issue_comments

FYI, merged.time.encoding = {} before calling to_netcdf seems to avoid the RuntimeWarning.

There you go !

```python import numpy import pandas import tempfile import warnings import xarray

array1 = xarray.DataArray( numpy.random.rand(5), dims=['time'], coords={'time': pandas.to_datetime(['2018-01-01', '2018-01-01 00:01', '2018-01-01 00:02', '2018-01-01 00:03', '2018-01-01 00:04'])}, name='foo' )

array2 = xarray.DataArray( numpy.random.rand(5), dims=['time'], coords={'time': pandas.to_datetime(['2018-01-01 00:05', '2018-01-01 00:05:10', '2018-01-01 00:05:20', '2018-01-01 00:05:30', '2018-01-01 00:05:40'])}, name='foo' )

with tempfile.NamedTemporaryFile() as tmp: # save first array array1.to_netcdf(tmp.name) # reload it array1_reloaded = xarray.open_dataarray(tmp.name)

# the time encoding stores minutes as int, so seconds won't be allowed at next call of to_netcdf
assert array1_reloaded.time.encoding['dtype'] == numpy.int64
assert array1_reloaded.time.encoding['units'] == 'minutes since 2018-01-01 00:00:00'

merged = xarray.merge([array1_reloaded, array2])
array1_reloaded.close()

with warnings.catch_warnings():
    warnings.filterwarnings('error', category=RuntimeWarning)
    merged.to_netcdf(tmp.name)

```

@NotSqrt can you make a minimum working example for this? e.g., a netCDF file with problematic data, and associated code that writes a netCDF file with lost time resolution. That would really help us diagnose and solve this problem.

I faced this issue when switching from a concat to a merge.

The first merged dataset had a time dimension which encoding says {'calendar': 'proleptic_gregorian', 'dtype': dtype('int64'), 'units': 'minutes since 2017-08-20 00:00:00'}, which meant that the data from the second merged dataset could not be stored with a finer resolution than minutes.

If I try to store values like '2017-08-20 00:00:30', I get the warning xarray\conventions.py:1092: RuntimeWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs.

Maybe it is similar in your case: netcdf stored the data as 'hours since XXXX', so you lose the minutes.

@jhamman Here my example file ftp://ftp.irsn.fr/argon/Example

This is the warning I got when I wrote on my file with "to_necdf()" xarray\conventions.py:1060: RuntimeWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs for k, v in iteritems(variables))

@jhamman It's seems than the error appears only with a variable "rain" who commes from a previous created netcdf file, but I will try to provide you an example. tanks

@Scheibs - thanks for the report. Can you provide a simple, minimum working example (MWE)?