频率感知视觉变压器用于地球系统模型的高保真度超分辨率
Ehsan Zeraatkar1, Salah A Faroughi2, Jelena Tešić3
1Computer Science, Texas State University, San Marcos, Texas, USA. ehsanzeraatkar@txstate.edu.
Scientific reports
|February 24, 2026
概括
新的频率感知深度学习模型ViSIR和ViFOR在地球系统模型模拟中增强了空间细节. 这些模型克服了光谱偏差,提高了气候科学数据的准确性.
科学领域:
- 气候科学 气候科学
- 地球系统建模模型
- 人工智能的人工智能
背景情况:
- 粗地球系统模型 (ESM) 模拟缺乏对气候科学至关重要的空间忠实性.
- 传统的深度超分辨率方法经常引入光谱偏差,偏好低频细节而不是高频信息.
研究的目的:
- 开发新的频率意识深度学习框架,以减轻ESM超分辨率中的光谱偏差.
- 改进气候数据中微型结构和高频细节的重建.
主要方法:
- 介绍了ViSIR (视觉转换器调整的阴影暗示表示),将视觉转换器与阴影激活相结合.
- 开发了ViFOR (视觉转换器里埃表示网络),集成显式里埃过,用于独立的频率学习.
- 在E3SM-HR地球系统数据集上对表面温度,短波和长波流的评估模型.
主要成果:
- ViSIR和ViFOR的性能优于卷积神经网络 (CNN),生成网络和香草变压器.
- 维福在峰值信号与噪声比率 (PSNR) 中实现了高达2.6dB的改进.
- 与基线方法相比,这两种模型都显示出更高的结构相似性 (SSIM).
结论:
- 像ViSIR和ViFOR这样的频率意识框架有效地解决了ESM超分辨率中的光谱偏差.
- 这些模型显著提高了气候模拟输出的空间真实性.
- 对于高准确度的气候数据重建,ViFOR特别有前途.
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