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AI model BEAST achieves high-fidelity atmospheric forecasting with novel 4D-parallelism

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BEAST, a Bayesian Swin Transformer model for atmospheric forecasting that can quantify uncertainty. This model utilizes a novel 4D-parallelization scheme to efficiently train a 2.4-billion-parameter model, achieving high performance on the JUPITER supercomputer. Trained on 40 years of data, BEAST demonstrates competitive predictive skill and can forecast extreme events with exceptional accuracy, outperforming current AI and numerical models in speed for generating large ensembles. AI

IMPACT Advances AI capabilities in climate and Earth system sciences by enabling high-fidelity uncertainty quantification and faster extreme event prediction.

RANK_REASON Research paper detailing a new AI model and parallelization technique for atmospheric modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model BEAST achieves high-fidelity atmospheric forecasting with novel 4D-parallelism

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Research paper detailing a new AI model and parallelization technique for atmospheric modeling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus… ·

    4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

    arXiv:2609.12815v1 Announce Type: cross Abstract: We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computationa…

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