将大气数据压缩成其真实信息内容
Milan Klöwer1, Miha Razinger2, Juan J Dominguez2
1Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK. milan.kloewer@physics.ox.ac.uk.
Nature computational science
|January 13, 2024
概括
天气和气候数据是高度可压缩的. 通过识别和删除多余的信息,科学家可以实现显著的数据压缩,保留预测的关键细节.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 信息理论 信息理论
- 数据科学数据科学数据科学
背景情况:
- 全球天气和气候中心每年产生大量数据 (数百倍字节).
- 有效的数据压缩对于存储和共享这些气候数据至关重要.
- 现有的压缩方法无法区分真实和虚假信息,因此数据的精度没有得到评估.
研究的目的:
- 定义和量化哥白尼天文大气监测服务 (CAMS) 数据的比特级真实信息内容.
- 为天气和气候数据开发优化的压缩策略.
- 提出一个数据压缩图灵测试来评估可压缩性和信息丢失.
主要方法:
- 应用信息理论来定义CAMS数据中的真实信息内容.
- 分析时空相关性,以评估数据的可压缩性.
- 实现了无损压缩算法,在经过非信息性比特的圆形化后.
- 评估压缩比和信息保存.
主要成果:
- 大多数CAMS变量每值都包含不到7位的真实信息.
- 与64位浮动相比,实现了17×的数据压缩系数,保留了99%的真实信息.
- 结合四维压缩,得到了超过60×的因数.
- 绕过非信息位方便无损压缩,并编码数据不确定性.
结论:
- 由于固有的相关性,天气和气候数据具有很高的可压缩性.
- 一种新的数据压缩方法有效地减少了存储,同时保留了关键信息.
- 拟议的压缩图灵测试可以指导优化用于天气和气候预报的最终用途应用.
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