Hi

For now, I found a workaround loading and renaming the problematic coordinates with netCDF4.Dataset(). Soon I will post this and other solutions for this model output in iuryt/FVCOMpy.

For now, you could try: ``` import xarray as xr from netCDF4 import Dataset

define year and month to be read

year = 2019 month = 5

we could use this to run a loop through the years/months we need

list problematic coordinates

drop_variables = ['siglay','siglev']

base url for openDAP server

url = "".join(["http://www.smast.umassd.edu:8080/thredds/dodsC/models/fvcom/", f"NECOFS/Archive/NECOFS_GOM/{year}/gom4_{year}{month:02d}.nc?"])

lazy load of the data

ds = xr.open_dataset(url,drop_variables=drop_variables,decode_times=False)

load data with netCDF4

nc = Dataset(url)

load the problematic coordinates

coords = {name:nc[name] for name in drop_variables}

function to extract ncattrs from Dataset()

get_attrs = lambda name: {attr:coords[name].getncattr(attr) for attr in coords[name].ncattrs()}

function to convert from Dataset() to xr.DataArray()

nc2xr = lambda name: xr.DataArray(coords[name],attrs=get_attrs(name),name=f'{name}_coord',dims=(f'{name}','node'))

merge xr.DataArray() objects

coords = xr.merge([nc2xr(name) for name in coords.keys()])

reassign to the main xr.Dataset()

ds = ds.assign_coords(coords) ```

Leaving it here in case someone fall into the same problem.