How much ice entered Scoresby Sund in 2018?

[mankoff]

Request: How much ice entered Scoresby Sund in 2018?

[mankoff]

Open file

import xarray as xr
ds = xr.open_dataset('gate.nc')
print(ds)

<xarray.Dataset>
Dimensions:        (time: 451, gate: 267)
Coordinates:
  ,* time           (time) datetime64[ns] 1986-04-15 1986-05-15 ... 2021-11-09
  ,* gate           (gate) int64 1 3 4 5 6 7 8 10 ... 350 352 353 354 355 356 357
Data variables:
    discharge      (gate, time) float32 ...
    err            (gate, time) float32 ...
    coverage       (gate, time) float32 ...
    mean_x         (gate) int32 ...
    mean_y         (gate) int32 ...
    mean_lon       (gate) float32 ...
    mean_lat       (gate) float32 ...
    sector         (gate) int32 ...
    region         (gate) object ...
    Zwally_2012    (gate) int64 ...
    name_Bjørk     (gate) object ...
    name_Mouginot  (gate) object ...
Attributes:
    featureType:      timeSeries
    title:            Greenland discharge
    summary:          Greenland discharge per gate
    keywords:         Greenland; Ice Discharge; Calving; Submarine Melt
    source:           git commit: db03334
    creator_name:     Ken Mankoff
    creator_email:    [email protected]
    creator_url:      http://kenmankoff.com
    institution:      GEUS
    references:       10.22008/promice/ice_discharge
    product_version:  2.0

Extract ROI, compute annual average, sum discharge within ROI

ans = ds.where(ds['mean_x'] > 500000)\
        .where(ds['mean_y'] > -2111111)\
        .where(ds['mean_y'] < -1858906)\
        .dropna(dim='gate')\
        .resample({'time':'YS'})\
        .mean()\
        .sel({'time':'2018'})\
        .squeeze()\
        .to_dataframe()


# print gate indices and manually check that these are the 15 gates in the ROI in QGIS
print(df.index)

: Int64Index([138, 139, 140, 144, 146, 148, 149, 150, 151, 152, 154, 156, 158, 159, 160], dtype='int64', name='gate')

ans = df.agg({'discharge':sum, 'err':sum, 'coverage':mean,
              'mean_x':mean, 'mean_y':mean, 'mean_lon':mean, 'mean_lat':mean,
              'region':min, 'Zwally_2012':min})

print(ans)

#+RESULTS:
: discharge          17.857052
: err                 1.883314
: coverage            0.687745
: mean_x         653457.933333
: mean_y            -2020563.4
: mean_lon          -27.116436
: mean_lat           70.568184
: region                    CE
: Zwally_2012             31.0