#
`measures_data` documentation

`measures_data` imports MEaSUREs Antarctic surface velocity or grounding line data into your current Matlab workspace.

## Contents

## Before you use this function:

This function requires the MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2 netcdf dataset which can be downloaded here: https://nsidc.org/data/NSIDC-0484.

## Citing these datasets:

VELOCITY DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2017. MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/D7GK8F5J8M8R.

TWO LITERARY REFERENCES FOR THE VELOCITY DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011. Ice Flow of the Antarctic Ice Sheet, Science, Vol. 333(6048): 1427-1430. doi:10.1126/science.1208336.

Mouginot, J., B. Scheuchl, and E. Rignot. 2012. Mapping of Ice Motion in Antarctica Using Synthetic- Aperture Radar Data, Remote Sensing. 4. 2753-2767. http://dx.doi.org/10.3390/rs4092753

GROUNDING LINE DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011. MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry. Boulder, Colorado USA: National Snow and Ice Data Center. http://dx.doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0498.001.

A LITERARY REFERENCE FOR THE GROUNDING LINE DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011. Antarctic Grounding Line Mapping from Differential Satellite Radar Interferometry. Geophyical Research Letters 38: L10504. doi:10.1029/2011GL047109.

ANTARCTIC MAPPING TOOLS: Greene, C.A., Gwyther, D.E. and Blankenship, D.D. Antarctic Mapping Tools for Matlab. Computers & Geosciences. http://dx.doi.org/10.1016/j.cageo.2016.08.003

## Syntax

[lat,lon,V] = measures_data('speed') [lat,lon,vx,vy] = measures_data('velocity') [lat,lon,errx,erry] = measures_data('error') [lat,lon,err] = measures_data('error') [lat,lon,count] = measures_data('count') [gllat,gllon,year] = measures_data('gl') [...] = measures_data(...,lati,loni) [...] = measures_data(...,lati,loni,extrakm) [...] = measures_data(...,xi,yi) [...] = measures_data(...,xi,yi,extrakm) [x,y,...] = measures_data(...,'xy') [...] = measures_data(...,'v1')

## Description

`[lat,lon,V] = measures_data('speed')` gives the surface speed (m/yr) grid corresponding to georeferenced coordinates `lat,lon`.

`[lat,lon,vx,vy] = measures_data('velocity')` gives the surface velocity as x and y components on a polar stereographic grid referenced to the geographic locations `lat,lon`. Note: you can convert `vx` and `vy` to zonal and meridional components of velocity with the AMT function `vxvy2uv`.

`[lat,lon,errx,erry] = measures_data('error')` gives the polar stereographic x and y components of the estimated error in m/yr.

`[lat,lon,err] = measures_data('error')` gives the scalar value of estimated error in m/yr (computed as the hypotenuse of `errx` and `erry`).

`[lat,lon,count] = measures_data('count')` gives the count of scenes used per pixel.

`[gllat,gllon,year] = measures_data('gl')` loads grounding line locations measured by InSAR. The year corresponding to each datapoint is an optional output.

`[...] = measures_data(...,lati,loni)` only loads data within the smallest polar stereographic quadrangle bounding any data in `lati,loni`. This is convenient for loading only the data within current map extents or near some airborne survey lines.

`[...] = measures_data(...,lati,loni,extrakm)` adds a specified number of kilometers to each side of the quadrangle described above. The `extrakm` option can be a scalar to add, say, a 20 km frame around your flight line, or it can have two elements such as [20 10] to specify 20 extra kilometers on the sides and 10 extra kilometers on the top and bottom.

`[...] = measures_data(...,xi,yi)` same as above, but with polar stereographic meters (true lat 71 S) as inputs instead of geo coordinates. Coordinates are automatically parsed by the `islatlon` function.

`[...] = measures_data(...,xi,yi,extrakm)` again, as above, but with polar stereographic coordinates.

`[x,y,...] = measures_data(...,'xy')` returns geolocation data as polar stereographic (true latitude 71 S) meters.

`[...] = measures_data(...,'v1')` uses MEaSUREs version 1 gridded data if you have it.

## Example 1a: Lambert Glacier speed

Plot a 1000 km wide by 600 km high map of Lambert Glacier surface speed. Start by getting the coordinates of Lambert Glacier:

```
scarloc 'lambert glacier'
```

ans = -71.5000 69.0000

So we'll enter -71.5,69 into `measures_data` to get MEaSUREs data near Lambert Glacier. Note that for a 1000 km wide and 600 km tall dataset, you specify the center coordinates plus 500 on each side and 300 km on top and bottom:

```
[lat,lon,speed] = measures_data('speed',-71.5,69,[500 300]);
```

The easiest way to plot this dataset is with the AMT function `pcolorps`:

```
h = pcolorps(lat,lon,speed);
axis tight
```

## Example 1b: Overlay a grounding line

Want a black grounding line? You can get all the grounding line data within the current limits of the figure simply by passing `xlim` and `ylim` to the `measures_data` function, then plot with `plotps`:

% Load the grounding line data: [gllat,gllon] = measures_data('gl',xlim,ylim); % Plot the grounding line data: hold on plotps(gllat,gllon,'k-') axis tight scalebarps

## Example 2: A continent of uncertainty

If you don't want to subset the data, you can load the entire MEaSUREs dataset pretty easily. However, it's a huge dataset, so it will take a second to load. The best plotting function to can handle such a large dataset is `imagesc`, so we'll have to get the reference coordinates in x and y, which is always faster than plotting lats and lons for polar stereographic grids.

[x,y,err] = measures_data('error','xy');

If you plot with `imagesc` you'll usually want to follow it with the `axis xy` command to make the up/down orientation correct. I also use `axis image`, which properly sets the aspect ratio and trims up any extra space.

figure h=imagesc(x,y,err); axis xy image

To make it like Figure 3 in the documentation, simply add a colorbar and set the colorbar to jet (#endtherainbow). Unfortunately, this dataset does not distinguish between open ocean and bad data--it all has an uncertainty of zero. We can make all the zero values transparent.

cb = colorbar; ylabel(cb,'Error [m/yr]') colormap(jet(10)) caxis([1 17]) set(h,'alphadata',err>0)

and as in Example 1 we can add a grounding line:

[gllat,gllon] = measures_data('gl'); hold on plotps(gllat,gllon,'k-')

## Example 3: Grounding line migration

Let's take a look at Pine Island Glacier. Start by initializing a 100 km wide map then load all the grounding line data within the extents of the map:

figure mapzoomps('pine island glacier','size',100) [gllat,gllon,year] = measures_data('gl',xlim,ylim);

Use `scatterps` to plot the time dependence of the grounding line location:

```
scatterps(gllat,gllon,6,year,'filled')
colorbar
```

Perhaps you'd also like to add a quiver plot showing surface velocity. Start by getting the x and y components of velocity:

[x,y,vx,vy] = measures_data('vel',xlim,ylim,'xy');

And a grid of hundreds of arrows will be too cluttered to make any sense of, so I suggest downsampling the data. Downsampling can be performed in a cheap-and-dirty way by taking every Nth row and colum of data, but it's often easier to use the Image Processing Toolbox function `imresize`, which has the added benefit of automatic anti-aliasing. Let's only plot about 20% of the the data:

```
scale = 0.2; % for 20%
x = imresize(x,scale);
y = imresize(y,scale);
vx = imresize(vx,scale);
vy = imresize(vy,scale);
```

With a scaled-down dataset, plotting is easy with Matlab's built-in `quiver` function:

hold on quiver(x,y,vx,vy,'r')

## Citing these datasets:

If you use these datasets, please be sure to cite the following:

VELOCITY DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2017. MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/D7GK8F5J8M8R.

A LITERARY REFERENCE FOR THE VELOCITY DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011.Ice Flow of the Antarctic Ice Sheet, Science, Vol. 333(6048): 1427-1430. doi:10.1126/science.1208336.

GROUNDING LINE DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011. MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry. Boulder, Colorado USA: National Snow and Ice Data Center. doi: 10.5067/MEASURES/CRYOSPHERE/nsidc-0498.001.

A LITERARY REFERENCE FOR THE GROUNDING LINE DATA: Rignot, E., J. Mouginot, and B. Scheuchl. 2011. Antarctic Grounding Line Mapping from Differential Satellite Radar Interferometry. Geophyical Research Letters 38: L10504. doi:10.1029/2011GL047109.

ANTARCTIC MAPPING TOOLS: Greene, C.A., Gwyther, D.E. and Blankenship, D.D., 2016. Antarctic Mapping Tools for Matlab. Computers & Geosciences. http://dx.doi.org/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 Institute for Geophysics (UTIG), October 2016. Updated for Version 2 gridded data May 2017.