id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
829426650,MDU6SXNzdWU4Mjk0MjY2NTA=,5023,Unable to load multiple WRF NetCDF files into Dask array on pangeo,7237617,closed,0,,,5,2021-03-11T18:44:55Z,2021-04-28T19:16:44Z,2021-04-28T19:16:44Z,NONE,,,,"(Sorry if this is not correct place for this, obviously Pangeo repo is another option)

Working with @jkingslake, our immediate goal is to load many WRF history files (3 hourly in this case), currently stored in our public GCS, and write out to Zarr for public/open use.  We can get all of this working on local machine, but on us-central1-b `open_mfdataset` returns the following error `ValueError: Could not find any dimension coordinates to use to order the datasets for concatenation`.  I suspect this is related to non-standard, CF-incompliant dimensions in WRF (each model initialization will have multiple forecast period, i.e. 'minutes since 2019-12-31 00:00:00')

As this [gist](https://gist.github.com/porterdf/4d875e692b200abffa9fe7088a148321) shows, opening a single WRF file works as expected.  Is this a limitation of open_mfdatasets?  Alternatively, looping through each file and using `xr.concat` along dim='Time' works locally, but not on us-central1-b.

```
xr.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.8.6 | packaged by conda-forge | (default, Jan 25 2021, 23:21:18) 
[GCC 9.3.0]
python-bits: 64
OS: Linux
OS-release: 4.19.112+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.6
libnetcdf: 4.7.4

xarray: 0.16.2
pandas: 1.2.1
numpy: 1.20.0
scipy: 1.6.0
netCDF4: 1.5.5.1
pydap: installed
h5netcdf: 0.8.1
h5py: 3.1.0
Nio: None
zarr: 2.6.1
cftime: 1.4.1
nc_time_axis: 1.2.0
PseudoNetCDF: None
rasterio: 1.2.0
cfgrib: 0.9.8.5
iris: None
bottleneck: 1.3.2
dask: 2021.01.1
distributed: 2021.01.1
matplotlib: 3.3.4
cartopy: 0.18.0
seaborn: None
numbagg: None
pint: 0.16.1
setuptools: 49.6.0.post20210108
pip: 20.3.4
conda: None
pytest: None
IPython: 7.20.0
sphinx: 3.4.3
```
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