用单图超分辨率技术将气候预测从12.5公里提升至1公里，保持精度不变。
Downscaling climate projections to 1 km with single-image super resolution
- 用观测数据训练超分辨率模型，对低分辨率气候预测进行统计降尺度。
- 在气象站位置评估气候指标，结果误差与原始低分辨率投影相当。
- 适合需要高精度本地气候决策的研究者和政策制定者。
高分辨率气候预测对本地决策至关重要，但现有气候预测空间分辨率较低（如12.5公里），限制了其应用。本文通过单图超分辨率模型，将气候预测统计性地降尺度至1公里分辨率。由于缺乏真实高分辨率气候数据，模型在高分辨率观测网格数据上训练，并应用于低分辨率气候预测。由于缺少真实高分辨率气候数据，无法使用像素级均方根误差等常见评估指标，因此采用气象站位置的气候指标进行评估。在日均温度上的实验表明，单图超分辨率模型可在不增加气候指标误差的前提下实现降尺度。
原文摘要 · Abstract (English)
High-resolution climate projections are essential for local decision-making. However, available climate projections have low spatial resolution (e.g. 12.5 km), which limits their usability. We address this limitation by leveraging single-image super-resolution models to statistically downscale climate projections to 1-km resolution. Since high-resolution climate projections are unavailable, we train models on a high-resolution observational gridded data set and apply them to low-resolution climate projections. We cannot evaluate downscaled climate projections with common metrics (e.g. pixel-wise root-mean-square error) because we lack ground-truth high-resolution climate projections. Therefore, we evaluate climate indicators computed at weather station locations. Experiments on daily mean temperature demonstrate that single-image super-resolution models can downscale climate projections without increasing the error of climate indicators compared to low-resolution climate projections.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。