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New Astra weather model uses less compute, beats ECMWF ensemble

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Astra, a novel probabilistic weather forecasting model that achieves high resolution with significantly reduced computational resources. Astra utilizes a two-sided transport attention mechanism, Kantorovich Attention, and a self-featurised continuous ranked probability score (CRPS). When benchmarked against WeatherBench 2, Astra demonstrated comparable performance to GenCast while using only 9.8% of its training compute. Furthermore, Astra outperformed the ECMWF ensemble on all tested combinations and showed a lower CRPS than GenCast on a majority of them, all while requiring substantially less computational power and memory. AI

IMPACT This model's efficiency could accelerate the development and deployment of high-resolution weather forecasting systems.

RANK_REASON The cluster describes a new research paper detailing a novel model and its performance on benchmarks. [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 →

New Astra weather model uses less compute, beats ECMWF ensemble

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The cluster describes a new research paper detailing a novel model and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Junu Choi, Youngjoon Hong ·

    Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention

    arXiv:2610.35545v1 Announce Type: new Abstract: High-resolution probabilistic weather forecasting remains computationally demanding. Here we present Astra, a 0.25$^\circ$ ensemble model trained on eight H200 GPUs. It combines Kantorovich Attention, a two-sided transport attention…

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