html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1844#issuecomment-359025678,https://api.github.com/repos/pydata/xarray/issues/1844,359025678,MDEyOklzc3VlQ29tbWVudDM1OTAyNTY3OA==,7747527,2018-01-19T16:55:25Z,2018-01-19T16:55:25Z,NONE,"So you got a two-year temperature field with dimension [730, 1, 481, 781], and another mean, and std data arrays  of [366, 1, 481, 781] and you want to normalize the temperature field. 

Sorry I'm not familiar with the Xarray's groupby functions, I'll try several things before some experts jumping in. 

- Concat two std/mean fields along dayofyear, and reindex to the time index from the temperature data. Then you can do the (dset-mean)/std
- Separate the temperature fields into two one-year chunks, reindex time to dayofyear, then do the calculation. 
- Flatten the spatial grid then use numpy to do the trick.

I'm also interested in the right way to do it using built-in Xarray functions. I'm pretty sure there are some more clever ways to do this. 
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290023410