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New AI model S2S-JEPA improves long-range weather forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed S2S-JEPA, a new AI model designed to improve weather forecasting accuracy on subseasonal-to-seasonal (S2S) timescales, which typically range from two weeks to two months. Unlike previous AI weather models that struggle beyond two weeks due to predicting unpredictable fine details, S2S-JEPA focuses on forecasting only the stable, predictable components of weather patterns. This approach, inspired by computer vision's Joint-Embedding Predictive Architecture (JEPA), allows S2S-JEPA to match the skill of established physics-based ensemble models and even surpass them at longer forecast horizons. AI

IMPACT This model could enhance long-range weather predictions, benefiting sectors like agriculture, energy, and water management.

RANK_REASON The cluster contains an academic paper detailing a new AI model for a specific scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New AI model S2S-JEPA improves long-range weather forecasting

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The cluster contains an academic paper detailing a new AI model for a specific scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chenyu Dong, Gianmarco Mengaldo ·

    S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales

    arXiv:2610.03106v1 Announce Type: cross Abstract: The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water management. Yet, it is widely known as the `predictability d…

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