Investigating Read Performance of Python and NetCDF When Using HPC Parallel Filesystems

New methods need to be developed to handle the increasing size of data sets in atmospheric science - traditional analysis scripts often inefficiently read and process the data. NetCDF4 is a common file format used in atmospheric and ocean sciences, and Python is widely used in atmospheric and ocean science data analysis. The aim of this work is to provide insight into which read patterns and sizes are most effective when using the netCDF4-python library. Quantitative information on these would be useful information for scientists, library developers, and data managers.