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| Home > Publications database > Compression of ERA5 meteorological reanalysis data and their application to simulations with the Lagrangian model for Massive Parallel Trajectory Calculations (MPTRAC v2.7) |
| Preprint | FZJ-2026-02272 |
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2025
Copernicus
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Please use a persistent id in citations: doi:10.5194/egusphere-2025-3147 doi:10.34734/FZJ-2026-02272
Abstract: Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.
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