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829426650 MDU6SXNzdWU4Mjk0MjY2NTA= 5023 Unable to load multiple WRF NetCDF files into Dask array on pangeo porterdf 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 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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   completed xarray 13221727 issue

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