Hello everyone,
My research is in rainfall prediction using radar data. I wanted to classify rain echoes using the inbuilt function ‘pyart.retrieve.steiner_conv_strat()’ in py-art. My input file is an already gridded reflectivity filed stored in netcdf format. Is there anyway to give this as input instead of radar grid object? Thank you all in advance
@Mehzooz Is the input file just the reflectivity values? Does the file have coordinate information etc? If it has just that field, but still has the cartesian coordinates etc it should still work if you read the netcdf with pyart.io.read_grid. If it does not, you could always create a fake grid object using pyart.testing.make_empty_grid with your grid limits and then plug in the reflectivity field and give that a shot?
Cheers
@zssherman Thank you for your reply. Yes it does have coordinate infos but in lat and lon. Also when I checked the source code of function it does ask for variables ‘x’ and ‘y’ (which is not in my dataset) and “refl_field” for which the input should be a reflectivity field from a radar object. But I didn’t know about creating a fake grid. Thank you for bringing that to my attention. I will surely give it a try.