This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017.
The surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.
The algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012
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Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: http://licences.ceda.ac.uk/image/data_access_condition/esacci_icesheets_greenland_terms_and_conditions.pdf. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data were processed by the ESA CCI Greenland Ice Sheet project team and supplied to CEDA in the context of the ESA CCI Open Data Portal Project.
As provided by the CCI Greenland Ice Sheets team
Contains three different types of data files: 1) png plots of the surface elevation changes. 2) gridded ASCII files of the surface elevation changes and their associated errors 3) NetCDF files containing the surface elevation changes and their associated errors
Computation Element: 1
|Title||The ESA CCI Greenland Ice Sheets Cryosat2 Surface Elevation Change product|
|Abstract||The algorithms used to derive the product are explained in detail in Simonsen, S. B. and Sørensen, L. S. (2017) ‘Implications of changing scattering properties on Greenland ice sheet volume change from Cryosat-2 altimetry’, Remote Sensing of Environment. Elsevier Inc., 190, pp. 207–216. doi: 10.1016/j.rse.2016.12.012.. The approach used corresponds to Least Squares Method (LSM) as described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 10/20 km.|
- long_name: Average time for time slice
- var_id: t
- names: Time, Average time for time slice
- units: meters
- long_name: Cartesian x-coordinate - easting
- standard_name: projection_x_coordinate
- var_id: x
- names: projection_x_coordinate, Cartesian x-coordinate - easting
- units: meters
- long_name: Cartesian y-coordinate - northing
- standard_name: projection_y_coordinate
- var_id: y
- names: projection_y_coordinate, Cartesian y-coordinate - northing
- long_name: End of time slice
- var_id: end_time
- names: Time, End of time slice
- long_name: Error estimates associated with surface elevation changes
- var_id: SEC_error
- names: Error estimates associated with surface elevation changes
- var_id: landtype
- long_name: Land type definition.
- names: Land type definition.
- var_id: lat
- long_name: Latitude
- units: degrees north
- names: Latitude
- var_id: start_time
- long_name: Start of time slice
- names: Time, Start of time slice
- long_name: Surface elevation changes, 10.0 km gaussian filter
- var_id: SEC
- names: Surface elevation changes, 10.0 km gaussian filter
- long_name: Surface elevation changes, 20.0 km gaussian filter
- var_id: SEC
- names: Surface elevation changes, 20.0 km gaussian filter
- var_id: grid_projection
- var_id: lon
- units: degrees east
- long_name: longitude
- names: longitude