issue_comments

Thanks @dcherian ```

ds = xr.open_mfdataset(NCs_urls, engine='netcdf4', parallel=True, concat_dim='XTIME', )

ValueError: Could not find any dimension coordinates to use to order the datasets for concatenation ```

So it doesn't work, but perhaps that's not surprising give that 'XTIME' is a coordinate, but 'Time' is the dimension (one of WRF's quirks related to staggered grids and moving nests).

```

print(ds.coords)

Coordinates: XLAT (Time, south_north, west_east) float32 dask.array<chunksize=(8, 1035, 675), meta=np.ndarray> XLONG (Time, south_north, west_east) float32 dask.array<chunksize=(8, 1035, 675), meta=np.ndarray> XTIME (Time) datetime64[ns] dask.array<chunksize=(8,), meta=np.ndarray> XLAT_U (Time, south_north, west_east_stag) float32 dask.array<chunksize=(8, 1035, 676), meta=np.ndarray> XLONG_U (Time, south_north, west_east_stag) float32 dask.array<chunksize=(8, 1035, 676), meta=np.ndarray> XLAT_V (Time, south_north_stag, west_east) float32 dask.array<chunksize=(8, 1036, 675), meta=np.ndarray> XLONG_V (Time, south_north_stag, west_east) float32 dask.array<chunksize=(8, 1036, 675), meta=np.ndarray> ```

As such, I'm following the documentation to add a preprocessor ds.swap_dims({'Time':'XTIME'}), which works as expected.

Thanks for everyone's help! Shall I close this? (as it was never actually an issue?

Thanks for the great suggestion @shoyer - your suggestion to loop through the netCDF files is working well in Dask using the following code:

``` import xarray as xr import gcsfs from tqdm.autonotebook import tqdm xr.set_options(display_style="html");

fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='r',cache_timeout = 0) NCs = fs.glob('gs://ldeo-glaciology/AMPS/WRF_24/domain_02/*.nc') url = 'gs://' + NCs[0] openfile = fs.open(url, mode='rb') ds = xr.open_dataset(openfile, engine='h5netcdf',chunks={'Time': -1}) for i in tqdm(range(1, 8)): url = 'gs://' + NCs[i] openfile = fs.open(url, mode='rb') temp = xr.open_dataset(openfile, engine='h5netcdf',chunks={'Time': -1}) ds = xr.concat([ds,temp],'Time') ```

However, I am still confused why open_mfdataset was not parsing the Time dimension - the concatenated DataSet using the looping method above appears to have a time dimension compatible with datetime64[ns].

```

ds.coords['XTIME'].compute()

xarray.DataArray'XTIME'Time: 8 array(['2019-01-01T03:00:00.000000000', '2019-01-01T06:00:00.000000000', '2019-01-01T09:00:00.000000000', '2019-01-01T12:00:00.000000000', '2019-01-01T15:00:00.000000000', '2019-01-01T18:00:00.000000000', '2019-01-01T21:00:00.000000000', '2019-01-02T00:00:00.000000000'], dtype='datetime64[ns]') ```