2. Data formats

One of the most convenient features of earthkit is format-agnosticism - that is, earthkit helps you focus on analysing and visualising your data using the same tools and workflows no matter the format.

In this section, we will explore visualising data from different file formats in earthkit-maps.

import earthkit.data
import earthkit.maps

GRIB and NetCDF

Two of the most commonly used data formats for gridded weather and climate science data are GRIB and NetCDF. This example demonstrates that the features of earthkit-maps work equivalently with both data formats.

Let’s start by getting the same sample data we used in the introduction examples - ERA5 monthly average temperature data in GRIB format.

earthkit.data.download_example_file("era5-2m-temperature-dec-1993.grib")
grib_data = earthkit.data.from_source("file", "era5-2m-temperature-dec-1993.grib")

Now let’s convert this data to NetCDF format, and open the result using earthkit-data.

grib_data.to_xarray().to_netcdf("era5-2m-temperature-dec-1993.nc")
netcdf_data = earthkit.data.from_source("file", "era5-2m-temperature-dec-1993.nc")

Now let’s try calling earthkit.maps.quickplot on both pieces of data, and see what the result looks like. Note that in this case we’re passing show=False to quickplot, which allows us to intercept the generated Chart object and modify the titles so that we can more easily identify which data is GRIB and which is NetCDF.

chart = earthkit.maps.quickplot(grib_data, netcdf_data, units="celsius", disjoint=True, show=False)
chart[0].title("GRIB {variable_name} - {time:%B %Y}")
chart[1].title("NetCDF {variable_name} - {time:%B %Y}")
chart.show()

As expected, they look identical! The automatic styling and metadata detection work with the same keys for each data format, and the plotted image is exactly the same for each.

GeoTIFF

Coming soon!