Earthly Machine Learning

00:20:59

Machine learning is revolutionizing weather...

Earthly Machine Learning ·

2026/09/20

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

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00:19:41

CORDEX-ML-Bench: A Benchmark for Data-Driven Regional...

Earthly Machine Learning ·

2026/09/13

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, T.,

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00:19:24

AIMIP Phase 1: Systematic Evaluations of AI Weather...

Earthly Machine Learning ·

2026/09/06

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

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00:20:48

WV-Net: A Foundation Model for SAR Ocean Satellite...

Earthly Machine Learning ·

2026/08/30

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-25-0003.1Key TakeawaysFirst 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

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00:17:13

Toward Skillful Forecasting of Super El Niño Events...

Earthly Machine Learning ·

2026/08/27

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-xKey Takeaways:Innovative Generative AI Parameterization: The study introduces a state-of-the-art Denoising Diffusion

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00:19:15

Aligning artificial intelligence with climate change...

Earthly Machine Learning ·

2026/05/09

Citation: Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527. https://doi.org/10.1038/s41558-022-01377-7Main Takeaways:Three Layers of AI's Climate Footprint: The authors propose a framework that splits machine learning's climate impact into three distinct categories — the energy and hardware emissions of computing itself, the immediate effects of

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00:19:37

Machine learning for the physics of climate

Earthly Machine Learning ·

2026/05/03

Machine learning for the physics of climateCitation: Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2025). Machine learning for the physics of climate. Nature Reviews Physics, 7, 6–20. https://doi.org/10.1038/s42254-024-00776-3Main Takeaways:Breaking the El Niño Spring Barrier: For decades, forecasts of the El Niño Southern Oscillation hit a hard wall at roughly 6 months lead time — a limit known as the spring predictability barrier. Convolutional neural

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00:20:31

Atmospheric Transport Modeling of CO2 With Neural...

Earthly Machine Learning ·

2026/04/27

Citation: Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., & Reichstein, M. (2025). Atmospheric transport modeling of CO2 with neural networks. Journal of Advances in Modeling Earth Systems, 17, e2024MS004655. https://doi.org/10.1029/2024MS004655Main Takeaways:A New Benchmark for AI Carbon Tracking: The authors introduce CarbonBench, the first systematic benchmark dataset designed specifically for training and evaluating machine learning emulators of Eulerian atmospheric transport

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00:17:32

On the foundations of Earth foundation models

Earthly Machine Learning ·

2026/04/20

Citation: Zhu, X. X., Xiong, Z., Wang, Y., Stewart, A. J., Heidler, K., Wang, Y., Yuan, Z., Dujardin, T., Xu, Q., & Shi, Y. (2026). On the foundations of Earth foundation models. Communications Earth & Environment, 7, 103. https://doi.org/10.1038/s43247-025-03127-xMain Takeaways:Current Earth AI Models Are Missing the Point: Researchers have identified eleven features that an ideal Earth foundation model must have — including geolocation awareness, multi-sensor integration, physical

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00:21:31

Whose weather is it? A fairness framework for...

Earthly Machine Learning ·

2026/04/14

Citation: Olivetti, L., & Messori, G. (2025). Whose weather is it? A fairness framework for data-driven weather forecasting. Environmental Research Letters, 20, 121006. https://doi.org/10.1088/1748-9326/ae21f5Main Takeaways:AI Weather Models Aren't Fair to Everyone: The latest generation of AI-powered weather forecasts improves predictions globally — but not equally. Using ECMWF's AIFS model as a case study, the authors show that wealthier and more densely populated areas consistently

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00:18:09

Learning predictable and informative dynamical...

Earthly Machine Learning ·

2026/03/07

Citation: Spuler, F. R., Kretschmer, M., Balmaseda, M. A., Kovalchuk, Y., & Shepherd, T. G. (2025). Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders. Weather and Climate Dynamics, 6, 995–1014. https://doi.org/10.5194/wcd-6-995-2025Main Takeaways:Innovative Machine Learning Approach: The study introduces the Categorical Mixture Model Variational Autoencoder (CMM-VAE), a novel generative machine learning method designed to identify

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00:18:25

Green and intelligent: the role of AI in the climate...

Earthly Machine Learning ·

2026/02/28

Green and intelligent: the role of AI in the climate transitionCitation: Stern, N., Romani, M., Pierfederici, R., Braun, M., Barraclough, D., Lingeswaran, S., Weirich-Benet, E., & Niemann, N. (2025). Green and intelligent: the role of AI in the climate transition. https://doi.org/10.1038/s44168-025-00252-3.Main Takeaways:Five Key Areas for Climate Action: Artificial Intelligence can accelerate the net-zero transition across five primary avenues: transforming complex economic systems, innovating

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00:11:57

Climate Knowledge in Large Language Models

Earthly Machine Learning ·

2026/01/26

Climate Knowledge in Large Language ModelsKuznetsov, I., Grassi, J., Pantiukhin, D., Shapkin, B., Jung, T., & Koldunov, N. (2025). Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research.LLMs have an internal "map" of the climate, but it is fuzzy: Without access to external tools, Large Language Models (LLMs) can recall the general structure of Earth’s climate—correctly identifying that the tropics are warm and high latitudes are cold. However, their specific numeric

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00:13:46

Artificial Intelligence for Atmospheric Sciences: A...

Earthly Machine Learning ·

2026/01/11

Artificial Intelligence for Atmospheric Sciences: A Research RoadmapCitation: Zaidan, M. A., Motlagh, N. H., Nurmi, P., Hussein, T., Kulmala, M., Petäjä, T., & Tarkoma, S. (2025). Artificial Intelligence for Atmospheric Sciences: A Research Roadmap.Revolutionizing Environmental Monitoring: The paper illustrates how AI is transforming atmospheric sciences by bridging the gap between computer science and environmental research. It details how AI processes massive datasets generated by diverse

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00:13:02

Differentiable and accelerated spherical harmonic and...

Earthly Machine Learning ·

2025/12/19

Differentiable and accelerated spherical harmonic and Wigner transformsMatthew A. Price, Jason D. McEwen*Journal of Computational Physics (2024)** This work introduces novel algorithmic structures for the **accelerated and differentiable computation** of generalized Fourier transforms on the sphere ($S^2$) and the rotation group ($SO(3)$), specifically spherical harmonic and Wigner transforms.* A key component is a **recursive algorithm for Wigner d-functions** designed to be stable to high

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00:12:46

Score-based diffusion nowcasting of GOES imagery

Earthly Machine Learning ·

2025/12/11

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 Engineering, Colorado State University, Fort Collins, CO** The research explored score-based diffusion models to perform short-term forecasts (nowcasting) of GOES geostationary infrared satellite imagery (zero to three hours). This newer machine learning

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00:15:57

FuXi-Ocean: A Global Ocean Forecasting System with...

Earthly Machine Learning ·

2025/12/04

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*---* **First Data-Driven Sub-Daily Global Forecast:** FuXi-Ocean is the first deep learning-based global ocean forecasting model to achieve six-hour temporal resolution at an eddy-resolving 1/12° spatial resolution, with vertical coverage extending up to 1500 meters. This capability addresses a crucial need for high-frequency

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00:15:07

Beyond the Training Data: Confidence-Guided Mixing of...

Earthly Machine Learning ·

2025/11/28

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-concept for transferring a machine learning (ML) convection parameterization**—trained on the ClimSim dataset—to the ICON-A climate model. The resulting hybrid ML-physics model achieved stable and accurate simulations in long-term AMIP-style runs lasting at least

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00:13:50

Climate in a Bottle: Towards a Generative Foundation...

Earthly Machine Learning ·

2025/11/23

Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere(By Noah D. Brenowitz, Tao Ge, Akshay Subramaniam, Peter Manshausen, Aayush Gupta, David M. Hall, Morteza Mardani, Arash Vahdat, Karthik Kashinath, Michael S. Pritchard, NVIDIA* The paper introduces **Climate in a Bottle (cBottle)**, a generative diffusion-based AI framework capable of synthesizing full global atmospheric states at an unprecedented $\mathbf{5 \text{ km resolution}}$ (over 12.5 million

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00:13:09

Probabilistic Measures for Fair AI and NWP Model...

Earthly Machine Learning ·

2025/11/07

Probabilistic measures afford fair comparisons of AIWP and NWP model output (Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch, June 10, 2025)Introduction of a New Fair Comparison Metric: The paper introduces the Potential Continuous Ranked Probability Score (PC), a new measure designed to allow fair and meaningful comparisons between single-valued output from data-driven Artificial Intelligence based Weather Prediction (AIWP) models and

en

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