The SIOS Data Management Service (SDMS) integrates information from SIOS partner data repositories into a unified virtual data centre, the SIOS Data Access Portal, allowing users to search for and access data regardless of where they are archived. Providers and users have to commit to the SIOS data policy.
The current focus is on dataset discovery through standardised metadata, and retrieval, visualisation & transformation of data. Ultimately, the Data Management Service works towards integration of datasets which requires a high level of interoperability at the data level.
SDMS currently harvests information on SIOS relevant datasets from a number of data centres (see below), some hosted by SIOS partners and some not. Data centres hosted by SIOS partners work to harmonise access to the data allowing integrated visualisation etc for the relevant datasets.
Data centres SDMS is harvesting information from.
SIOS partner data centres
Other
AWI (DE)
British Antarctic Survey
CNR (IT) - temporarily disabled due to server issues
National Snow and Ice Data Center
IGPAS (PL)
IMR (NO)
IOPAN (PL)
MET (NO) - weather stations have not been updated for a while, update in progress
NERSC (NO)
NILU (NO)
NIPR (JP)
NPI (NO)
UiS (PL)
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source: Research Council of Norway, project number 291644, Svalbard Integrated Arctic Earth Observing System – Knowledge Centre, operational phase.
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)
SIOS recommends all partner data repositories to mint Digital Object Identifiers (DOI) on all datasets. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
SIOS Core Data
In order to find SIOS Core Data please use the searchable item marked "Collection" on the right hand side of the map and select "SIOSCD". Quick access to SIOS Core Data is provided here.
Nansen Legacy Data
The Nansen Legacy project is using the SIOS Data Management system as the data portal. Quick access to all Nansen Legacy related datasets is available here.
Brief user guide
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 from the drop down above the text field and prefixing words with '+' to require their presence and '-' to require their non presence.
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.
The facility in Adventdalen can determine atmospheric parameters such as winds and turbulence from a few km altitude to over 100km and at a wide variety of spatial and temporal resolutions (which parameters are derived depends on altitude of the measurement).
The Sousy Svalbard Radar (SSR), is a so-called "mesosphere-stratosphere-troposphere" (MST) radar, operates at 53.5 MHz and is located in Adventdalen approximately 10km SW of Longyearbyen. The system is of the phased array type and as such has a low visual impact on the environment. Typical average power is only 200W - and thus a negligible radiation hazard (think of looking at 2 or 3 lightbulbs from several kilometers away). The MST radar is complemented with a meteor detection system extending the set of parameters.
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
Turbulent parameters are measured at the Amundsen-Nobile Climate Change Tower (CCT) by means of a Gill R3 sonic anemometer installed at 7.5 m from the ground since 2010. It measures the three components of the wind (u, v and w) and the sonic temperature at a rate of 20 Hz. These micro-meteorological measurements are complemented by standard meteorological ones at 4 levels: 2, 5, 10 and 33 m (acquisition time step equal to 1 minute). From these measurements, sensible heat flux, friction velocity and roughness length are calculated.
Wind components and sonic temperature measurements were used to estimate friction velocity and kinematic heat flux. Before computing the micrometeorological parameters, a preliminary analysis is applied in order to assess the data quality and to remove low quality records. After the quality analysis application, mean values of the turbulence statistics were computed following two coordinate rotations to ensure the mean lateral and vertical velocities were zero (McMillen, 1988). Half-hour turbulent statistics (heat fluxes and friction velocity) were derived using two time-scales: a standard averaging time of 30 min and a reduced one (2 min) necessary for filtering out submeso motions contributions that can greatly alter the estimation of turbulent fluxes in a strong and long-lived stable BL. The short averaging time scale was evaluated on the basis of spectral analysis of data in order to include all turbulent scales, but excluding submeso motions (larger than turbulence). The turbulent statistics evaluated over the short subsets and then re-averaged over 30 min following Vickers and Mahrt (2006).
Turbulent parameter relative to unfavorable wind direction ([150÷270] degrees) for which the tower was upwind of the sonic anemometer were not discarded but are flagged (flagdir=1) in the final dataset. More, the percentage of NaNs relative to each run is indicated.
The wind speed vertical profile measured by slow response standard meteorological anemometers at 2, 5, 10 and 33 m was used for estimating the roughness length assuming a typical log wind profile under statically neutral conditions.
Mahrt, L., 1998. Flux Sampling Errors for aircraft and towers. J. Atmos. Ocean. Technol. 15, 416-429.
Mc Millen, R.T., 1988. An Eddy correlation technique with extended applicability to non-simple terrain. Boundary-Layer Meteorol. 43, 231-245.
Vickers D, Mahrt L. 2006. A solution for flux contamination by mesoscale motions with very weak turbulence. Boundary-Layer Meteorol. 118: 431–447. https://doi.org/10.1007/s10546-005-9003-y.
Zahn, E., Chor, T.L., Dias, N. L., 2016. A Simple Methodology for Quality Control of Micrometeorological Datasets. American Journal of Environmental Engineering 6(4A): 135-142 DOI: 10.5923/s.ajee.201601.20.
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
The sensor used to measure the radiation budget and energy fluxes is a CNR1 net radiometer at 33 m of height and a CM11 and a CG4 at 25 m to measure the upwelling radiation from the surface.
30 minutes average (μ) and standard deviation (σ) of radiation data as well as products such as total net radiation average and shortwave albedo are available for the download.
Data at resolution of 1 minute are available for online visualization and downloadable under request.
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
30 minutes average (μ) and standard deviation (σ) of meteorological data are available for the download.
Data at resolution of 1 minute are available for online visualization and downloadable under request.
The automated nivological station was installed in November 2020 in a flat area over the tundra about 80 meters far from the Gruvebadet Atmospheric Laboratory and nearby a snow sampling site from where weekly snow samples are collected for chemical analysis. Sensors have been calibrated by their companies before installation and are connected to a datalogger for continuous acquisition. For all the parameters, data are logged with 10-minute time resolution and then averaged over 1 hour. This activity is carried out by the Aldo Pontremoli Centre part of the Joint Research Agreement ENI-CNR.
On many glaciers in Svalbard, three surface types are visible on SAR images, the dark glacier ice at the glacier's lower end, the brighter superimposed ice in the middle, and the white firn at the higher elevations. Surface classification of these types is valuable especially since the retreat or advance of the firn area provides information on the status of the glacier. While the snowline reacts immediately to annual changes, the firn area smoothes out these short-term changes and shows, similar to the glacier front, longer-term changes of the glaciers.
Glacier Firn Area Change is based on the "Glacier Surface Type - Svalbard" dataset, presenting the actual area value sper glacier and year as tabular data to be plotted graphically
On many glaciers in Svalbard, three surface types are visible on SAR images, the dark glacier ice at the glacier’s lower end, the brighter superimposed ice in the middle, and the white firn at the higher elevations. Surface classification of these types is valuable especially since the retreat or advance of the firn area provides information on the status of the glacier. While the snowline reacts immediately to annual changes, the firn area smoothes out these short-term changes and shows, similar to the glacier front, longer-term changes of the glaciers.
GST uses a Otsu three-category algorithm to separate the image into these three surface types for selected Svalbard glaciers. The method works very well on glacier with distinct surface types, the main weakness is crevasses and rough areas being classifed as superimposed ice. A quality number indicates if an individual classification is ideal (1), good (2) or of medium quality (3). The quality number mainly indicates how much crevasses are classified as superimposed ice. The firn area should be displayed correctly for all selected glaciers.
Measurements of incoming direct, diffuse, and global solar radiation and incoming infrared radiation measured at the Zeppelin Observatory in Ny-Ålesund Research Station, Svalbard.
Data are stored as one-minute averages of 1-Hz measurements. Instruments include 2 Kipp & Zonen CMP22 pyranometers (diffuse and global), 1 Kipp & Zonen CHP1 pyrheliometer (direct), and 1 Eppley PIR pyrgeometer (infrared); they are mounted on a Kipp & Zonen Solys2 sun tracker.
Quality
Data are uploaded to the database daily, without quality control. The two pyranometers and one pyrgeometer are ventilated and heated, and engineers are present once per day Monday to Friday at the station to clean the sensors if necessary. The sun does not rise at the location from late October until mid February, and the top of Zeppelin Mountain can cast a shadow on the instruments in the afternoon.
Satellite albedo data from MODIS is used to track snow-line on glaciers in Svalbard, with snow-line serving as a proxy for the Equilibrium Line Altitude (ELA). Eventually the results will be available as shapefiles.
A table with snow lines derived with this method is available and snow line can be plotted against time
Method: albedo data and snow line tracking. Sensors: MODIS.
The archipelago of Svalbard presently contains approximately 33,200 km2 of glaciers, with a large number of small valley glaciers as well as large areas of contiguous ice fields and ice caps. While a first glacier inventory was compiled in 1993, there has not been a readily available digital version. Here we present a new digital glacier database, which will be available through the GLIMS project. Glacier outlines have been created for the years 1936, 1966-71, 1990, and 2001-2010. For most glaciers, outlines are available from more than one of these years. A complete coverage of Svalbard is available for the 2001-2010 dataset. Glacier outlines were created using cartographic data from the original Norwegian Polar Institute topographic map series of Svalbard as basis by delineating individual glaciers and ice streams, assigning unique identification codes relating to the hydrological watersheds, digitizing center-lines, and providing a number of attributes for each glacier mask. The 2001-2010 glacier outlines are derived from orthorectified satellite images acquired from the SPOT-5 and ASTER satellite sensors. In areas where coverage for all time periods is available, the overwhelming majority of glaciers are observed to be in sustained retreat over the period from 1936-2010.
This study was conducted in a collaboration between the Department of Geoscience, University of Oslo, and the Norwegian Polar Institute, it was supported by the European Space Agency (ESA) through the projects Glaciers_CCI (4000101778/10/I-AM) and Cryoclim, which is also supported by the Norwegian Space Centre.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2023-12-11T08:39:51Z
Show more...
Abstract:
The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2023-12-11T08:39:30Z
Show more...
Abstract:
The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2023-12-11T08:39:30Z
Show more...
Abstract:
The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2023-12-11T08:40:12Z
Show more...
Abstract:
The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute
Last metadata update: 2023-12-11T08:39:39Z
Show more...
Abstract:
The SENTINEL-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather.