It might be related to the issue, but the code below is for a different GES DISC dataset from a different server goldsmr5 and the code also break for parallel=True. The same code works well for parallel=False.

```python import xarray as xr import pandas as pd

list daily timestamps from a starting date to an end date

dts = pd.date_range(start = "2013-01-01", end = "2022-12-01", freq = "D")

base url for the files

base = "https://goldsmr5.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2I3NPASM.5.12.4/"

prefix of the files

prefix = "MERRA2_400.inst3_3d_asm_Np"

function to construct the url of the files

url = lambda d: f"{base}{d.year}/{d.month:02d}/{prefix}.{d.year}{d.month:02d}{d.day:02d}.nc4"

list of urls for the files

fnames = [url(dti) for dti in dts]

lets try to load the first 5 files

ds = xr.open_mfdataset(fnames[:5], parallel=True) ```

The error:

python OSError: [Errno -51] NetCDF: Unknown file format: b'https://goldsmr5.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2I3NPASM.5.12.4/2013/01/MERRA2_400.inst3_3d_asm_Np.20130105.nc4'

Ok, that makes sense. I suspected that that was the case, but wanted to create an issue just in case. I think that we can close this now.

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.

actually, did you try !pip install xarray[complete]?

That worked! Thanks!

As I am using Google Colab I simply installed zarr and xarray using pip (as they suggest) and tried to run the command. python !pip install zarr xarray

Maybe this is something with the way Colab setup things, but it would be nice to have a way to make it work. I am planning to use it in a course and Colab helps to save time on the installing process.

I can tackle this one, if no one does that before, I just have to separate a time in my week schedule for open-source projects.