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.
This ocean model is operated at 20km resolution covering the Nordic Seas
and the Arctic Ocean. This specific dataset provides the daily analysis
from the operational model. Only the analysis is provided for historical
periods, the daily forecast with 1 hour resolution is provided as a
separate dataset. Currently the WMS presentation of this dataset is not
supporting the 3D nature.
A numerical model is applied to describe the dynamics of the oceans, such
as sea level variations (tides and storm surge), movements in the water
column (currents) and the salinity and temperature. To simulate the ocean,
a 3-D grid is applied with different sizes, i.e., small grids for fine
scale or detailed calculations, and larger or coarser grids to cover
larger areas (and depth). The model runs on a supercomputer, and provides
forecasts of sea level, currents, salinity and temperature for a
time-range between 66 (2.75 days) and 240 hours (10 days). The model is
run operationally, i.e, in a "24/7/365" environment to provide a 99.5%
stability on a yearly basis. Currents from the model is further applied in
emergency-models that simulates pathways of oil slicks and drifting
objects (Search And Rescue).
The ocean model used is the Regional Ocean Modeling System (ROMS). This is
a three-dimensional, free-surface, terrain-following numerical model that
solve the Reynolds-averaged Navier-Stokes equations using the hydrostatic
and Boussinesq assumptions (Haidvogel et al., 2008).
Haidvogel, D. B., H. Arango, W. P. Budgell, B. D. Cornuelle, E.
Curchitser, E. Di Lorenzo, K. Fennel, W. R. Geyer, A. J. Hermann, L.
Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M.
Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner,
and J. Wilkin, Ocean forecasting in terrain-following coordinates:
Formulation and skill assessment of the Regional Ocean Modeling System,
JOURNAL OF COMPUTATIONAL PHYSICS, 227, 3595–3624, 2008.
THIS MODEL IS DISCONTINUED AND NO FORECAST DATA IS AVAILABLE ONLINE.
This ocean model is operated at 20km resolution covering the Nordic Seas
and the Arctic Ocean. This specific dataset provides the hourly forecast
fields from the operational model. For historical purposes, the daily
analysis is provided as another dataset. If for some reason the
historical forecast is required, pleased use the contact information
provided to receive this (manual task).
A numerical model is applied to describe the dynamics of the oceans, such
as sea level variations (tides and storm surge), movements in the water
column (currents) and the salinity and temperature. To simulate the ocean,
a 3-D grid is applied with different sizes, i.e., small grids for fine
scale or detailed calculations, and larger or coarser grids to cover
larger areas (and depth). The model runs on a supercomputer, and provides
forecasts of sea level, currents, salinity and temperature for a
time-range between 66 (2.75 days) and 240 hours (10 days). The model is
run operationally, i.e, in a "24/7/365" environment to provide a 99.5%
stability on a yearly basis. Currents from the model is further applied in
emergency-models that simulates pathways of oil slicks and drifting
objects (Search And Rescue).
The ocean model used is the Regional Ocean Modeling System (ROMS). This is
a three-dimensional, free-surface, terrain-following numerical model that
solve the Reynolds-averaged Navier-Stokes equations using the hydrostatic
and Boussinesq assumptions (Haidvogel et al., 2008).
Haidvogel, D. B., H. Arango, W. P. Budgell, B. D. Cornuelle, E.
Curchitser, E. Di Lorenzo, K. Fennel, W. R. Geyer, A. J. Hermann, L.
Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M.
Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner,
and J. Wilkin, Ocean forecasting in terrain-following coordinates:
Formulation and skill assessment of the Regional Ocean Modeling System,
JOURNAL OF COMPUTATIONAL PHYSICS, 227, 3595–3624, 2008.
This repository includes datasets for the ice nucleating particle samples collected in Svalbard. The data on the ice nucleation active site densities per unit mass (n_m) for glacial outwash sediments (before and after H2O2 treatment) were obtained from the samples collected at two points near the glacier Broggerbreen in Svalbard. The data on the number concentrations of atmospheric ice nucleating particles (N_INP) were obtained from the samples collected at the Zeppelin Observatory in Ny-Alesund, Svalbard, during the intensive measurements campaigns in July 2016 (6 samples) and March 2017 (7 samples). The 95% confidence intervals for these data are also presented.
Historical AROME Arctic files from the operational numerical weather prodiction model run. The moste recent datasets are also available labelled post-processed or extracted as separate datsets.
Extracted variables based on the latest run of the AROME-Arctic model, without additional post-processing. Data on surface, and selected model and pressure levels. Horizontal data resolution is 2,5km. The forecast is updated 4 times per day. For historical data see https://thredds.met.no/thredds/catalog/aromearcticarchive/catalog.html
Post processed forecasts based on the latest run of the AROME-Arctic model. Parameters like temperature, cloud cover, precipitation and wind have gone through additional post-processing. Horizontal data resolution is 2,5km. The forecast is updated 4 times per day. For historical data see https://thredds.met.no/thredds/catalog/aromearcticarchive/catalog.html
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T15:00:52Z
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Abstract:
Sea ice concentration charts based on a manual interpretation of different satellite data. The main satellite sensor used are the SAR sensor (Synthetic Aperture Radar) suplemented by visual and infrared sensors and data from passive microwave sensors. As part of the Copernicus project the sea ice concentration product is gridded to a 1km spatial resoluton and converted to a NetCDF format. The concentration intervals follow the World Meteorological Organization (WMO) total concentration standard. A new product is delivered every weekday around 1500 UTC.
Institutions: Norwegian Meteorological Institute, Norwegian Meteorological Institute / Arctic Data Centre
Last metadata update: 2022-11-15T15:00:52Z
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Abstract:
The product is based on a manual interpolation of available satellite data and insitu observations and provides a gridded map. It is a continuation of the previous sea ice chart which basically identified the ice edge.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from BARDUFOSS
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from KONGSOYA
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from HEKKINGEN LH
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from HORNSUND
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from JAN MAYEN
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from NY-ALESUND
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.
WMO Year of Polar Prediction, Svalbard Integrated Arctic Earth Observing System (YOPP, APPLICATE, SIOS)
Institutions: Norwegian Meteorological Institute
Last metadata update: 2022-11-15T14:48:52Z
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Abstract:
Synoptic meteorological measurements from VERLEGENHUKEN
extracted from the WMO Global Telecommunication System (GTS).
Data are not quality controlled after extraction from GTS.