bedmachine_interp documentation

bedmachine_interp loads data from Morlighem et al.'s BedMachine datasets.

See also: bedmachine, bedmachine_data and bedmachine_profile.



This function requires a set of Matlab tools and a Bedmachine dataset, and both will depend on where you're working. Get them here:

  1. Arctic Mapping Tools
  2. Greenland Bedmachine Data
  1. Antarctic Mapping Tools
  2. Antarctic Bedmachine Data


zi = bedmachine_interp(variable,lati,loni)
zi = bedmachine_interp(variable,xi,yi)
zi = bedmachine_interp(...,IceSheet)
zi = bedmachine_interp(...,'datum',datum)
zi = bedmachine_interp(...,'method',InterpMethod)


zi = bedmachine_interp(variable,lati,loni) interpolates a specified BedMachine variable to the given by geo coordinates lati,loni. The variable can be:

zi = bedmachine_interp(variable,xi,yi) As above, but for polar stereographic coordinates xi,yi in meters (ps70 for Greenland; ps71 for Antarctica). The function automatically determines whether input coordinates are geo or polar stereographic via the islatlon function.

zi = bedmachine_interp(...,IceSheet) specifies either 'greenland' or 'antarctica' (default).

zi = bedmachine_interp(...,'datum',datum) specifies a datum as either 'geoid' (default) or 'ellipsoid' for wgs84.

zi = bedmachine_interp(...,'method',InterpMethod) specifies any interpolation method allowed by the interp2 function.

Example 1: A map in Greenland

Here's a grid centered on Jakobshavn Glacier. The grid is 500 km wide and 800 km tall, at 10 km resolution:

% Create the grid:
[latgrid,longrid] = psngrid(69.167,-49.833,[500 800],10);

% Plot the grid as blue dots:

% Add som context to the map:
axis tight off
h = greenland('patch');
uistack(h,'bottom') % sends greeland to the bottom

Get surface elevations at every grid point:

zi = bedmachine_interp('surface',latgrid,longrid);

% Plot contours:
[C,h] = contourpsn(latgrid,longrid,zi,0:250:3000);

% Label the contour lines:

Example 2: Compare BedMachine to Bedmap2

Here's a quick comparison of BedMachine to Bedmap2. Start by loading the Bedmap2 data, and since Bedmap2 and BedMachine use slightly different geoids, we'll load both to the same common WGS84 ellispoid reference:

% Get coordinates of grounding line:
[gl_lat,gl_lon] = antbounds_data('gl');

% Only load enough Bedmap2 data to encompass grounding line:
[lat,lon,bed_b2] = bedmap2_data('bedw',gl_lat,gl_lon);

Now bedmachine_interp makes comparison easy (but note, these are massive datasets, so it might take several seconds to a minute to interpolate):

bed_BM = bedmachine_interp('bed',lat,lon,'datum','wgs84');

Plot the difference. Below I'm using the cmocean (Thyng et al., 2016) balance colormap.

axis image off
caxis([-1000 1000])
cmocean balance
cb = colorbar;
ylabel(cb,'BedMachine-Bedmap2 (m)')

Citing this dataset

If you use BedMachine data, please cite the Morlighem paper listed below. And if this function is useful for you, please do me a kindness and cite my Antarctic Mapping Tools paper.

Morlighem M. et al., (2017), BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multi-beam echo sounding combined with mass conservation, Geophys. Res. Lett., 44, doi:10.1002/2017GL074954.

Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2019. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, Nature Geoscience. doi:10.1016/j.cageo.2016.08.003.

Greene, C. A., Gwyther, D. E., & Blankenship, D. D. Antarctic Mapping Tools for Matlab. Computers & Geosciences. 104 (2017) pp.151-157. doi:10.1016/j.cageo.2016.08.003.

Author Info

This function and supporting documentation were written by Chad A. Greene of the University of Texas at Austin, October 2018.