What is your issue?

The example code in the documentation for the Dataset class (e.g., here) is probably clear to those who study Earth and Atmospheric Sciences, but it makes no sense to me. Here is the code:

np.random.seed(0)
temperature = 15 + 8 * np.random.randn(2, 2, 3)
precipitation = 10 * np.random.rand(2, 2, 3)
lon = [[-99.83, -99.32], [-99.79, -99.23]]
lat = [[42.25, 42.21], [42.63, 42.59]]
time = pd.date_range("2014-09-06", periods=3)
reference_time = pd.Timestamp("2014-09-05")

ds = xr.Dataset(
    data_vars=dict(
        temperature=(["x", "y", "time"], temperature),
        precipitation=(["x", "y", "time"], precipitation),
    ),
    coords=dict(
        lon=(["x", "y"], lon),
        lat=(["x", "y"], lat),
        time=time,
        reference_time=reference_time,
    ),
    attrs=dict(description="Weather related data."),
)

To be clear, I understand each individual line of code, but I don't understand why there is both a latitude/longitude and an x/y in this example or how they are supposed to be related to each other (and there do not appear to be any additional details about this dataset's intended structure). Probably due to this lack of clarity I'm having a hard time wrapping my head around what the x/y coordinates and the lat/lon coordinates are supposed to demonstrate about xarray here, or how the x/y and lat/lon values are represented in the data structure. Are the x and y coordinates in a map projection of some kind? I have worked successfully with Datasets in the past, but as someone who doesn't work with geospatial data, I find myself more confused about Datasets after reading this example than before.

I suspect that all that is needed is a clear description of what these data are supposed to represent, how they are intended to be used, and how x/y and lat/lon are related. If someone can explain this to me, I'd be happy to submit a PR for the docs.