Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 24
Articles | Volume 17, issue 24
https://doi.org/10.5194/gmd-17-8909-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8909-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 24
Development and technical paper
 | 
19 Dec 2024
Development and technical paper |  | 19 Dec 2024

The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11

Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig

Data sets

ERA5 hourly data on pressure levels from 1959 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

Model code and software

wavestoweather/enstools-encoding: Release v2022.11.1 Oriol Tintó Prims https://doi.org/10.5281/zenodo.7327685

wavestoweather/enstools-compression: Release v2023.11.1 Oriol Tintó Prims https://doi.org/10.5281/zenodo.7327682

The effect of lossy compression of numerical weather prediction data on data analysis: software to reproduce figures using enstools-compression Oriol Tintó Prims https://doi.org/10.5281/zenodo.10998604

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Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called enstools-compression that makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
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