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Astra model achieves high-resolution weather forecasts with reduced compute

By PulseAugur Editorial · [1 sources] ·

A new probabilistic weather forecasting model named Astra has been developed, achieving high-resolution predictions with significantly reduced computational resources. Astra utilizes a novel two-sided transport attention mechanism and a self-featurised continuous ranked probability score. It demonstrates comparable or superior performance to existing models like GenCast and the European Centre for Medium-Range Weather Forecasts ensemble on various benchmarks, while requiring substantially less hardware and time for training and inference. AI

IMPACT This model's efficiency could accelerate the development and deployment of AI for complex scientific forecasting tasks.

RANK_REASON The item describes a new model and its performance on benchmarks, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

Astra model achieves high-resolution weather forecasts with reduced compute

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The item describes a new model and its performance on benchmarks, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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 in which each region chooses where it reads fro…

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