 million un
Toward Skillful Forecasting of Super El Niño Events Using a Diffusion-Based Westerly Wind Burst Parameterization (opens the original)
Read excerpt
Citation: Ji, C., Mu, M., Qin, B., Lian, T., Yuan, S., Feng, J., Song, S., Wei, Y., Dai, G., Wang, J., & Fang, X. (2025). Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization. npj Climate and Atmospheric Science (Published in partnership with CECCR at King Abdulaziz University). https://doi.org/10.1038/s41612-025-01158-x Key Takeaways: Innovative Generative AI Parameterization: The study introduces a state-of-the-art Denoising Diffusion
Publishing over time
Last 90 days. Choose a month to open its work.
Recurring subjects
Named in the text we hold. One piece can cover several.
Not enough subject data for this period yet.
Audience
No verified audience measurement yet.
About this data
Counts cover the work we have indexed. Tone needs enough text and a confident classification. Excerpts and episode notes are not full articles or transcripts.
Identity or attribution wrong? Suggest a correction.