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 SAON Data Portal.
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 through the drop down menu above the text field. Text strings that are not quoted are treated as separate words and will match any of the words (i.e. assuming the OR operator). Phrases may be prefixed with '-' to indicate no occurence of the phrase in the results.
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 dataset are the raw data files from the Svalbard SuperDARN radar (data from other SuperDARN radars are also stored at this repository but this collection refers specifically to the Svalbard SuperDARN radar only). The data freely available and are archived in two open-access repositories (a username and password are required to access the system). The data repository at the British Antarctic Survey is used here. The SuperDARN community maintains two software packages to process and analyse the raw data : i) Radar Software Toolkit (RST), the primary SuperDARN data analysis software, ii) pyDARN, a python library for SuperDARN data visualisation
These open source packages are maintained and distributed by an international team of scientists, engineers and software developers.
The final (post processed) datafiles will contain line-of-sight doppler velocity, backscattered power and spectral width along the 16 pointing directions of the Svalbard SuperDARN radar at an altitude of 250km. The data coverage is of an area to the North East of Svalbard, across the polar cap, extending towards Alaska.
Institutions: JP02L, Japan Meteorological Agency, JMA, Global Environment and Marine Department, 1-3-4 Ootemachi, Chiyoda-ku, 1008122, Tokyo, Japan, JP02L, Japan Meteorological Agency, JMA, Global Environment and Marine Department, 1-3-4 Ootemachi, Chiyoda-ku, 1008122, Tokyo, Japan
Last metadata update: 2021-05-21T00:00:00Z
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Abstract:
Ground based in situ observations of sun_tracking_filter_radiometer at Minamitorishima (JP1028G). These measurements are gathered as a part of the following projects GAW-WDCA and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: aerosol_optical_depth in aerosol (atmosphere_optical_thickness_due_to_ambient_aerosol_particles), aerosol_optical_depth in aerosol (atmosphere_optical_thickness_due_to_ambient_aerosol_particles), aerosol_optical_depth in aerosol (atmosphere_optical_thickness_due_to_ambient_aerosol_particles)
Surface mass balance (SMB) for varying time periods in the past three decades were obtained by dating tracked radar reflectors. These estimated cover two ice rises (Djupranen and Leningradkollen) and the Nivlisen Ice Shelf between them in central Dronning Maud Land. This dataset consists of (i) processed radargrams used to obtain the SMB (ii) tracked reflectors with age, two-way travel time, depth, and estimated SMB and, (iii) period wise surface mass balance estimates. The data are presented as:
(i) Daily retrieved (year/month/date) processed radargrams as HDF structure files consisting of following variables: • lon: longitudes in decimal degrees • lat: latitudes in decimal degrees • x: x coordinate (m) in a polar stereographic projection EPSG:3031 • y: y coordinate (m) in a polar stereographic projection EPSG:3031 • z: elevations (m) above the WGS84 ellipsoid • wfm: matrix with radar amplitude data. • fwfm: filtered wave form i.e. matrix with radar amplitude data. • dist: distance (m) from the starting point along the profile • twt: two-way-travel time (ns) vector
(ii) Each ‘reflectors_age_depth_SMB’ files consisting of tracked reflectors and associated SMB (meter ice equivalent per year or m i.e. a-1). Each file has following variables: • name: name of the reflector tracked • lon: longitudes in decimal degrees • lat: latitudes in decimal degrees • x: x coordinate (m) in a polar stereographic projection • y: y coordinate (m) in a polar stereographic projection • twt: two-way-travel time (ns) • age (years): age of reflector (with reference to year 2016/2017 surface) • SMB: surface mass balance (m i.e a-1) • depth: Ice equivalent depth (m i.e.) from the surface 2016/2017
(iii) ESRI shapefiles consisting of SMB in meter ice equivalent (m i.e. a-1) for a specific period. Each file has following variables: • name: name of the reflector tracked with the years and period • x: x coordinate (m) in a polar stereographic projection • y: y coordinate (m) in a polar stereographic projection • SMB: surface mass balance (m i.e a-1)
Quality
If you use the dataset in presentations and publications please also refer to the peer-reviewed paper (Pratap others, 2021, accepted), where the data is described in more detail.
Contact person: Kenichi Matsuoka (kenichi.matsuoka@npolar.no) This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
Characterization of effective precipitation that occurs at ground of Antarctica region, plays a crucial rules in defining and validating global climate models and numerical weather prediction model. The observatory is designed to be set up at the Italian Antarctic station Mario Zucchelli integrating the current instrumentation for weather measurements with other instruments specific for precipitation observations. In particular, a 24-GHz vertical pointing radar, Micro Rain Radar, and an optical disdrometer, Parsivel will be integrated with the advanced weather stations, radiosoundings and the ceilometer. The synergetic use of the set of instruments allows for characterizing precipitation and studying properties of Antarctic precipitation such as dimension, shapes, fall behavior, density of particles, particles size distribution, particles terminal velocity, reflectivity factor and including some information on their vertical extent. The project is for four years, it started in July 2017 and will be active until July 2020, covering the Special Observation Period (SOP) in the Southern Hemisphere of Year of Polar Predicition (YOPP) period. APP can be provide specific measurements for precipitation occurring over the Antarctic coast at high temporal resolution, in particular specific snow products such as snow rate, snow depth and their water equivalent.
Disdrometric data from an OTT Parsivel with 32 size classes and 32 velocity classes positioned at Mario Zucchelli Station (Antarctica), with monthly spectra and particle size distributions (PSD).
The dataset consists of ice thickness derived from low-frequency radar measurements on Nivlisen ice shelf, Dronning Maud Land (70 deg S, 12 deg E). The surface elevation was not corrected for tides.
If you use the dataset in presentations and publications please also refer to the peer-reviewed paper (Lindbäck et al., 2019, https://doi.org/10.5194/tc-2019-108), where the data is described in more detail. The dataset will be updated when the quality of the data is improved or if new datasets become available.
This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
The dataset consists of ice thickness derived from low-frequency radar measurements on Djupranen and Leningradkollen ice rises, Dronning Maud Land (70 deg S, 12 deg E). Processing methods are described in the peer-reviewed paper Lindbäck et al., 2019 (https://doi.org/10.5194/tc-2019-108).
If you use the dataset in presentations and publications please also refer to this data portal reference (authors and doi). The dataset will be updated when the quality of the data is improved or if new datasets become available.
This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation.
This NISE Version 5 product contains DMSP-F18, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F18 satellite. For DMSP-F16, SSMIS-derived data, see <a href="https://doi.org/10.5067/JAQDJKPX0S60"> NISE Version 3</a>. For DMSP-F17, SSMIS-derived data, see <a href="https://nsidc.org/data/nise/versions/4"> NISE Version 4</a>. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see <a href="https://doi.org/10.5067/4FSODMDM1WEJ">NISE Version 2</a>.
Chlorophyll a concentration is the most common indicator of phytoplankton biomass, basically regulated by macro-nutrients and light intensity. Therefore, long term monitoring of these parameters gives us fundamental information on ecosystem changes with climate changes. As part of the monitoring programs of the Japanese Antarctic Research Expedition (JARE), chlorophyll a concentration and macro-nutrients (nitrate, nitrite, phosphate and silicate) have been measured on board icebreakers Fuji and Shirase from JARE-14 in the 1972/73 season and from JARE-7 in the 1965/66 season, respectively. This report is the latest data determined during the Shirase cruise in 2016/17 season.
As part of the monitoring program of the Japanese Antarctic Research Expedition (JARE), zooplankton samplings by a NORPAC (North Pacific) standard net have been routinely carried out from JARE-14 in the 1972/73 season to estimate the mean biomass of zooplankton and time/space variation in the uppermost 150 m of the Indian Ocean sector of the Southern Ocean. Two separate monitoring surveys were completed during the JARE-58 (December 2016 to March 2017). The icebreaker Shirase has a fixed time schedule and route down 110E longitude each December and return leg along 150E longitude in March. These routine schedule of Shirase is ideal as a long-term temporal reference for monitoring work. T/V Umitakam-maru transects were also along 110E longitude in January. This report are the latest data of long-term zooplankton monitoring continued by JARE over 40 years, and can be used as time series and/or seasonal data of monitoring transects.
Drifter sediment trap was deployed in the Indian sector of the Southern Ocean from December 2016 to January 2017, and in January 2018, and from December 2019 to January 2020. Fecal pellet and Gyrodinium cells were sampled using a drifter sediment trap. Size, sinking rate, and flux of fecal pellets and Gyrodinium cells were calculated using microscopic observation and an isotope-ratio mass spectrometer. The summary of the fecal pellets and Gyrodinium cells data is listed in the Excel file.
The dataset consists of basal melt and strain rates derived from phase-sensitive radars (ApRES) on Nivlisen ice shelf, Dronning Maud Land (70 deg S, 12 deg E).
If you use the dataset in presentations and publications please also refer to to the peer-reviewed paper (Lindbäck et al., 2019, https://doi.org/10.5194/tc-2019-108), where the data is described in more detail. The dataset will be updated when the quality of the data is improved or if new datasets become available.
This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
Measurements of sea ice thickness, sea ice concentration, water temperature/salinity profile, and water current profile. Monitoring of vessel movement during ice navigation.