edu.ucar.opensky::articles:43854 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:47:15.524874 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d75b06z5 https://doi.org/10.48550/arXiv.1912.12180 https Related Preprint #1 Axial Attention in Multidimensional Transformers https://doi.org/10.48550/arXiv.2202.07575 https Related Preprint #2 Forecasting Global Weather with Graph Neural Networks https://doi.org/10.48550/arXiv.2406.01465 https Related Preprint #3 AIFS -- ECMWF's data-driven forecasting system https://doi.org/10.48550/arXiv.2408.01581 https Related Preprint #4 Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators https://doi.org/10.48550/arXiv.2410.07472 https Related Preprint #5 Exploring the design space of deep-learning-based weather forecasting systems https://doi.org/10.48550/arXiv.2504.06007 https Related Preprint #6 CAMulator: Fast Emulation of the Community Atmosphere Model https://doi.org/10.5065/D6RX99HX https Related Service #1 Cheyenne: SGI ICE XA Cluster Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling 2025-06-23T00:00:00Z publication https://n2t.net/ark:/85065/d75b06z5 https://doi.org/10.1038/s41612-025-01125-6 Schreck, John S. author Sha, Yingkai author Chapman, William author Kimpara, Dhamma author Berner, Judith author McGinnis, Seth A. author Kazadi, Arnold author Sobhani, Negin author Kirk, Benjamin S. author Becker, Charles author Gantos, Gabrielle author Gagne, David John author UCAR/NCAR - Library publisher Recent advancements in artificial intelligence (AI) numerical weather prediction (NWP) have transformed atmospheric modeling. AI NWP models outperform state-of-the-art conventional NWP models like the European Center for Medium Range Weather Forecasting’s (ECMWF) Integrated Forecasting System (IFS) on several global metrics while requiring orders of magnitude fewer computational resources. However, existing AI NWP models still face limitations due to training datasets and dynamic timestep choices, often leading to artifacts that affect performance. To begin to address these challenges, we introduce the Community Research Earth Digital Intelligence Twin (CREDIT) framework, developed at the NSF National Center for Atmospheric Research (NCAR). CREDIT is a flexible, scalable, foundational research platform for training and deploying AI NWP models, providing an end-to-end pipeline for data preprocessing, model training, and evaluation. The CREDIT framework supports both existing architectures and the development of new models. We showcase this flexibility with WXFormer, a novel multiscale vision transformer designed to predict atmospheric states while mitigating common AI NWP pitfalls through techniques like spectral normalization, intelligent padding, and multi-step training. Additionally, we train the FuXi architecture within the CREDIT framework for comparison. Our results demonstrate that both FuXi and WXFormer, trained on hybrid sigma-pressure level ERA5 sampled at 6-h intervals, generally achieve better performance than the IFS High-Resolution (IFS HRES) on 10-day forecasts, offering potential improvements in efficiency and accuracy. The modular nature of CREDIT fosters collaboration, enabling researchers to experiment with models, datasets, and training options. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication <span style="font-family:Arial;font-size:10pt;font-style:normal;" data-sheets-root="1">Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</span> None eng geoscientificInformation