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English(EN) WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models

WeatherBridge 适配器通过整合数值天气预报 (NWP) 和 ERA5 数据，改进了人工智能天气预报

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 WeatherBridge，这是一种新颖的适配器，旨在提高预训练天气预报模型的准确性。该适配器弥合了业务数值天气预报 (NWP) 初始状态与常用于训练的 ERA5 数据集之间的差距。WeatherBridge 在不改变原始预报模型的情况下，利用 Swin-UNet 预测残差校正并融入源嵌入以实现空间尺度感知，从而使异构 NWP 输入适应 ERA5 参考状态。该系统的训练目标包括 ERA5 重建和一步预报误差对齐，同时优化初始状态保真度和下游预报兼容性。评估表明，WeatherBridge 显著降低了各种 NWP 系统的预报均方根误差 (RMSE)，证明了其在改进来自不同业务输入的预报方面的有效性及其跨不同预训练模型的可迁移性。 AI

影响 通过使各种业务输入适应训练数据分布，提高了人工智能驱动的天气预报的准确性。

排序理由 详细介绍新模型自适应方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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WeatherBridge 适配器通过整合数值天气预报 (NWP) 和 ERA5 数据，改进了人工智能天气预报

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mutian Xi, Xinyu Su, Xiaohui Zhong, Hao Li ·

    WeatherBridge：面向预训练天气预报模型的预测感知多源输入自适应

    arXiv:2610.14863v1 Announce Type: new Abstract: Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numerical weather prediction (NWP) systems whose distributions differ substantially fr…

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