Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature
Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature
复制标题
使用统计模型压缩系综：全局 3D 时空温度算法
DOI：
10.1080/00401706.2015.1027068
复制
发表时间：
2016
期刊：
影响因子：
2.5
通讯作者：
M. Genton
中科院分区：
文献类型：
--
作者：
S. Castruccio;M. Genton
使用现代气候模式集合的主要挑战之一是所产生的数据越来越大，因此难以存储大量时空分辨信息。许多压缩算法可用于缓解这一问题，但由于它们被设计用于压缩通用科学数据集，因此它们不考虑气候模型输出的性质，并且它们仅压缩单个模拟。在这项工作中，我们提出了一个不同的，基于南极洲的方法，明确占的时空依赖性的数据在初始条件合奏的年度全球三维温度场。估计参数的集合是小的（与数据大小相比），并且可以被视为集合输出的基本结构的总结;因此，它可以用于瞬时再现集合中的温度场，从而大大节省存储和时间。该统计模型利用了数据的网格几何形状和跨处理器的并行化。因此，它在计算上是方便的，并且允许将非平凡模型拟合到具有包括1018个条目的协方差矩阵的10亿个数据点的数据集。本文的补充材料可在网上查阅。
One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific datasets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 1018 entries. Supplementary materials for this article are available online.