Articles | Volume 8, issue 6
https://doi.org/10.5194/tc-8-2409-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-8-2409-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Dec 2014
Research article |
| 23 Dec 2014
Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea
U. Löptien
GEOMAR Helmholtz Centre for Ocean Research, Düsternbrooker Weg 20, 24105 Kiel, Germany
L. Axell
Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
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Cited
24 citations as recorded by crossref.
- Prediction and empirical formula correction for brash ice resistance using CFD-DEM based on invisible bulbous bow ship J. Chen et al. 10.1016/j.oceaneng.2024.118752
- Sea-ice information and forecast needs for industry maritime stakeholders P. Wagner et al. 10.1080/1088937X.2020.1766592
- The role of polarseaworthiness in shipping planning for infrastructure projects in the Arctic: The case of Yamal LNG plant P. Rigot-Müller et al. 10.1016/j.tra.2021.11.009
- Economic and environmental impacts of Arctic shipping: A probabilistic approach A. Cheaitou et al. 10.1016/j.trd.2020.102606
- Mapping and analysis of maritime accidents in the Russian Arctic through the lens of the Polar Code and POLARIS system L. Fedi et al. 10.1016/j.marpol.2020.103984
- Arctic weather routing: a review of ship performance models and ice routing algorithms Q. Liu et al. 10.3389/fmars.2023.1190164
- In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice Q. Wang et al. 10.3390/app8050675
- The Northern Sea Route competitiveness for oil tankers O. Faury & P. Cariou 10.1016/j.tra.2016.09.026
- How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications D. Yang et al. 10.1080/01441647.2019.1649315
- Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea P. Pemberton et al. 10.5194/gmd-10-3105-2017
- Big maritime data for the Baltic Sea with a focus on the winter navigation system M. Lensu & F. Goerlandt 10.1016/j.marpol.2019.02.038
- Estimation of the Fatigue Damage for an Ice-going Vessel under Broken Ice ConditionPart I - Direct Approach J. Kim & Y. Kim 10.3744/SNAK.2019.56.3.217
- Data fusion and data assimilation of ice thickness observations using a regularisation framework N. Asadi et al. 10.1080/16000870.2018.1564487
- Numerical simulation on the ice-induced fatigue damage of ship structural members in broken ice fields J. Kim & Y. Kim 10.1016/j.marstruc.2019.03.002
- Remote Sensing of Ice Conditions in the Southeastern Baltic Sea and in the Curonian Lagoon and Validation of SAR-Based Ice Thickness Products I. Kozlov et al. 10.3390/rs12223754
- AIS in maritime research M. Svanberg et al. 10.1016/j.marpol.2019.103520
- Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon) R. Idzelytė et al. 10.3390/rs11172059
- Feasibility of the Northern Sea Route for seasonal transit navigation: The role of ship speed on ice and alternative fuel types for the oil product tanker market D. Theocharis et al. 10.1016/j.tra.2021.03.013
- Influence of sea ice on ship routes and speed along the Arctic Northeast Passage Y. Shu et al. 10.1016/j.ocecoaman.2024.107320
- Numerical Simulations of Sea Ice Conditions in the Baltic Sea for 2010–2016 Winters Using the 3D CEMBS Model M. Janecki et al. 10.2478/pomr-2018-0094
- Feasibility of the Northern Sea Route: The role of distance, fuel prices, ice breaking fees and ship size for the product tanker market D. Theocharis et al. 10.1016/j.tre.2019.07.003
- Development of the Analysis Procedure for the Ice-Induced Fatigue Damage of a Ship in Broken Ice Fields J. Kim 10.1115/1.4046874
- Study of ship speed regimes in the Arctic sea ice conditions A. Afonin et al. 10.1088/1755-1315/194/7/072012
- Predicting vessel speed in the Arctic without knowing ice conditions using AIS data and decision trees P. Rao et al. 10.1016/j.martra.2021.100024
24 citations as recorded by crossref.
- Prediction and empirical formula correction for brash ice resistance using CFD-DEM based on invisible bulbous bow ship J. Chen et al. 10.1016/j.oceaneng.2024.118752
- Sea-ice information and forecast needs for industry maritime stakeholders P. Wagner et al. 10.1080/1088937X.2020.1766592
- The role of polarseaworthiness in shipping planning for infrastructure projects in the Arctic: The case of Yamal LNG plant P. Rigot-Müller et al. 10.1016/j.tra.2021.11.009
- Economic and environmental impacts of Arctic shipping: A probabilistic approach A. Cheaitou et al. 10.1016/j.trd.2020.102606
- Mapping and analysis of maritime accidents in the Russian Arctic through the lens of the Polar Code and POLARIS system L. Fedi et al. 10.1016/j.marpol.2020.103984
- Arctic weather routing: a review of ship performance models and ice routing algorithms Q. Liu et al. 10.3389/fmars.2023.1190164
- In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice Q. Wang et al. 10.3390/app8050675
- The Northern Sea Route competitiveness for oil tankers O. Faury & P. Cariou 10.1016/j.tra.2016.09.026
- How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications D. Yang et al. 10.1080/01441647.2019.1649315
- Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea P. Pemberton et al. 10.5194/gmd-10-3105-2017
- Big maritime data for the Baltic Sea with a focus on the winter navigation system M. Lensu & F. Goerlandt 10.1016/j.marpol.2019.02.038
- Estimation of the Fatigue Damage for an Ice-going Vessel under Broken Ice ConditionPart I - Direct Approach J. Kim & Y. Kim 10.3744/SNAK.2019.56.3.217
- Data fusion and data assimilation of ice thickness observations using a regularisation framework N. Asadi et al. 10.1080/16000870.2018.1564487
- Numerical simulation on the ice-induced fatigue damage of ship structural members in broken ice fields J. Kim & Y. Kim 10.1016/j.marstruc.2019.03.002
- Remote Sensing of Ice Conditions in the Southeastern Baltic Sea and in the Curonian Lagoon and Validation of SAR-Based Ice Thickness Products I. Kozlov et al. 10.3390/rs12223754
- AIS in maritime research M. Svanberg et al. 10.1016/j.marpol.2019.103520
- Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon) R. Idzelytė et al. 10.3390/rs11172059
- Feasibility of the Northern Sea Route for seasonal transit navigation: The role of ship speed on ice and alternative fuel types for the oil product tanker market D. Theocharis et al. 10.1016/j.tra.2021.03.013
- Influence of sea ice on ship routes and speed along the Arctic Northeast Passage Y. Shu et al. 10.1016/j.ocecoaman.2024.107320
- Numerical Simulations of Sea Ice Conditions in the Baltic Sea for 2010–2016 Winters Using the 3D CEMBS Model M. Janecki et al. 10.2478/pomr-2018-0094
- Feasibility of the Northern Sea Route: The role of distance, fuel prices, ice breaking fees and ship size for the product tanker market D. Theocharis et al. 10.1016/j.tre.2019.07.003
- Development of the Analysis Procedure for the Ice-Induced Fatigue Damage of a Ship in Broken Ice Fields J. Kim 10.1115/1.4046874
- Study of ship speed regimes in the Arctic sea ice conditions A. Afonin et al. 10.1088/1755-1315/194/7/072012
- Predicting vessel speed in the Arctic without knowing ice conditions using AIS data and decision trees P. Rao et al. 10.1016/j.martra.2021.100024
Saved (final revised paper)
Latest update: 26 Dec 2024
Short summary
The Baltic Sea is a seasonally ice-covered marginal sea in central northern Europe. In wintertime, on-time shipping depends crucially on sea ice forecasts. Among the forecasting tools heavily applied are numerical models, which suffer from a lack of calibration data because relevant ice properties are difficult (and costly) to monitor. We developed an innovative and inexpensive approach, by using ship speed observations obtained by the automatic identification system (AIS) to asses such models.
The Baltic Sea is a seasonally ice-covered marginal sea in central northern Europe. In...
