diff --git a/uxarray/core/dataarray.py b/uxarray/core/dataarray.py
old mode 100644
new mode 100755
index 693b436de..5f5833a3f
--- a/uxarray/core/dataarray.py
+++ b/uxarray/core/dataarray.py
@@ -412,14 +412,17 @@ def nodal_average(self):
 
     # weighted_mean()
     # .weighted.mean()
+    # weights are only on grid - not supporting custom weights for now 
     def mean(self, dim=None, *, skipna=None, keep_attrs=None, weighted=False, **kwargs):
         if weighted:
             if self._face_centered():
+                grid_dim = "n_face"
                 # use face areas as weight
-                weights = self.uxgrid.face_areas
+                weights = da.from_array(self.uxgrid.face_areas.values)
             elif self._edge_centered():
+                grid_dim = "n_edge"
                 # use edge magnitude as weight
-                weights = self.uxgrid.edge_node_distances
+                weights = da.from_array(self.uxgrid.edge_node_distances.values)
             else:
                 # apply regular Xarray mean
                 warnings.warn(
@@ -428,13 +431,13 @@ def mean(self, dim=None, *, skipna=None, keep_attrs=None, weighted=False, **kwar
                     "variables. Performing an unweighted mean."
                 )
 
-                return super().mean(dim=None, skipna=None, keep_attrs=None, **kwargs)
+                return super().mean(dim=dim, skipna=skipna, keep_attrs=keep_attrs, **kwargs)
 
             # compute the total weight
             total_weight = weights.sum()
 
-            # compute weighted mean
-            weighted_mean = (self * weights).sum() / total_weight
+            # compute weighted mean #assumption on index of dimension (last one is geometry)
+            weighted_mean = (self * weights).sum(dim=grid_dim) / total_weight
 
             # create a UxDataArray and return it
             return UxDataArray(weighted_mean, uxgrid=self.uxgrid)