Geo2grid software accesses SatPy libraries that allow for the reading of level-1c netcdf files holding FCI (Flexible Combined Imager) data from the latest operational Meteosat satellite (Meteosat-12). So if you can access to that output, you can create beautiful images as the ones shown above. The RGB shows a storm moving into western Europe. The Red/Orange in the image is associated with the Potential Vorticity Anomaly helping to support the storm (a second Potential Vorticity Anomaly is apparent over the central Mediterranean, and there seems to be one over the Arabian Peninsula too — maybe that’s a Westerly Disturbance for India later this weekend) To create the image above, I first moved all 40 (!) image sectors holding the data valid at 0000 UTC on 30 January 2025 to my computer that holds the geo2grid software (downloadable from this link). The geo2grid documentation includes a section on readers, including the FCI Level-1c reader.

The two geo2grid commands to create the image above are shown below. One creates the .tif file, one adds coastlines to the image. In geo2grid call, those last 4 wildcards (????) refer to the 40 different sectors (that is, 0001, 0002, 0003, 0004, … , 0039, 0040) that hold the data. The 0001 in the filename refers to the 1st image of the day.

./geo2grid.sh -r fci_l1c_nc -w geotiff -p airmass -f /pathtoL1CData/W*FDHSI*_0001_????.nc
./add_coastlines.sh --add-coastlines --coastlines-resolution f MTG-I1_FCI_airmass_20250130_000000_mtg_fci_fdss_2km.tif 

As with other geo2grid commands, one can create a region into which you can subsect the data so you can focus on a particular region. This was done below to focus on parts of Europe.

/p2g_grid_helper.sh Europe4 10.0 45.0 4000 -4000 1440 1120 > Europe4.yaml
./geo2grid.sh -r fci_l1c_nc -w geotiff -p airmass -g Europe4 --grid-configs ./Europe4.yaml -f /pathtoL1CData/W*FDHSI*_0001_????.nc 
 ./add_coastlines.sh --add-coastlines --coastlines-resolution f MTG-I1_FCI_airmass_20250130_000000_Europe4.tif 

The output from that series of calls is shown below.

Thanks to EUMETSAT for sharing the data that allowed the creation of these beautiful images!

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