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

Yu, Zhaoyuan; Li, Dongshuang; Zhang, Zhengfang; Luo, Wen; Liu, Yuan; Wang, Zengjie; Yuan, Linwang

doi:https://doi.org/10.5194/gmd-14-875-2021

Articles | Volume 14, issue 2

https://doi.org/10.5194/gmd-14-875-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-875-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 2

Development and technical paper

|

11 Feb 2021

Development and technical paper |

| 11 Feb 2021

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

Zhaoyuan Yu, Dongshuang Li, Zhengfang Zhang, Wen Luo, Yuan Liu, Zengjie Wang, and Linwang Yuan

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Final revised paper (published on 11 Feb 2021)
Preprint (discussion started on 02 Jun 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'review of "Adaptive lossy compression of climate model data based on hierarchical tensor with Adaptive-HGFDR (v1.0)"', Anonymous Referee #1, 01 Jul 2020
RC2: 'Adaptive lossy compression of climate model data based on hierarchical tensor', Anonymous Referee #2, 27 Jul 2020
AC1: 'Response to reviewers', Zhaoyuan Yu, 26 Aug 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Zhaoyuan Yu on behalf of the Authors (26 Aug 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (31 Aug 2020) by Patrick Jöckel

RR by Anonymous Referee #2 (19 Oct 2020)

RR by Anonymous Referee #3 (04 Nov 2020)

ED: Reconsider after major revisions (13 Nov 2020) by Patrick Jöckel

AR by Zhaoyuan Yu on behalf of the Authors (23 Dec 2020) Manuscript

ED: Publish as is (04 Jan 2021) by Patrick Jöckel

AR by Zhaoyuan Yu on behalf of the Authors (05 Jan 2021)

Short summary

Few lossy compression methods consider both the global and local multidimensional coupling correlations, which could lead to information loss in data compression. Here we develop an adaptive lossy compression method, Adaptive-HGFDR, to capture both the global and local variation of multidimensional coupling correlations and improve approximation accuracy. The method can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity.