Minisymposium Presentation

The storage requirements for meteorological reanalysis data have increased significantly in recent years. To address the challenges of handling these large data sets, efficient compression techniques are required. In addition, the error of the compressed data should be as small as possible. Although lossless compression algorithms exist, the resulting data are still too large. Conversely, lossy compression formats allow a small file size, but are often not able to control the error relative to the original data. We propose a multiresolution-based grid adaptation as an alternative method for lossy compression. To do this, we perform a multiresolution analysis using multiwavelets on ahierarchy of nested grids. This method provides us with local information on the differences between successive refinement levels. Since smooth regions have small local differences, we apply hard thresholding to resolve these regions on a coarser grid. Thus, the data is projected onto an adaptive grid where only regions with steep gradients or discontinuities have a high resolution, which significantly reduces the file size. We present how data compression is achieved by applying multiresolution-based grid adaptation using ERA5 meteorological reanalysis data. We additionally discuss the implementation of this method into the Lagrangian model for Massive-Parallel Trajectory Calculation (MPTRAC).