Dybkjær, Gorm; Suhr, Magnus Barfod; Gierisch, Andrea M U; Kreiner, Matilde Brandt; Ribergaard, Mads Hvid; Wulf, Tore; Singha, Suman (2025): Arctic PASSION - High Resolution Synthetic Aperture Radar based Risk Index Outcome (AP-RIO) [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.975054
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Published: 2025-03-24 • DOI registered: 2025-04-22
Abstract:
The Risk Index Outcome (RIO) is a critical component of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) developed by the International Maritime Organization (IMO, 2016). RIO evaluates the operational risks for ships navigating in ice-infested waters by evaluating ice conditions and offers a quantifiable measure of risk that aids in decision-making for safe navigation in polar regions based on ship ice class, sea ice type/stage of development (SOD) and sea ice concentration (SIC). The DMI-led Automated Sea Ice Products (DMI-ASIP; Wulf et al., 2024, dataset) provides daily maps of SOD and SIC based on Sentinel-1 SAR imagery, AMSR-2 Passive Microwave and Ice Charts from the Greenland and Canadian Ice Services, combined with novel AI retrieval and processing techniques. In the framework of EU funded Arctic PASSION project, we produced 10 years of satellite observation based weekly RIO maps referred as the Arctic PASSION-RIO (AP-RIO) by leveraging DMI-ASIP datasets. The AP-RIO dataset will provide weekly risk assessment maps for the given ship classes and will support the establishment of a 10 year climatology thereby enabling the assessment of RIO variability in the years covered by the input DMI-ASIP products. The AP-RIO dataset will enhance the safety and efficiency of maritime operations in the polar seas, providing a robust reference for evaluating normal and extreme ice conditions. AP-RIO is produced in the framework of the Arctic PASSION project (European Union's Horizon 2020 research and innovation program under grant agreement No. 101003472) and supported by the DMI-ASIP development team.
Algorithm and Processing Scheme:
SIC and SOD from ASIP are processed (by taking the mean and mode respectively) into a weekly field based on the daily files for that week. This is done for the time period of 3 Oct. 2014 - 3 Oct. 2024. The weekly SOD is used to find the Risk Value (RV) by looking at the lookup table (Dybkjær et al. 2025a). Risk Index Outcome (RIO) values are computed for each pixel in the field based on the RIO formula (RIO = SIC x RV) using the SIC from ASIP and the found RV. The meaning of the computed RIO values can be interpreted using the table in (Dybkjær et al. 2025b). The RIO field is finally saved to weekly NetCDF files.
Keyword(s):
References:
European Union-Copernicus Marine Service (2024): Arctic Ocean - High Resolution Sea Ice Information L3 [dataset]. Mercator Ocean International, https://doi.org/10.48670/MDS-00343
International Maritime Organization (IMO) (2016): Guidance on methodologies for assessing operational capabilities and limitations in ice. https://www.imorules.com/MSCCIRC_1519.html
Wulf, Tore; Buus-Hinkler, Jørgen; Singha, Suman; Shi, Hoyeon; Kreiner, Matilde Brandt (2024): Pan-Arctic sea ice concentration from SAR and passive microwave. The Cryosphere, 18(11), 5277-5300, https://doi.org/10.5194/tc-18-5277-2024
Further details:
Dybkjær, Gorm; Suhr, Magnus Barfod; Gierisch, Andrea M U; Kreiner, Matilde Brandt; Ribergaard, Mads Hvid; Wulf, Tore; Singha, Suman (2025): RIO classes for 3 selected ship classes. RIO classes for 3 selected ship classes.png
Dybkjær, Gorm; Suhr, Magnus Barfod; Gierisch, Andrea M U; Kreiner, Matilde Brandt; Ribergaard, Mads Hvid; Wulf, Tore; Singha, Suman (2025): Risk Value (RV) lookup table based on SOD from ASIP. Risk Value (RV) lookup table based on SOD from ASIP.png
Project(s):
Funding:
Horizon 2020 (H2020), grant/award no. 101003472: Pan-Arctic observing System of Systems: Implementing Observations for societal Needs (Arctic PASSION)
Coverage:
Median Latitude: 67.500000 * Median Longitude: 180.000000 * South-bound Latitude: 45.000000 * West-bound Longitude: 179.999999 * North-bound Latitude: 90.000000 * East-bound Longitude: -179.999999
Date/Time Start: 2014-10-03T00:00:00 * Date/Time End: 2025-01-02T00:00:00
Event(s):
AP-RIO (Arctic PASSION Risk Index Outcome) * Latitude Start: 90.000000 * Longitude Start: 179.999999 * Latitude End: 45.000000 * Longitude End: -179.999999 * Date/Time Start: 2014-10-03T00:00:00 * Date/Time End: 2025-01-02T00:00:00 * Location: Arctic Ocean * Method/Device: Risk Index Outcome (RIO) values from calculation
Comment:
NetCDF file example of the AP-RIO data set:
netcdf RIO_Lthick_new_PC2_PC7_1C_mean_20150109_20150115
{ dimensions:
time = UNLIMITED ; // (1 currently)
yc = 15100 ;
xc = 10400 ;
shipclass = 3 ;
variables:
float lon(yc, xc) ;
lon:_FillValue = NaNf ;
lon:least_significant_digit = 3LL ;
lon:standard_name = longitude ;
lon:long_name = longitude coordinate ;
lon:units = degrees_east ;
lon:grid_mapping = crs ;
lon:coordinates = lon lat ;
float lat(yc, xc) ;
lat:_FillValue = NaNf ;
lat:least_significant_digit = 3LL ;
lat:long_name = latitude coordinate ;
lat:standard_name = latitude ;
lat:units = degrees_north ;
lat:grid_mapping = crs ;
lat:coordinates = lon lat ;
double time(time) ;
time:_FillValue = NaN ;
time:standard_name = time ;
time:units = days since 1970-01-01 ;
time:calendar = proleptic_gregorian ;
double xc(xc) ;
xc:_FillValue = NaN ;
xc:axis = X ;
xc:standard_name = projection_x_coordinate ;
xc:long_name = x-coordinate in cartesian system ;
xc:units = m ;
double yc(yc) ;
yc:_FillValue = NaN ;
yc:axis = Y ;
yc:standard_name = projection_y_coordinate ;
yc:long_name = y-coordinate in cartesian system ;
yc:units = m ;
byte shipclass(shipclass) ;
double RIO(time, shipclass, yc, xc) ;
RIO:_FillValue = NaN ;
RIO:long_name = Risk Index Outcome (POLARIS) ;
RIO:units = [] ;
RIO:comment = Ship classes included in this file: PC2, PC7, 1C ;
RIO:comment2 = Meaning of shipclass-dimension: 1:PC1, 2:PC2, 3:PC3, 4:PC4, 5:PC5, 6:PC6, 7:PC7, 10:1ASuper, 11:1A, 12:1B, 13:1C, 20:noclass ;
RIO:coordinates = lat lon ;
// global attributes:
:title = Risk Index Outcome (RIO) calculated from DMI\'s ASIP data ;
:history = Created by RIOengine_ASIP.py on 2024-12-05 ;
: acknowledgement = Used RIO calculation method: Lthick_new. This dataset has been funded by the European Union's Horizon 2020 research and innovation programme through the project Arctic PASSION under grant agreement No. 101003472. ;
:institution = Danish Meteorological Institute, DMI ;
:Contact = gd@dmi.dk or msu@dmi.dk ;
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File name | File name | Suhr, Magnus Barfod | Satellite imagery (SATI) | ||
| 2 | File content | Content | Suhr, Magnus Barfod | Satellite imagery (SATI) | ||
| 3 | Binary Object | Binary | Suhr, Magnus Barfod | Satellite imagery (SATI) | ||
| 4 | Binary Object (Media Type) | Binary (Type) | Suhr, Magnus Barfod | Satellite imagery (SATI) | ||
| 5 | Binary Object (File Size) | Binary (Size) | Bytes | Suhr, Magnus Barfod | Satellite imagery (SATI) | |
| 6 | Binary Object (MD5 Hash) | Binary (Hash) | Suhr, Magnus Barfod | Satellite imagery (SATI) |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Enhanced curation (CurationLevelC) * Processing Level: PANGAEA data processing level 1 (ProcLevel1)
Size:
1605 data points