Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 4
Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1519-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1519-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 4
Development and technical paper
 | 
20 Feb 2026
Development and technical paper |  | 20 Feb 2026

A new sub-chunking strategy for fast netCDF-4 access in local, remote and cloud infrastructures, chunkindex V1.1.0

Cédric Penard, Flavien Gouillon, Xavier Delaunay, and Sylvain Herlédan

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Cited articles

Ambatipudi, S. and Byna, S.: A comparison of HDF5, Zarr, and netCDF4 in performing common I/O operations, arXiv [preprint], https://doi.org/10.48550/arXiv.2207.09503, 2022. 
Barciauskas, A., Mandel, A., Barron, K., and Deziel, Z.: Cloud-Optimized Geospatial Formats Guide (cloudnativegeo.org) https://web.archive.org/web/20250413102259/https://guide.cloudnativegeo.org/ (last access: April 2025), 2023. 
Carval, T., Bodere, E., Meillon, J., Woillez, M., Le Roux, J. F., Magin, J., and Odaka, T.: Enabling simple access to a data lake both from HPC and Cloud using Kerchunk and Intake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17494, https://doi.org/10.5194/egusphere-egu23-17494, 2023. 
Clementi, E., Aydogdu, A., Goglio, A. C., Pistoia, J., Escudier, R., Drudi, M., Grandi, A., Mariani, A., Lyubartsev, V., Lecci, R., Cretí, S., Coppini, G., Masina, S., and Pinardi, N.: Mediterranean Sea Physical Analysis and Forecast (CMEMS MED-Currents, EAS6 system) (Version 1), Copernicus Monitoring Environment Marine Service (CMEMS) [data set], https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS8, 2021. 
CNES: Data processing centre of the french space agency CNES, https://web.archive.org/web/20241206205004/https://cnes.fr/en/projects/centre-de-calcul, last access: December 2024.  
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In this work, we propose a novel approach, called chunkindex, that was designed to improve the access to time series from native NetCDF (Network Common Data Form) files in the cloud. The advantage of our approach is that it keeps existing data as they are without requiring any reformatting. The idea is to reduce the amount of data read from the NetCDF file by creating sub-chunks that allow extracting smaller portions of compressed data without reading the entire chunk.
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