issue_comments

Great. Thanks for following up @porterdf

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?

Does it work if you pass concat_dim="XTIME"?

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]') ```

I suspect there is at least one netCDF file with inconsistent metadata, e.g., without a Time dimension. If you can find and fix that dataset (or otherwise deal with it in whatever special way is required), then that would resolve the issue. In my experience, looping through files (rather than using open_mfdataset) is definitely helpful in this regard because you can verify that each file has the expected metadata.

The only reason why I can imagine this behavior might be different in GCP rather than on your workstation would be if you are using inconsistent package version.

Note: In general, for multi-file netCDF -> Zarr workflows you might check out pangeo-forge: https://github.com/pangeo-forge/pangeo-forge