具有上下文意识的隐性神经表征来压缩地球系统模型数据
Farinaz Mostajeran1, Nikhil M Pawar2, Jonathan M Villarreal2
1Energy & Intelligence Lab, Department of Chemical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
Scientific reports
|July 17, 2025
概括
情境感知隐性神经表示 (CA-INR) 通过使用辅助物理变量来改善气候数据的压缩. 这种方法减少了重建错误,提高了气候分析数据的质量.
科学领域:
- 地球系统科学 地球系统科学
- 数据科学数据科学数据科学
- 气候建模气候模型
背景情况:
- 像E3SM这样的多物理多尺度气候模型产生了对气候分析至关重要的大量数据集.
- 数据压缩对于管理大型气候数据集至关重要,隐式神经表示 (INR) 是有前途的.
- 标准INR可以引入重建错误,可能阻碍下游气候研究.
研究的目的:
- 为改善气候数据的损耗压缩,开发一种新的上下文感知隐性神经表示 (CA-INR).
- 评估CA-INR在减少重建错误的有效性,同时保持高压缩率.
- 评估将上下文物理变量纳入气候数据压缩性能的影响.
主要方法:
- 提出了一个使用多层感知器 (MLP) 架构的上下文感知隐含神经表示 (CA-INR) 模型.
- 训练CA-INR模型以超拟合数据,使用时空坐标和辅助物理变量 (上下文) 作为输入.
- 评估了CA-INR在来自E3SM (Energy Exascale Earth System Model) 的表面温度数据上的性能,测试了诸如地形和气候温度等各种上下文输入.
主要成果:
- 整合上下文信息显著减少了气候数据压缩中的重建错误.
- CA-INR模型实现了与标准INR相比较的高压缩率.
- 包含上下文,特别是地形和气候平均温度,提高了数据重建质量.
结论:
- 与标准INR相比,CA-INR为气候数据的损耗压缩提供了一种优越的方法.
- 该方法有效地减少了重建错误,使压缩数据适合详细的气候分析.
- 语境信息整合是提高压缩地球系统模型数据的准确性和实用性的关键.
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