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Dataset: zfp0.5_b.e11.B20TRC5CNBDRD.f09_g16.030.cam.h1.PRECT.19200101-20051231.nc
Catalog: /thredds/catalog/files/d583147/catalog.html
dataFormatNetCDF
authorityedu.ucar.gdex
featureTypeGRID
dataSize39657113
idfiles/d583147/zfp0.5_b.e11.B20TRC5CNBDRD.f09_g16.030.cam.h1.PRECT.19200101-20051231.nc
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OpenDAP Data Access Access dataset through OPeNDAP using the DAP2 protocol.
DAP4 Data Access Access dataset using the DAP4 protocol.
NetcdfSubset Data Access A web service for subsetting CDM scientific grid datasets.
CdmRemote Data Access Provides index subsetting on remote CDM datasets, using ncstream.
CdmrFeature Data Access Provides coordinate subsetting on remote CDM Feature Datasets, using ncstream.
WCS Data Access Supports access to geospatial data as 'coverages'.
WMS Data Access Supports access to georegistered map images from geoscience datasets.
HTTPServer Data Access HTTP file download.
ISO Metadata Provide ISO 19115 metadata representation of a dataset's structure and metadata.
NCML Metadata Provide NCML representation of a dataset.
UDDC Metadata An evaluation of how well the metadata contained in the dataset conforms to the NetCDF Attribute Convention for Data Discovery (NACDD)

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Godiva3 Browser
default_viewer.ipynb Jupyter Notebook The TDS default viewer attempts to plot any Variable contained in the Dataset.
Documentation
Dates
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Publishers

Description:

  • Rights: Freely Available
  • summary: 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. This dataset was generated to evaluate the effects of two leading lossy compression algorithms, SZ and ZFP, on daily output data from the CESM-LENS dataset. In particular, it contains daily data for variables TS (surface temperature) and PRECT (precipitation rate) from the historical forcing period (1920-2005) for CESM-LENS ensemble member 30. The provided data has been compressed and reconstructed via two popular compressors: SZ 1.4.13 and ZFP 0.5.3 with a number of different absolute error tolerances. Errors due to compression can be determined by comparing these reconstructed files to the original CESM-LENS timeseries data, and statistical methods can evaluate the errors at different spatiotemporal scales. While both compression algorithms show promising fidelity with the original output, detectable artifacts are introduced even at relatively tight error tolerances.
  • NCAR GDEX - A Statistical Analysis of Lossily Compressed CESM-LENS Data(d583147)

Dates:

  • modified : 2025-08-19T19:23:02.956Z

Creators:

  • UCAR/NCAR/CISL

Publishers:

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