用预训练模型实现无需重训的气候降尺度，跨分辨率通用。
Resolution-Agnostic Transformer-based Climate Downscaling
- 基于预训练地球视觉变换器，直接从50公里分辨率降至3公里。
- 在3公里分辨率上表现良好，无需额外训练，精度媲美传统方法。
- 适合需快速生成大量区域气候模拟的研究者与决策部门。
理解区域和局部尺度上的未来气候变化对规划和决策至关重要，尤其在极端天气事件背景下，也广泛应用于农业、保险和基础设施建设。然而，将全球气候模型（GCMs）降尺度至所需精细分辨率的计算成本构成重大障碍。借鉴天气预报模型的进展，本研究提出一种基于预训练地球视觉变换器（Earth ViT）的高效降尺度方法。该模型最初在ERA5数据上训练，将分辨率从50公里降至25公里，随后在更高分辨率的BARRA-SY数据集（3公里）上测试，表现出色且无需额外训练，证明其具备跨分辨率泛化能力。该方法有望通过降尺度不同输入分辨率的GCMs，以极低训练成本生成大规模区域气候模拟集合。最终可提供更全面的关键气候变量未来变化估计，助力极端天气应对与气候变化适应策略制定。
原文摘要 · Abstract (English)
Understanding future weather changes at regional and local scales is crucial for planning and decision-making, particularly in the context of extreme weather events, as well as for broader applications in agriculture, insurance, and infrastructure development. However, the computational cost of downscaling Global Climate Models (GCMs) to the fine resolutions needed for such applications presents a significant barrier. Drawing on advancements in weather forecasting models, this study introduces a cost-efficient downscaling method using a pretrained Earth Vision Transformer (Earth ViT) model. Initially trained on ERA5 data to downscale from 50 km to 25 km resolution, the model is then tested on the higher resolution BARRA-SY dataset at a 3 km resolution. Remarkably, it performs well without additional training, demonstrating its ability to generalize across different resolutions. This approach holds promise for generating large ensembles of regional climate simulations by downscaling GCMs with varying input resolutions without incurring additional training costs. Ultimately, this method could provide more comprehensive estimates of potential future changes in key climate variables, aiding in effective planning for extreme weather events and climate change adaptation strategies.
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