Introduction to handling raster time series in Julia, Python and R
This tutorial will showcase how to work with raster data efficiently. The analysis will be shown in Julia, Python and R to showcase the similarities and differences in handling raster data in these ecosystems.
In this tutorial we are going to use the COSMO REA reanalyis near surface air temperature data. The data is an reanalysis dataset on a 6km by 6km grid. We are going to use the monthly average values, but the data is also avialable with an hourly or daily temporal resolution. The data was produced in the GRIB format but was converted to NetCDF files in the NFDI4Earth Pilot.
Raster Data Analysis in Python
