The Data Access Portal has information in 3 columns. An outline of the content in these columns is provided above. When first entering the search interface, all potential datasets are listed. Datasets are indicated in the map and results tabulation elements which are located in the middle column. The order of results can be modified using the "Sort by" option in the left column. On top of this column is normally relevant guidance information to user presented as collapsible elements.
If the user want to refine the search, this can be done by constraining the bounding box search. This is done in the map - the listing of datasets is automatically updated. Date constraints can be added in the left column. For these to take effect, the user has to push the button marked search. In the left column it is also possible to specific text elements to search for in the datasets. Again pushing the button marked "Search" is necessary for these to take action. Complex search patterns can be constructed using logical operators and phrases embedded in quotation marks. Logical operators include AND, OR and NOT. Remember to add space around operators. Text strings that are not quoted are trated as separate words and will match any of the words (i.e. assuming the OR operator). E.g. in order to find WMO synoptic weather station data from Verlegenhuken use the search phrase: [synop AND verlegenhuken]. Searches are case insensitive.
Other elements indicated in the left and right columns are facet searches, i.e. these are keywords that are found in the datasets and all datasets that contain these specific keywords in the appropriate metadata elements are listed together. Further refinement can be done using full text, date or bounding box constraints. Individuals, organisations and data centres involved in generating or curating the datasets are listed in the facets in the right column.
Collections
Collections allows the user to search in subsets of the existing catalogue. The collections are primarily data management projects that have been incorporated in the ADC catalogue after the project has ended. In this context the ADC is the long term access solution for these data. The collections currently served through ADC include (datasets may belong to multiple data collections):
ADC is the full collection of this service CC is the CryoClim collection
In order to search a specific data collection select that collection. If no data collection is selected all collections are searched.
AeN are data related to the Nansen Legacy project and are better explored through the SIOS Data Access Point using the collection defined there which is available through this URL.
SIOS, InfraNOR, SIOSCD, SIOSAP, SESS_* are collections related to SIOS. These are better explored through the SIOS Data Access Portal
Some cleaning is pending between InfraNOR and SIOSIN, for some of the SESS collections.
Citation of data and service
Always remember to cite data when used!
Citation information for individual datasets is often provided in the metadata. However, not all datasets have this information embedded in the discovery metadata. On a general basis a citation of a dataset include the same components as any other citation:
author,
title,
year of publication,
publisher (for data this is often the archive where it is housed),
edition or version,
access information (a URL or persistent identifier, e.g. DOI if provided)
The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
If you use data retrieved through this portal, please acknowledge the Norwegian Meteorological Institute/Arctic Data Centre.
This dataset contains annually averaged ice surface velocity and thickness for all 202 tidewater glacier fronts on Svalbard, dating from 2012 to 2021. This is combined with mapping of front position changes to derive annual ice mass rates for retreat/advance, ice flux (discharge) and total frontal ablation.
The dataset is planned to be updated with new results every autumn.
Quality
Ice thickness was calculated with the help of surface (annual mosaics of ArcticDEM strip data) and bedrock DEMs (SVIFT v1.0 from Fürst et al., 2018; NPI DEM, and available bathymetry data from the Norwegian Mapping Authority). Velocity data were obtained from 3 datasets: Landsat-8 velocities from the ITS-Live product (2013 to 2018), and Sentinel-1 velocities derived by Adrian Luckman (2015 to 2020) and by Friedl et al. (2021) (2015 to 2021).Mean velocities values were calculated for each glacier when different datasets were available for similar years. Mass rates for retreat/advance were derived from front position changes manually digitized based on available satellite or aerial imagery, mainly acquired by Sentinel-2 or Landsat-8 during the period 15 Aug. to 15 Sept. each year and from ice thickness data. Ice flux (discharge) was derived from annual velocity data and ice thickness data. Frontal ablation was calculated as the combination of the ice mass rate and the discharge of ice.
Quantarctica - A free GIS package for research, education, and operation in Antarctica
Last metadata update: 2018-02-12T09:28:30Z
Show more...
Abstract:
Quantarctica is a collection of Antarctic geographical datasets which works with the free, cross-platform, open-source software QGIS. It includes community-contributed, peer-reviewed data from ten different scientific themes and a professionally-designed basemap.
Quality
The Quantarctica Editorial Board selects peer-reviewed datasets for a wide range of Antarctic users, including over 150 basemap and scientific data layers and thematic coverage from Glaciology and Geophysics to other themes such as Atmospheric Science, Biology, Oceanography, Social Sciences, and more. The Quantarctica project team at the Norwegian Polar Institute then incorporates these layers into the Quantarctica data package by importing, reprojecting, styling, labeling, and organizing them for user-friendly presentation.
This dataset provides the positions of icebergs in August 2012, August 2013 and August 2014 along the Dronning Maud Land coast.
It also provides positions of lingered icebergs over five periods:
- August 2012 to October 2012 (2 months)
- August 2013 to October 2013 (2 months)
- August 2012 to August 2013 (1 year)
- August 2013 to August 2014 (1 year)
- August 2012 to August 2014 (2 years)
These icebergs were identified by a human operator using Radarsat2’s ScanSAR Narrow images. Data are provided in point shapefile format. The attribute for each point are the bed elevation (meters above sea level) at the point position according to IBCSO bathymetry dataset (Arndt et al., 2013; “ibcso_b”) and the method used to determine the elevation (“ibcso_s”). If the first digit of “ibcso_s” is a 2, “ibcso_b” was acquired using multi-beam sonar, if the first digit is a 3 data were acquired using single-beam sonar, and if the first digit is a 6, data were interpolated. Each file is named after the date at which the iceberg were tracked. For example, icebergs lingered between August 2012 and October 2012 are stored in SIB_8_12-10_12_IBCSO.shp. Icebergs found in August 2012 are stored in SIB_8_12_IBCSO.shp.
This dataset provides the characteristic timescales (T = ice thickness divided by surface mass balance) for ice rises in Dronning Muad Land and Enderby Land, East Antarctica. T is a useful parameter to examine the stability of ice rises and their suitability for studies in ice dynamics and paleoclimate. The dataset includes data used for estimating T as well as ice thickness, surface mass balance of these ice rises obtained from various sources. This dataset is a supplement to the peer-reviewed paper (Goel et al., 2020, in press), which can be referred for further details.
This dataset provides Dronning Maud Land ice shelf front positions in August 2012, February 2013, March 2013, August 2013, October 2013 and December 2013 determined using Radarsat2’s ScanSAR narrow imagery. The area extent goes from 5 W to 34 E. The front position has been digitized by human operator using QGIS digitizing features. To determine the front position in each occasion, images taken over about two weeks were used. Data are provided in shapefile format. In each shapefile, the front position is represented by numerous line segments. Individual segments have following attributes:
- Date at which the image was taken (attributes “year”,”month”,”day”)
- Qualitative measure of the position uncertainty (attribute “quality”, number can be 0 or 1). If the quality is 1, the front position is confidently determined. If the quality is 0, the front position is poorly determined. These quality indicators are assigned qualitatively by the operator.
Each file is named after the occasion at which the imagery was acquired. For example the ice shelves front position in February 2013 is stored in IS_front_2013_2.shp .
This dataset provides surface elevations of three ice rises, Blåskimen Land (IRD), Kupol Ciolkovskogo (IRB) and Kupol Moskovskij (IRC), around the Fimbul Ice Shelf, in Western Dronning Maud Land.
The digital elevation models (DEMs) were developed using kinematic GPS measurements tied to a local base station on each ice rise. The DEMs are projected to a polar stereographic projection parallel to 71°S (GOCE gravity product (http://www.opengis.net/def/crs/EPSG/0/3031)) was subtracted from the heights above the WGS84 ellipsoid.
The data are provided in MATLAB (.mat) file format. For each ice rise the files are named such as ‘IRB_DEM.mat and ‘IRB_GPS.mat’:
IRB_DEM.mat includes a struct-class variable. XX and YY show the coordinates, and ZZ shows elevations. All are in the unit of meters. IRB_GPS.mat include a struct-class variable. LAT and LON are decimal degrees, and Z is in meters. IRB_DEM.png shows the DEM in color and contours (10 m intervals) together with GPS tracks (white). It is plotted against a local coordinate, which is parallel to EPSG3031 but the origin is the summit of the ice rise.
GPS data for IRD provided here include erroneous data that show (both positive and negative) spikes in the elevations; these data are outliers from adjacent data points, and in some cases the spike height reaches 5-10 m. These spikes appear near the summit and other places. Because the number of erroneous data points is quite small, these features do not significantly affect the DEM.
See the following manuscript for further details: Goel, V., Brown, J., and Matsuoka, K.: Glaciological settings and recent mass balance of Blåskimen Island in Dronning Maud Land, Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-61, in review, 2017.
On Svalbard, the long-lasting snow cover and the timing of the snowmelt is a crucial factor in the yearly cycle of all land ecosystems. To monitor the timing and patterns of snow melt, automatic camera systems have been set up at three locations overlooking key research areas near Ny-Ålesund, Svalbard. All images are provided in daily resolution, and the date coded in the filename as yyyy-MM-dd. This work was funded by SMACS (project no. 236768 / E10; Svalbard Science Forum, Research Council of Norway). ** For all details see the full metadata description at "https://doi.pangaea.de/10.1594/PANGAEA.846617"!
The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestiral automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway.
This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.
This data set contains in situ vegetation data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set consists of soil texture classification data derived from field surveys as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). The soil texture classification map provides information about vegetation present in the study area.
This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.
This data set contains soil moisture data obtained by the Passive Active L-band System (PALS) aircraft instrument. The data were collected as part of SMAPVEX12, the Soil Moisture Active Passive Validation Experiment 2012.
This data set contains surface roughness data collected at several agricultural sites as a part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12).
This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.