Earthly Machine Learning

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 co...

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., S...

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 prepr...

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 desi...

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 Takeaw...

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/s41558-022-01377-7 Main Takeaways: Three Layers of AI's Climate Footprint : The authors propose a framework that splits machine learning's climate impact into t...

Machine learning for the physics of climate Citation: 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/s42254-024-00776-3 Main Takeaways: Breaking the El Niño Spring Barrier : For decades, forecasts of the El Niño Southern Oscillation hit a har...

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/10.1029/2024MS004655 Main Takeaways: A New Benchmark for AI Carbon Tracking : The authors introduce CarbonBench, the first systematic benchmark dataset d...

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-x Main Takeaways: Current Earth AI Models Are Missing the Point : Researchers have identified eleven features that an ideal E...

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/ae21f5 Main 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'...

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-6-995-2025 Main Takeaways: Innovative Machine Learning Approach:  The study introduces the Categorical Mixture...

Green and intelligent: the role of AI in the climate transition Citation:  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 accel...

Climate Knowledge in Large Language Models Kuznetsov, 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—corr...

Artificial Intelligence for Atmospheric Sciences: A Research Roadmap Citation: 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...

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 computation** of generalized Fourier transforms on the sphere ($S^2$) and the rotation group ($SO(3)$), specifically spherical harmonic and Wigner transforms....

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 (nowcastin...

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 vert...

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 hybr...

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 synthesizin...

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 singl...

Jigsaw: Training Multi-Billion-Parameter AI Weather Models With Optimized Model Parallelism Authors: Deifilia Kieckhefen, Markus Götz, Lars H. Heyen, Achim Streit, and Charlotte Debus (Karlsruhe Institute of Technology, Helmholtz AI) The paper introduces  WeatherMixer (WM) , a multi-layer perceptron (MLP)-based architecture designed for atmospheric forecasting, which serves as a competitive altern...

XiChen: An observation-scalable fully AI-driven global weather forecasting system with 4D variational knowledge Authors: Wuxin Wang, Weicheng Ni, Lilan Huang, Tao Han, Ben Fei, Shuo Ma, Taikang Yuan, Yanlai Zhao, Kefeng Deng, Xiaoyong Li, Boheng Duan, Lei Bai, Kaijun Ren XiChen is the first observation-scalable fully AI-driven global weather forecasting system . Its entire pipeline, from Data Assi...

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 , https://doi.org/10.1038/s41467-025-62024-1 • FuXi Weather is introduced as a groundbreaking end-to-end machine learning system for global weather forecasting. It autonomously performs data assimilation and forecasting in a 6-h...

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, and Alexander Keller • FourCastNet 3 (FCN3) introduces a pioneering geometric machine learning approach for probabilistic ensemble weather forecasting...

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 Inability to Extrapolate to Gray Swans Globally: AI weather models like FourCastNet struggle to predict "gray swan" tropical cyclones (TCs), which are rare, strong, and absent from training data. When Ca...