xskillscore.rmse

xskillscore.rmse(a, b, dim=None, weights=None, skipna=False, keep_attrs=False)

Root Mean Squared Error.

\[\mathrm{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(a_{i} - b_{i})^{2}}\]
Parameters:

  • a (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • b (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • dim (str, list) – The dimension(s) to apply the rmse along. Note that this dimension will be reduced as a result. Defaults to None reducing all dimensions.

  • weights (xarray.Dataset or xarray.DataArray or None) – Weights matching dimensions of dim to apply during the function.

  • skipna (bool) – If True, skip NaNs when computing function.

  • keep_attrs (bool) – If True, the attributes (attrs) will be copied from the first input to the new one. If False (default), the new object will be returned without attributes.

Returns:

Root Mean Squared Error.

Return type:

xarray.Dataset or xarray.DataArray

See also

sklearn.metrics.mean_squared_error

References

https://en.wikipedia.org/wiki/Root-mean-square_deviation

Examples

>>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
>>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
>>> xs.rmse(a, b, dim="time")
<xarray.DataArray (x: 3, y: 3)> Size: 72B
array([[0.30366872, 0.5147618 , 0.57410211],
       [0.2963848 , 0.37177283, 0.40563885],
       [0.55686111, 0.38189299, 0.21317579]])
Dimensions without coordinates: x, y