Iris is available in many of the existing conda environments. If you are interested in trying that out I am happy to help. I don’t have access to the k10 group though so we will have to find a way to pass over files.
I also tried running the um2nc command as you did:
um2nc dj863a.pm0269dec test.nc
And this seemed to work for me. I could open the test.nc file in ncview and in xconv ok. But there were some warnings and perhaps not every field was converted correctly.
I also ran the alternative script method which is a script called conv2nc.tcl, which is also copied into that folder.
Running the following:
conv2nc.tcl dj863a.pm0269dec
Creates a netcdf file called dj863a.pm0269dec.nc
this also seems ok (variable names are a bit different). I did notice that the file sizes ended up being a bit different. I removed the netcdf compression from each of the outputs to get a proper comparison of file sizes. The resulting files are saved as:
out_convsh.nc
out_um2nc.nc
(Both have compression removed.) The “out_convsh.nc” file from my script comes out at 40 MB, while the out_um2nc.nc file comes out as 21 MB. The original UM file is 40 MB. This suggests that a fair portion of the data was never converted properly by um2nc.
I’ve tried reconverting the um file with um2nc and also ran into the same segmentation fault when opening the result with xconv. This appears to be a limitation with xconv, which can crash when a variable’s “long_name” attribute is too long. The netCDF file test.nc contains two variables with long_name attributes of length 110, field1221 and field1225 with long names product_of_effective_radius_of_stratiform_cloud_liquid_water_particle_and_stratiform_cloud_liquid_water_area_f and product_of_effective_radius_of_convective_cloud_liquid_water_particle_and_convective_cloud_liquid_water_area_f respectively.