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Preprints
https://doi.org/10.5194/egusphere-2024-753
https://doi.org/10.5194/egusphere-2024-753
25 Apr 2024
 | 25 Apr 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

Abstract. The increasing amount of data in meteorological science requires effective data reduction methods. Our study demonstrates the use of advanced scientific lossy compression techniques to significantly reduce the size of these large datasets, achieving reductions ranging from 5x to over 150x, while ensuring data integrity is maintained. A key aspect of our work is the development of the 'enstools-compression' Python library. This user-friendly tool simplifies the application of lossy compression for Earth scientists and is integrated into the commonly used NetCDF file format workflows in atmospheric sciences. Based on the HDF5 compression filter architecture, 'enstools-compression' is easily used in Python scripts or via command line, enhancing its accessibility for the scientific community. A series of examples, drawn from current atmospheric science research, shows how lossy compression can efficiently manage large meteorological datasets while maintaining a balance between reducing data size and preserving scientific accuracy. This work addresses the challenge of making lossy compression more accessible, marking a significant step forward in efficient data handling in Earth sciences.

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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
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024,https://doi.org/10.5194/gmd-17-8909-2024, 2024
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Advanced compression techniques can drastically reduce the size of meteorological datasets (by...
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