I want to create an Xarray DataArray with the following coordinates using a series of numpy arrays for annual time series data (let's say it's temperature over a uniform 1500X1500 matrix).

('time', 'lon', 'lat')
Coordinates:

* time (time) datetime64[ns] 2000-12-31 2001-12-31 ... 2020-12-31
* lon (lon) float64 -19.98 -19.93 -19.88 -19.82 ... 54.88 54.93 54.98
* lat (lat) float64 39.97 39.92 39.87 39.82 ... -34.88 -34.93 -34.98

The original ('raw') time series data I am using is stored as separate files with the filename denoting each year in the time series (i.e, there is no time information provided within the data files themselves, just in the name, temp2000.xxx, temp2001.xxx, etc). I imported each of these individual data files into separate numpy arrays which have a spatial dimension (corresponding to lat/lon above) but no time dimension other than the variable name I assigned.

I am trying to figure out how to combine all these numpy arrays into one multi-dimensional xarray DataArray with the lat/lon of the numpy arrays and the time defined by a time variable (taken from file name).

This is probably straightforward but I can't wrap my head around it.

```
temp2000 = np.random.rand(1500, 1500)

xll = -20.0
xur = 55.0
yll = -35.0
yur = 40.0
cellsize = 0.1

lon_tup = np.arange(xll, xur, cellsize) + (cellsize / 2)
lat_tup = np.arange(yll, yur, cellsize)
lat_tup = lat_tup[::-1] + (cellsize / 2)
time2 = pd.date_range("2000-01-01", freq="Y", periods=21)

ds = xr.DataArray(
            coords=[time2, lat_tup, lon_tup], dims=["time", "lat", "lon"])

ds["Temperature_2000"] = (["time", "lat", "lon"], temp2000)

```

The DataArray is created just fine but obviously the numpy array can't be added because it lacks the "time" dimension. Can I force a time dimension via a separate step? Example is just for one time step (2000) with dummy data for illustration purposes.