期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 113, 期 521, 页码 56-67出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2017.1395339
关键词
Gaussian process; Half-spectral; Nonstationary; Spatial-temporal data; SPDE
资金
- Yellowstone by NCAR's Computational and Information Systems Laboratory - National Science Foundation [ark:/85065/d7wd3xhc]
- NSF Research Network on Statistics in the Atmosphere and Ocean Sciences (STATMOS) [DMS-1106862]
- National Science Foundation [1406016, 1613219]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1613219] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1406016] Funding Source: National Science Foundation
Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus it is important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. We decompress the data by computing conditional expectations and conditional simulations from the model given the summary statistics. Conditional expectations represent our best estimate of the original data but are subject to oversmoothing in space and time. Conditional simulations introduce realistic small-scale noise so that the decompressed fields are neither too smooth nor too rough compared with the original data. Considerable attention is paid to accurately modeling the original dataset1 year of daily mean temperature dataparticularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers. Supplementary materials for this article are available online.
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