In 2007, Denmark launched the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) to assess changes in the mass balance of the ice sheet. The two major contributors to the ice sheet mass loss are surface melt and a larger production of icebergs through faster ice flow. PROMICE is focused on both processes. Ice movement and discharge is tracked by satellites and GPSs. The surface mass balance is monitored by a network of weather stations in the melt zone of the ice sheet, providing ground truth data to calibrate mass budget models.

The Greenland Climate Network (GC-Net) was established in 1995 by Prof. Konrad Steffen at CIRES, to obtain knowledge of the mass gain and climatology of the ice sheet. The programme was funded by the USA until 2020, at which point Denmark assumed responsibility for the operation and maintenance of the weather station network. The snowfall and climatology are monitored by a network of weather stations in the accumulation zone of the ice sheet, supplemented by satellite-derived data products.

Together, the two monitoring programmes deliver data about the mass balance of the Greenland ice sheet in near real-time. Explore our project dataverses and datasets below.
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Jun 9, 2026 - pypromice v1.12.0
Python Source Code - 1.7 KB - MD5: 7bd8bda04e9284075dfb2044e20cc87d
Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
Jun 9, 2026 - pypromice v1.12.0
Python Source Code - 15.6 KB - MD5: 399014e41ca4d3b7932bb78e1e99e96d
Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
Jun 3, 2026 - Ice velocity
Solgaard, Anne Munck; Kusk, Anders, 2026, "Greenland Ice Velocity from Sentinel-1 Edition 5", https://doi.org/10.22008/FK2/K70OPK, GEUS Dataverse, V10
Greenland Ice Velocity from Sentinel-1 The PROMICE Ice Velocity product is a timeseries of Greenland Ice Sheet velocity mosaics based on ESA Sentinel-1 SAR offset tracking. The product spans the period January 2016 to present. Spatial resolution: 200 m Temporal resolution: A new...
Network Common Data Form - 324.6 MB - MD5: 6e22a6445c11e1fbf1af930a3c3693b6
Data
PNG Image - 3.7 MB - MD5: 453191c09d2ebf22228672ec0b2d16fe
Image
Jun 3, 2026 - Automatic weather stations
How, P.; Lund, M.C.; Ahlstrøm, A.P.; Andersen, S.B.; Box, J.E.; Citterio, M.; Colgan, W.T.; Fausto, R.S.; Karlsson, N.B.; Jakobsen, J.; Jakobsgaard, H.T.; Larsen, S.H.; Mankoff, K.D.; Nielsen, R.B.; Rutishauser, A.; Shield, C.L.; Solgaard, A.M.; Stevens, I.T.; van As, D.; Vandecrux, B.; Abermann, J.; Bjørk, A.A.; Langley, K.; Lea, J.; Messerli, A.; Prinz, R., 2022, "PROMICE and GC-Net automated weather station data in Greenland", https://doi.org/10.22008/FK2/IW73UU, GEUS Dataverse, V38
PROMICE and GC-Net automated weather station (AWS) data in Greenland. The data is available either as csv or netcdf files. Station locations, installation (if necessary, decommission) date, location type (tundra, ice sheet, local glacier), station type (one or two levels of measu...
Adobe PDF - 101.6 KB - MD5: a58cf9318064c77d9a6b7ca746400b37
Uploaded with pyDataverse 2026-06-01 16:59
Comma Separated Values - 3.5 KB - MD5: 32590e72ab7cd7e46185cb566a5888c1
Uploaded with pyDataverse 2026-06-01 16:58
Comma Separated Values - 6.0 KB - MD5: 6be7f0a965dffb60f2a17e095ea7f4e6
Uploaded with pyDataverse 2026-06-01 16:59
Comma Separated Values - 13.9 KB - MD5: 7fb0a9cb4ec3469add48484503dab76d
Uploaded with pyDataverse 2026-06-01 16:58
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