issue_comments: 670808763

I decided to try out this PR on some of the data files we are working with at my data office. In our datasets we have per variable quality flag information per variable uncertainty information. The CF way of tying all these together is via the ancillary_variables attribute. This PR pulls all these out into the Dataset coordinates. Since in the xarray data model (right now) the coordinates apply to an entire dataset, this feels inappropriate and maybe even breaking. The ancillary_variables attribute is not used in CF grid mapping or bounds as far as I can tell.

Here is an example using this PR (note that all the varN type names will be replaced with better variable names before we publish these):

```python In [1]: import xarray as xr

In [2]: ds = xr.open_dataset("examples/converted/06AQ19840719.nc")

In [3]: ds Out[3]: <xarray.Dataset> Dimensions: (N_LEVELS: 24, N_PROF: 38) Coordinates: var1_qc (N_PROF, N_LEVELS) float32 ... var4_qc (N_PROF, N_LEVELS) float32 ... var5_qc (N_PROF, N_LEVELS) float32 ... var6_qc (N_PROF, N_LEVELS) float32 ... var7_qc (N_PROF, N_LEVELS) float32 ... var8_qc (N_PROF, N_LEVELS) float32 ... var9_qc (N_PROF, N_LEVELS) float32 ... var10_qc (N_PROF, N_LEVELS) float32 ... var11_qc (N_PROF, N_LEVELS) float32 ... var12_qc (N_PROF, N_LEVELS) float32 ... var13_qc (N_PROF, N_LEVELS) float32 ... var14_qc (N_PROF, N_LEVELS) float32 ... var15_qc (N_PROF, N_LEVELS) float32 ... pressure (N_PROF, N_LEVELS) float64 ... latitude (N_PROF) float64 ... longitude (N_PROF) float64 ... time (N_PROF) datetime64[ns] ... expocode (N_PROF) object ... station (N_PROF) object ... cast (N_PROF) int8 ... sample (N_PROF, N_LEVELS) object ... Dimensions without coordinates: N_LEVELS, N_PROF Data variables: var0 (N_PROF) object ... var1 (N_PROF, N_LEVELS) object ... var2 (N_PROF) float32 ... var3 (N_PROF, N_LEVELS) float32 ... var4 (N_PROF, N_LEVELS) float32 ... var5 (N_PROF, N_LEVELS) float32 ... var6 (N_PROF, N_LEVELS) float32 ... var7 (N_PROF, N_LEVELS) float32 ... var8 (N_PROF, N_LEVELS) float32 ... var9 (N_PROF, N_LEVELS) float32 ... var10 (N_PROF, N_LEVELS) float32 ... var11 (N_PROF, N_LEVELS) float32 ... var12 (N_PROF, N_LEVELS) float32 ... var13 (N_PROF, N_LEVELS) float32 ... var14 (N_PROF, N_LEVELS) float32 ... var15 (N_PROF, N_LEVELS) float32 ... Attributes: Conventions: CF-1.8 CCHDO-0.1.dev157+g52933e0.d20200707 ```

This looks especially confusing when you ask for one specific variable:

python In [15]: ds.var6 Out[15]: <xarray.DataArray 'var6' (N_PROF: 38, N_LEVELS: 24)> array([[33.3965, 33.5742, 34.8769, ..., 34.9858, 34.9852, nan], [33.1129, 34.0742, 34.6595, ..., nan, nan, nan], [32.5328, 33.2687, 34.2262, ..., nan, nan, nan], ..., [35.0686, 35.09 , 35.1415, ..., nan, nan, nan], [35.0303, 35.0295, 35.0715, ..., nan, nan, nan], [35.0682, 35.0756, 35.0622, ..., nan, nan, nan]], dtype=float32) Coordinates: var1_qc (N_PROF, N_LEVELS) float32 ... var4_qc (N_PROF, N_LEVELS) float32 ... var5_qc (N_PROF, N_LEVELS) float32 ... var6_qc (N_PROF, N_LEVELS) float32 ... var7_qc (N_PROF, N_LEVELS) float32 ... var8_qc (N_PROF, N_LEVELS) float32 ... var9_qc (N_PROF, N_LEVELS) float32 ... var10_qc (N_PROF, N_LEVELS) float32 ... var11_qc (N_PROF, N_LEVELS) float32 ... var12_qc (N_PROF, N_LEVELS) float32 ... var13_qc (N_PROF, N_LEVELS) float32 ... var14_qc (N_PROF, N_LEVELS) float32 ... var15_qc (N_PROF, N_LEVELS) float32 ... pressure (N_PROF, N_LEVELS) float64 ... latitude (N_PROF) float64 ... longitude (N_PROF) float64 ... time (N_PROF) datetime64[ns] ... expocode (N_PROF) object ... station (N_PROF) object ... cast (N_PROF) int8 ... sample (N_PROF, N_LEVELS) object ... Dimensions without coordinates: N_PROF, N_LEVELS Attributes: whp_name: CTDSAL whp_unit: PSS-78 standard_name: sea_water_practical_salinity units: 1 reference_scale: PSS-78