Bedmachine Grids

Description: Gridded data in EPSG 3431 projection. Topographic layers, uncertainties, and masks.

Requires an EarthData login, see link for how to configure this.

Dataset:

National Snow and Ice Data Center. “IceBridge BedMachine Greenland, Version 5,” October 21, 2020. https://nsidc.org/data/idbmg4/versions/5.

Associated paper:

N.A.

%%capture
%load_ext autoreload
%autoreload 2


from polartoolkit import fetch, maps, regions, utils

fetch.bedmachine(
    layer="bed",
    hemisphere="north",
    spacing=5000,
)

<xarray.DataArray 'bed' (y: 549, x: 305)> Size: 670kB
dask.array<open_dataset-bed, shape=(549, 305), dtype=float32, chunksize=(275, 305), chunktype=numpy.ndarray>
Coordinates:
  * y        (y) float64 4kB -3.378e+06 -3.372e+06 ... -6.425e+05 -6.375e+05
  * x        (x) float64 2kB -6.475e+05 -6.425e+05 ... 8.675e+05 8.725e+05
Attributes:
    Conventions:   CF-1.7
    title:
    history:       gmt grdsample @GMTAPI@-S-I-G-M-G-N-000000 -G@GMTAPI@-S-O-G...
    description:
    actual_range:  [-5544.27490234375, 3240.066650390625]
    long_name:     z

xarray.DataArray

'bed'

• y: 549
• x: 305

• dask.array<chunksize=(275, 305), meta=np.ndarray>

  Array Chunk Bytes 654.08 kiB 327.64 kiB Shape (549, 305) (275, 305) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • y
    (y)
    float64
    -3.378e+06 ... -6.375e+05

    long_name :

    y

    axis :

    Y

    actual_range :

    [-3380000.0, -635000.0]

    array([-3377500., -3372500., -3367500., ...,  -647500.,  -642500.,  -637500.],
          shape=(549,))

  • x
    (x)
    float64
    -6.475e+05 -6.425e+05 ... 8.725e+05

    long_name :

    x

    axis :

    X

    actual_range :

    [-650000.0, 875000.0]

    array([-647500., -642500., -637500., ...,  862500.,  867500.,  872500.],
          shape=(305,))

• Indexes: (2)
  • y
    PandasIndex

    PandasIndex(Index([-3377500.0, -3372500.0, -3367500.0, -3362500.0, -3357500.0, -3352500.0,
           -3347500.0, -3342500.0, -3337500.0, -3332500.0,
           ...
            -682500.0,  -677500.0,  -672500.0,  -667500.0,  -662500.0,  -657500.0,
            -652500.0,  -647500.0,  -642500.0,  -637500.0],
          dtype='float64', name='y', length=549))

  • x
    PandasIndex

    PandasIndex(Index([-647500.0, -642500.0, -637500.0, -632500.0, -627500.0, -622500.0,
           -617500.0, -612500.0, -607500.0, -602500.0,
           ...
            827500.0,  832500.0,  837500.0,  842500.0,  847500.0,  852500.0,
            857500.0,  862500.0,  867500.0,  872500.0],
          dtype='float64', name='x', length=305))

• Attributes: (6)

  Conventions :

  CF-1.7

  title :

  history :

  gmt grdsample @GMTAPI@-S-I-G-M-G-N-000000 -G@GMTAPI@-S-O-G-G-G-Y-000001 -I5000.0+e -R-655000.0/880000.0/-3385000.0/-630000.0 -Vw

  description :

  actual_range :

  [-5544.27490234375, 3240.066650390625]

  long_name :

  z

layer_names = [
    # topographies
    "surface",
    "icebase",
    "bed",
    # uncertainties
    "errbed",
    # others
    "ice_thickness",
    "dataid",
    "geoid",
    "mask",
    "source",
]

grids = []
for name in layer_names:
    data = fetch.bedmachine(
        layer=name,
        # available options
        hemisphere="north",
        spacing=5e3,  # if >=5km, will use preprocessed grid
        # region,
        # registration, # "g" for gridline or "p" for pixel
        # reference, # "eigen-6c4" or "ellipsoid"
    )
    grids.append(data)
    print(f"Info for {name}")
    _ = utils.get_grid_info(data, print_info=True)
    print("##########")

Info for surface
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -0.00464886287227
grid zmax: 3240.26293945
grid registration: g
##########
Info for icebase
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -1055.84020996
grid zmax: 3240.06640625
grid registration: g
##########
Info for bed
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -5544.27490234
grid zmax: 3240.06665039
grid registration: g
##########
Info for errbed
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -8.39897711558e-06
grid zmax: 1000.00250244
grid registration: g
##########
Info for ice_thickness
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -0.00641952548176
grid zmax: 3374.90136719
grid registration: g
##########
Info for dataid
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -0.000416182418121
grid zmax: 10.0001955032
grid registration: g
##########
Info for geoid
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: 5.99998569489
grid zmax: 64.000038147
grid registration: g
##########
Info for mask
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: -9.44617422647e-05
grid zmax: 4.00011920929
grid registration: g
##########
Info for source
grid spacing: 5000.0 m
grid region: (-647500.0, 872500.0, -3377500.0, -637500.0)
grid zmin: 0.998742640018
grid zmax: 49.8486061096
grid registration: g
##########

cmaps = [
    "rain",
    "rain",
    "rain",
    "thermal",
    "dense",
    "viridis",
    "balance+h0",
    "viridis",
    "viridis",
]
cbar_labels = ["meters"] * 5 + ["data ID", "meters", "mask", "source"]

reverse_cpts = [True] * 3 + [False] * 6

fig = maps.subplots(
    grids,
    dims=(2, 5),
    titles=[t.capitalize() for t in layer_names],
    cmaps=cmaps,
    reverse_cpts=reverse_cpts,
    cbar_labels=cbar_labels,
    region=regions.greenland,
    hist=True,
    coast=True,
    robust=True,
    hemisphere="north",
)

fig.show(dpi=200)