Dfs2 - Bathymetric data

Convert GEBCO 2020 NetCDF to dfs2

GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)

import xarray
import mikeio

ds = xarray.open_dataset("../../data/gebco_2020_n56.3_s55.2_w12.2_e13.1.nc")
ds

<xarray.Dataset> Size: 118kB
Dimensions:    (lat: 264, lon: 216)
Coordinates: (2)
Data variables:
    elevation  (lat, lon) int16 114kB ...
Attributes: (8)

xarray.Dataset

• Dimensions:
  • lat: 264
  • lon: 216
• Coordinates: (2)
  • lat
    (lat)
    float64
    55.2 55.21 55.21 ... 56.29 56.3

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array([55.202083, 55.20625 , 55.210417, ..., 56.289583, 56.29375 , 56.297917])

  • lon
    (lon)
    float64
    12.2 12.21 12.21 ... 13.09 13.1

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array([12.202083, 12.20625 , 12.210417, ..., 13.089583, 13.09375 , 13.097917])

• Data variables: (1)
  • elevation
    (lat, lon)
    int16
    ...

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    Elevation relative to sea level

    units :

    m

    sdn_parameter_urn :

    SDN:P01::BATHHGHT

    sdn_parameter_name :

    Sea floor height (above mean sea level) {bathymetric height}

    sdn_uom_urn :

    SDN:P06::ULAA

    sdn_uom_name :

    Metres

    [57024 values with dtype=int16]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([ 55.20208333333332,  55.20625000000001, 55.210416666666674,
            55.21458333333334,           55.21875,  55.22291666666666,
           55.227083333333326,  55.23124999999999,  55.23541666666665,
            55.23958333333334,
           ...
            56.26041666666666,  56.26458333333332,  56.26875000000001,
           56.272916666666674,  56.27708333333334,           56.28125,
            56.28541666666666, 56.289583333333326,  56.29374999999999,
            56.29791666666665],
          dtype='float64', name='lat', length=264))

  • lon
    PandasIndex

    PandasIndex(Index([ 12.20208333333332, 12.206250000000011, 12.210416666666674,
           12.214583333333337,           12.21875, 12.222916666666663,
           12.227083333333326, 12.231249999999989, 12.235416666666652,
           12.239583333333343,
           ...
           13.060416666666669, 13.064583333333331, 13.068749999999994,
           13.072916666666657,  13.07708333333332, 13.081250000000011,
           13.085416666666674, 13.089583333333337,           13.09375,
           13.097916666666663],
          dtype='float64', name='lon', length=216))

• Attributes: (8)

  Conventions :

  CF-1.6

  title :

  The GEBCO_2020 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals

  institution :

  On behalf of the General Bathymetric Chart of the Oceans (GEBCO), the data are held at the British Oceanographic Data Centre (BODC).

  source :

  The GEBCO_2020 Grid is the latest global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and GEBCO. The Seabed 2030 Project aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor and make it available to all.

  history :

  Information on the development of the data set and the source data sets included in the grid can be found in the data set documentation available from https://www.gebco.net

  references :

  DOI: 10.5285/a29c5465-b138-234d-e053-6c86abc040b9

  comment :

  The data in the GEBCO_2020 Grid should not be used for navigation or any purpose relating to safety at sea.

  node_offset :

  1.0

ds.elevation.plot();

ds.elevation.sel(lon=12.74792, lat=55.865, method="nearest")

<xarray.DataArray 'elevation' ()> Size: 2B
[1 values with dtype=int16]
Coordinates: (2)
Attributes: (7)

xarray.DataArray

'elevation'

• ...

  [1 values with dtype=int16]

• Coordinates: (2)
  • lat
    ()
    float64
    55.86

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array(55.86458333)

  • lon
    ()
    float64
    12.75

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array(12.74791667)

• Indexes: (0)
• Attributes: (7)

  standard_name :

  height_above_reference_ellipsoid

  long_name :

  Elevation relative to sea level

  units :

  m

  sdn_parameter_urn :

  SDN:P01::BATHHGHT

  sdn_parameter_name :

  Sea floor height (above mean sea level) {bathymetric height}

  sdn_uom_urn :

  SDN:P06::ULAA

  sdn_uom_name :

  Metres

Check ordering of dimensions, should be (y,x)

ds.elevation.dims

('lat', 'lon')

el = ds.elevation.values
el.shape

(264, 216)

Check that axes are increasing, S->N W->E

ds.lat.values[0],ds.lat.values[-1] 

(55.20208333333332, 56.29791666666665)

ds.lat.values[0] < ds.lat.values[-1] 

True

ds.lon.values[0],ds.lon.values[-1] 

(12.20208333333332, 13.097916666666663)

el[0,0] # Bottom left

-8

el[-1,0] # Top Left

-31

geometry = mikeio.Grid2D(x=ds.lon.values, y=ds.lat.values, projection="LONG/LAT")
geometry

<mikeio.Grid2D>
x: [12.2, 12.21, ..., 13.1] (nx=216, dx=0.004167)
y: [55.2, 55.21, ..., 56.3] (ny=264, dy=0.004167)
projection: LONG/LAT

da = mikeio.DataArray(data=el,
               item=mikeio.ItemInfo("Elevation", mikeio.EUMType.Total_Water_Depth),
               geometry=geometry,
               dims=("y","x") # No time dimension
               )
da

<mikeio.DataArray>
name: Elevation
dims: (y:264, x:216)
time: 2018-01-01 00:00:00 (time-invariant)
geometry: Grid2D (ny=264, nx=216)

da.plot();

da.plot(cmap='coolwarm', vmin=-100, vmax=100);

da.to_dfs("gebco.dfs2")

ds = mikeio.read("gebco.dfs2")
ds.Elevation.plot()

Clean up

import os

os.remove("gebco.dfs2")