Comments (3)
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.
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.
Not providing basins for now.
from mass_balance.
Related Issues (20)
- Lag term for negative SMB and BMB HOT 1
- Multi-time-scale initial figure
- Make reproducible
- Include peripheral glaciers HOT 1
- Distinguish between random and systematic uncertainty
- Upgrade to BedMachine v4
- Rename marine mass balance (MMB) to discharge (D).
- Separate SMB terms
- RAMCO SMB is 0 from 1986 through 1989 HOT 2
- SMB uncertainty is >> 9 % (Table 3) HOT 1
- Sectors & regions disagree when summed. HOT 1
- SMB uncertainty is not fixed at 9 %
- Update to BedMachine V5
- Provide THREDDS interface HOT 1
- Improve daily summary graphic HOT 3
- Greenland mass flow Sankey diagram HOT 2
- Treatment for peripheral glaciers (SMB & D)
- Treatment for different domains
- Near Realtime (NRT)
- Compare with IMBIE
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from mass_balance.