English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

. Lossy compression has been applied to the data compression oflarge-scale Earth system model data (ESMD) due to its advantages of a highcompression ratio. However, few lossy compression methods consider bothglobal and local multidimensional coupling correlations, which could lead toinformation loss in data approximation of lossy compression. Here, anadaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on thefoundation of a stream compression method for geospatial data called blockedhierarchical geospatial field data representation (Blocked-HGFDR). In addition, theoriginal Blocked-HGFDR method is also improved from the following perspectives.Firstly, the original data are divided into a series of data blocks of amore balanced size to reduce the effect of the dimensional unbalance ofESMD. Following this, based on the mathematical relationship between the compressionparameter and compression error in Blocked-HGFDR, the control mechanism isdeveloped to determine the optimal compression parameter for the givencompression error. By assigning each data block an independent compressionparameter, Adaptive-HGFDR can capture the local variation ofmultidimensional coupling correlations to improve the approximationaccuracy. Experiments are carried out based on the Community Earth SystemModel (CESM) data. The results show that our method has higher compressionratio and more uniform error distributions compared with ZFP andBlocked-HGFDR. For the compression results among 22 climate variables,Adaptive-HGFDR can achieve good compression performances for most fluxvariables with significant spatiotemporal heterogeneity and fast changing rate.This study provides a new potential method for the lossy compression of thelarge-scale Earth system model data.

Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)