Towards predicting optimal compression settings for spatiotemporal earth system datasets
d573432
The data storage burden resulting from CESM simulations continues to grow, and lossy data compression methods can alleviate this burden, provided that key climate variables are not altered to the point of affecting scientific conclusions. We aim to facilitate the adoption of lossy compression in the geoscience community by making it easier to identify optimal compression parameter settings, and this dataset was generated to support that effort. In particular, this data was used to develop and evaluate an approach for creating models to predict optimal compression parameter settings and identify features that are useful for determining the compressibility of a particular output variable. We provide a subset of daily 2D atmospheric variables from the CESM1 LENS data compressed with the popular ZFP compressor. We also provide compression metrics for all of the compressed datasets and feature values for the original data (that can be used for model training, for example).
dataset
https://gdex.ucar.edu/datasets/d573432/
protocol: https
name: Dataset Description
description: Related Link
function: information
https://gdex.ucar.edu/datasets/d573432/dataaccess/
protocol: https
name: Data Access
description: Related Link
function: download
climatologyMeteorologyAtmosphere
dataset
revision
2021-03-30
CESM > NCAR Community Earth System Model
revision
2026-10-28
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE
revision
2026-10-28
-180.0
180.0
90.0
-90.0
1920-01-01T0000+00
1974-10-08T0000+00
publication
2026-11-09
notPlanned
Creative Commons Attribution 4.0 International License
None
pointOfContact
NSF NCAR Geoscience Data Exchange
name: NSF NCAR Geoscience Data Exchange
description: The Geoscience Data Exchange (GDEX), managed by the Computational and Information Systems Laboratory (CISL) at NSF NCAR, contains a large collection of meteorological, atmospheric composition, and oceanographic observations, and operational and reanalysis model outputs, integrated with NSF NCAR High Performance Compute services to support atmospheric and geosciences research.
function: download
pointOfContact
2026-11-09T23:19:55Z