“Applying AI Weather Models With NVIDIA Earth-2” by Lee
Conference:
Experience Type(s):
Labs Type(s):
Title:
- Applying AI Weather Models With NVIDIA Earth-2
Organizer(s)/Presenter(s):
Description:
Explore how NVIDIA Earth-2 facilitates efficient weather and climate modeling. Learn how to run a large and growing stack of global AI weather forecasting models and how downscaling models generate super-resolution outputs. Discover use cases and applications benefiting the most from this emerging technology.
References:
[1] Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar. 2023. Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. In Proceedings of the 40th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 2806–2823. https://proceedings.mlr.press/v202/bonev23a.html
[2] Georg Ertl, Jussi Leinonen, and Stefan Weissenberger. 2025. Applying AI Weather Models With NVIDIA Earth-2. NVIDIA On-Demand. In GPU Technology Conference (GTC) 25. NVIDIA. https://www.nvidia.com/en-us/on-demand/session/gtc25-DLIT71336/ Session ID: DLIT71336. Level: General Interest. Topic: Simulation / Modeling / Design – Climate / Weather / Ocean Modeling.
[3] Nicholas Geneva, Dallas Foster, and NVIDIA Corporation. 2024. NVIDIA Earth2Studio. https://github.com/NVIDIA/earth2studio Accessed: May 26, 2025. Refer to the specific version used if different from v1.2.0.
[4] Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna De Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia de Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 730 (2020), 1999–2049. doi:https://doi.org/10.1002/qj.3803 arXiv:https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3803
[5] Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, and Anima Anandkumar. 2023. FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. In Proceedings of the Platform for Advanced Scientific Computing Conference (Davos, Switzerland) (PASC ’23). Association for Computing Machinery, New York, NY, USA, Article 13, 11 pages. doi:https://doi.org/10.1145/3592979.3593412
[6] Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Mohammad Amin Nabian, Tao Ge, Akshay Subramaniam, Karthik Kashinath, Jan Kautz, and Mike Pritchard. 2024. Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling. (2024). arxiv:https://arXiv.org/abs/2309.15214 [cs.LG] https://arxiv.org/abs/2309.15214
[7] NOAA/NCEP. 2024. Global Forecast System (GFS). https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast Accessed: May 27, 2025. For specific version and access details, refer to the NCEI GFS page.
[8] NVIDIA Corporation. 2024. NVIDIA Earth-2: AI and Simulation to Accelerate Climate and Weather Prediction. Available at: https://www.nvidia.com/en-us/deep-learning-ai/solutions/earth-2/. Accessed: May 27, 2025. Content on this page may be updated; consider citing specific components or publications if applicable.
[9] Zhaoxia Pu and Eugenia Kalnay. 2018. Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation. In Handbook of Hydrometeorological Ensemble Forecasting, Qingyun Duan, Florian Pappenberger, Jutta Thielen, Andy Wood, Hannah Cloke, and John Schaake (Eds.). Springer, Berlin, Heidelberg.
