Wavelet data compression for archiving high-resolution icosahedral model data

With the increase of the resolution of global circulation models, it becomes ever more important to develop highly effective solutions to archive the huge datasets produced by those models. While lossless data compression guarantees the accuracy of the restored data, it can only achieve limited reduction of data size. Wavelet transform based data compression offers significant potentials in data size reduction, and it has been shown very effective in transmitting data for remote visualizations. However, for data archive purposes, a detailed study has to be conducted to evaluate its impact to the datasets that will be used in further numerical computations. In this study, we carried out two sets of experiments for both summer and winter seasons. An icosahedral grid weather model and a highly efficient wavelet data compression software were used for this study. Initial conditions were compressed and input to the model to run to 10 days. The forecast results were then compared to those forecast results from the model run with the original uncompressed initial conditions. Several visual comparisons, as well as the statistics of numerical comparisons are presented. These results indicate that with specified minimum accuracy losses, wavelet data compression achieves significant data size reduction, and at the same time, it maintains minimum numerical impacts to the datasets. In addition, some issues are discussed to increase the archive efficiency while retaining a complete set of meta data for each archived file.