Compression of meteorological reanalysis data files and their application to Lagrangian transport simulations 

Computer performance has increased immensely in recent years, but the ability to store data has hardly increased at all. The current version of meteorological reanalysis data ERA5 provided by the European Centre of Medium-Range Weather Forecasts (ECMWF) has 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) and investigate how the use of compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analysed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations were derived when using ZFP with the largest compression. Using a less strong compression we could reduce the transport deviation and still derive a significant compression. Since ZSTD is a lossless compressor, we derive no transport deviations at all when using these compressed files, but do not loose much disk space using this compressor (reduction of ∼20%). The best result concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50%, but transport deviations do not exceed 40 km in the free troposphere and are even lower in the upper troposphere and stratosphere. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of meteorological reanalyses data files we can overcome the challenges of high demand of disk space from these data sets.