```

input array

array = xr.DataArray([1,3,6,np.nan,19,20,13], dims=['time'], coords=[pd.date_range('2023-06-05 00:00','2023-06-05 06:00',freq='H')])

array xarray.DataArray(time: 7 array([ 1., 3., 6., nan, 19., 20., 13.]) Coordinates: time (time) datetime64[ns] 2023-06-05 ... 2023-06-05T06: Indexes: (1) Attributes: (0)

however the integrated value ends up as a NaN

array.integrate('time') xarray.DataArray array(nan) Coordinates: (0) Indexes: (0) Attributes: (0)

if one still wanted to know the integrated values for where there were values it would essentially by like integrating the separate chunks for where the valid values existed

first chunk

array.isel(time=slice(0,3)).integrate('time') xarray.DataArray array(2.34e+13) Coordinates: (0) Indexes: (0) Attributes: (0)

second chunk

array.isel(time=slice(4,7)).integrate('time') xarray.DataArray array(1.296e+14) Coordinates: (0) Indexes: (0) Attributes: (0)

and then the sum would be the fully integrated area

``` @dcherian I essentially was wondering whether it was possible for a skipna argument or some kind of NaN handling to be implemented that would allow users to avoid integrating in chunks due to the presence of NaNs. I do not work in dev so I would not know how to implement this, but I thought I'd see if others had thoughts.