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| Hauptseite > Externe Publikationen > Vita Publikationen > Multiresolution-based grid adaptation for the compression of ERA5 meteorological reanalysis data in MPTRAC v2.7 |
| Hauptseite > Externe Publikationen > Vita Publikationen > Multiresolution-based grid adaptation for the compression of ERA5 meteorological reanalysis data in MPTRAC v2.7 |
| Preprint | FZJ-2026-02273 |
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2026
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Please use a persistent id in citations: doi:10.31223/X5GF4R
Abstract: The continuous increase in computational power comes with a corresponding demand for storage space. However, the ability to store data has hardly increased in recent years. This makes the demand for efficient storage solutions even more pressing, especially for meteorological reanalysis data. The current European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data already poses a major challenge for the community, but with the upcoming ERA6 reanalysis data, which will have an even higher resolution, significantly more storage space will be needed. An efficient way to store data is to use either lossy or lossless data compression methods to reduce storage requirements. To compress the meteorological data, we perform a multiresolution-analysis using multiwavelets on a hierarchy of nested grids. Since the local differences become negligibly small in regions where the data are locally smooth, we apply hard thresholding for data compression. This results in a high compression rate while preserving the accuracy of the original data. This strategy has been implemented into the Lagrangian model for Massive-Parallel Trajectory Calculation (MPTRAC) and has been successfully applied to ERA5 reanalysis data. Compression rates ranging from 1.6 to 12.6 can be achieved while at the same time maintaining the accuracy of the data within acceptable error limits. This leads to a reduction in storage of up to 93%, for example, reducing the file size of an ERA5 data file corresponding to a time instant from 4.9 GB to 389 MB. This renders the multiresolution-based grid adaptation a particularly suitable and effective approach for addressing the data storage challenges in atmospheric transport simulations.
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