issue_comments: 1527050493

Thanks @dcherian for filling in the details.

I've digged up some more related issues: #2265, #3942, #4045

IIUC, #4684 did a great job to iron out much of these issues, but as it looks like only in the case when no NaT is within the time array (cc @spencerkclark). @christine-e-smit If you have no NaT in your time array then you can just omit encoding completely and Xarray will use int64 per default and your data should be fine on disk.

In the presence of NaT it looks like one workaround to circumvent that issue for the time being is to add the dtype in addition to _FillValue when writing out to zarr :

python encoding = { "time":{"_FillValue": time_fill_value, "dtype": np.int64} xr_ds.to_zarr(location, encoding=encoding) }

One note to this: Xarray is deducing the units from the current time data. So for the above example it will result in 'days since 2023-01-02 00:00:00' where days would now be the resolution in the file. If you want the resolution to be nanoseconds on disk units would need to be added to the encoding.

python encoding = { "time":{"_FillValue": time_fill_value, "dtype": np.int64, 'units': 'nanoseconds since 2023-01-02'} } xr_ds.to_zarr(location, encoding=encoding)

@christine-e-smit It would be great if you could confirm that from your side (some sanity check needed on my side).