Earth topography grid at 10 arc-minute resolution

The grid is grid-node registered and stored in netCDF with CF-compliant metadata. The values are derived from a spherical harmonic model of the ETOPO1 bedrock grid. Topography values are referenced to “sea level” and are positive upwards.

Original source: ETOPO1

Pre-processing: Source code for preparation of the original dataset for redistribution in Ensaio

import pygmt
import xarray as xr

import ensaio

Download and cache the data and return the path to it on disk.

fname = ensaio.fetch_earth_topography(version=1)
print(fname)

/home/runner/work/_temp/cache/ensaio/v1/earth-topography-10arcmin.nc

Load the netCDF grid with xarray.

data = xr.load_dataarray(fname)
data

<xarray.DataArray 'topography' (latitude: 1081, longitude: 2161)>
array([[ 2742.,  2742.,  2742., ...,  2742.,  2742.,  2742.],
       [ 2768.,  2768.,  2768., ...,  2768.,  2768.,  2768.],
       [ 2822.,  2822.,  2821., ...,  2822.,  2822.,  2822.],
       ...,
       [-4243., -4244., -4245., ..., -4242., -4243., -4243.],
       [-4156., -4157., -4157., ..., -4156., -4156., -4156.],
       [-4237., -4237., -4237., ..., -4237., -4237., -4237.]],
      dtype=float32)
Coordinates:
  * longitude  (longitude) float64 -180.0 -179.8 -179.7 ... 179.7 179.8 180.0
  * latitude   (latitude) float64 -90.0 -89.83 -89.67 -89.5 ... 89.67 89.83 90.0
Attributes:
    Conventions:     CF-1.8
    title:           Topographic and bathymetric height of ETOPO1 (ice surfac...
    crs:             WGS84
    source:          Generated from a spherical harmonic model by the ICGEM C...
    license:         public domain
    references:      https://doi.org/10.7289/V5C8276M
    long_name:       topographic height above mean sea level
    standard_name:   height_above_mean_sea_level
    description:     height topography/bathymetry referenced to mean sea level
    units:           m
    actual_range:    [-10023.   6082.]
    icgem_metadata:  generating_institute: gfz-potsdam\ngenerating_date: 2021...

xarray.DataArray

'topography'

• latitude: 1081
• longitude: 2161

• 2.742e+03 2.742e+03 2.742e+03 ... -4.237e+03 -4.237e+03 -4.237e+03

  array([[ 2742.,  2742.,  2742., ...,  2742.,  2742.,  2742.],
         [ 2768.,  2768.,  2768., ...,  2768.,  2768.,  2768.],
         [ 2822.,  2822.,  2821., ...,  2822.,  2822.,  2822.],
         ...,
         [-4243., -4244., -4245., ..., -4242., -4243., -4243.],
         [-4156., -4157., -4157., ..., -4156., -4156., -4156.],
         [-4237., -4237., -4237., ..., -4237., -4237., -4237.]],
        dtype=float32)

• Coordinates: (2)
  • longitude
    (longitude)
    float64
    -180.0 -179.8 ... 179.8 180.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    actual_range :

    [-180. 180.]

    array([-180.      , -179.833333, -179.666667, ...,  179.666667,  179.833333,
            180.      ])

  • latitude
    (latitude)
    float64
    -90.0 -89.83 -89.67 ... 89.83 90.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    actual_range :

    [-90. 90.]

    array([-90.      , -89.833333, -89.666667, ...,  89.666667,  89.833333,
            90.      ])

• Indexes: (2)
  • longitude
    PandasIndex

    PandasIndex(Index([             -180.0, -179.83333333333334, -179.66666666666666,
                        -179.5, -179.33333333333334, -179.16666666666666,
                        -179.0, -178.83333333333334, -178.66666666666666,
                        -178.5,
           ...
                         178.5,  178.66666666666663,  178.83333333333331,
                         179.0,  179.16666666666663,  179.33333333333331,
                         179.5,  179.66666666666663,  179.83333333333331,
                         180.0],
          dtype='float64', name='longitude', length=2161))

  • latitude
    PandasIndex

    PandasIndex(Index([             -90.0, -89.83333333333333, -89.66666666666667,
                        -89.5, -89.33333333333333, -89.16666666666667,
                        -89.0, -88.83333333333333, -88.66666666666667,
                        -88.5,
           ...
                         88.5,  88.66666666666666,  88.83333333333331,
                         89.0,  89.16666666666666,  89.33333333333331,
                         89.5,  89.66666666666666,  89.83333333333331,
                         90.0],
          dtype='float64', name='latitude', length=1081))

• Attributes: (12)

  Conventions :

  CF-1.8

  title :

  Topographic and bathymetric height of ETOPO1 (ice surface) at 10 arc-minute resolution

  crs :

  WGS84

  source :

  Generated from a spherical harmonic model by the ICGEM Calculation Service (http://icgem.gfz-potsdam.de)

  license :

  public domain

  references :

  https://doi.org/10.7289/V5C8276M

  long_name :

  topographic height above mean sea level

  standard_name :

  height_above_mean_sea_level

  description :

  height topography/bathymetry referenced to mean sea level

  units :

  m

  actual_range :

  [-10023. 6082.]

  icgem_metadata :

  generating_institute: gfz-potsdam generating_date: 2021/06/11 product_type: topography body: earth modelname: etopo1-2250 max_used_degree: 720 gentlecut: 540 to 720 functional: topography_shm (from shm) unit: meter refsysname: WGS84 radiusrefsys: 6378137.000 m flatrefsys: 3.352810664747480E-03 (1/298.25722356300) long_lat_unit: degree latlimit_north: 90.000000000000 latlimit_south: -90.000000000000 longlimit_west: -180.00000000000 longlimit_east: 180.00000000000 gridstep: 0.16666666666667 latitude_parallels: 1081 longitude_parallels: 2161 number_of_gridpoints: 2336041 gapvalue: 99999.0000 weighted_mean: -2.3852244E+03 meter maxvalue: 6.0816327E+03 meter minvalue: -1.0023309E+04 meter signal_wrms: 2.5003302E+03 meter grid_format: long_lat_value attributes: longitude latitude topography_shm attributes_units: deg. deg. meter

Make a PyGMT pseudo-color map of the grid in a Mollweide projection.

fig = pygmt.Figure()
fig.basemap(
    region="g",
    projection="W15c",
    frame=True,
)
fig.grdimage(data, cmap="etopo1", shading="+nt0.5")
fig.colorbar(frame='af+l"topography [m]"')
fig.coast(shorelines=True, resolution="c", area_thresh=1e4)
fig.show()