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.
[1]:
%%capture
%load_ext autoreload
%autoreload 2
from polartoolkit import fetch, maps, regions, utils
[2]:
fetch.bedmachine(
layer="bed",
hemisphere="north",
spacing=5000,
)
[2]:
<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[3]:
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
##########
[4]:
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)
[ ]: