issues: 166195300
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
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| 166195300 | MDU6SXNzdWUxNjYxOTUzMDA= | 900 | How to apply function to two (or more) variables simultaneously | 17951292 | closed | 0 | 3 | 2016-07-18T21:14:57Z | 2016-07-19T01:31:46Z | 2016-07-19T01:31:40Z | NONE | I have two datasets: fcst and obs, each with dimensions (time, lat, lon). The first contains predicted values on a lat x lon grid for a given lead time and the second the corresponding verifying observations. I want to compute skill scores at each time but this obviously involves applying a function involving variables from both datasets. Moreover, the two fields (fcst and obs) need to be cosine weighted first (involves coordinate 'lat'). Furthermore, I wish to align the forecasts and obs in time; there may be missing values of each at different times. I tried aligning the datasets using 'xr.align' but got all kinds of errors when trying to use the resulting new dataset. I suppose I could extract the values from each dataset, taking care to use indexing to extract the common times and then using standard numpy operations and the like but before I go that route is there a methodology for using xarray to do such a computation? If I were doing a single variable computation, it would be easy to use the groupby and apply methods. TIA for any advice! |
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completed | 13221727 | issue |