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mankoff avatar mankoff commented on July 18, 2024

Done (simply) because Mankoff 2020 (https://doi.org/10.22008/promice/data/ice_discharge) comes with ROI metadata:

import xarray as xr

ds = xr.open_dataset("/home/kdm/data/Mankoff_2020/ice/latest/gate.nc")

ds_Z = ds.drop_vars(["mean_x","mean_y","mean_lon","mean_lat","sector"])\
          .groupby("Zwally_2012")\
          .sum()

ds_Mb = ds.drop_vars(["mean_x","mean_y","mean_lon","mean_lat"])\
          .groupby("sector")\
          .sum()

ds_Mr = ds.drop_vars(["mean_x","mean_y","mean_lon","mean_lat","sector"])\
          .groupby("region")\
          .sum()

# print(ds_Z)
print(ds_Z['discharge'].to_dataframe().unstack().T.head())
Zwally_2012                  11     12     13         21         31         32         33  \
          time                                                                              
discharge 1986-04-15  15.504001  4.844  0.803  21.589001  15.722001  11.582000  34.144005   
          1986-05-15  14.807000  4.844  0.803  21.708000  15.662001  11.475000  34.777004   
          1986-06-15  18.068998  4.843  0.803  21.833000  16.401001  11.731999  36.752995   
          1986-07-15  19.074999  4.844  0.803  19.969000  16.516001  11.771999  35.920002   
          1986-08-15  18.014999  4.844  0.803  21.770000  16.478001  11.165000  36.254002   

Zwally_2012                  41         42         43         50     61     62         71  \
          time                                                                              
discharge 1986-04-15  40.632000  62.001999  40.166000  29.128998  6.737  1.972  23.174000   
          1986-05-15  44.097000  60.396004  39.656998  29.122000  6.740  1.931  30.070000   
          1986-06-15  44.028000  58.748009  39.128002  29.115999  6.743  1.887  30.006001   
          1986-07-15  45.708996  58.844002  38.619003  29.113003  6.746  1.845  29.944000   
          1986-08-15  43.092999  62.721001  38.094006  29.105003  6.749  1.803  29.879999   

Zwally_2012                  72         81        82  
          time                                        
discharge 1986-04-15  46.439999  83.981003  7.387001  
          1986-05-15  46.694004  84.958000  7.380000  
          1986-06-15  46.781002  87.003983  7.375000  
          1986-07-15  46.840996  89.250000  7.368001  
          1986-08-15  45.588997  88.822014  7.361000  

from mass_balance.

mankoff avatar mankoff commented on July 18, 2024

Re-opening because D is easily partitioned by Zwally sector or Mouginot region, but not by Mouginot basin.

For Mouginot basins, there are six basins (3x two basins) where the gate spans two basins.

Possible solutions:

  • Re-do ice discharge product to provide a pixel-level product that can be split by basin
  • Merge the Mouginot basins wherever this occurs
  • Only provide basin-level data for the n largest basins (solves many other quality issues). These six basins are not included in the n largest for reasonable values of n.

from mass_balance.

mankoff avatar mankoff commented on July 18, 2024

Not providing basins for now.

from mass_balance.

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