issue_comments: 359025678

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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. 

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