Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5731-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-5731-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Sep 2021
Model description paper |
| 16 Sep 2021
| 16 Sep 2021
Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea
Finnish Meteorological Institute, Helsinki, Finland
Patrik Ljungemyr
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Saeed Falahat
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Ida Ringgaard
Danish Meteorological Institute, Copenhagen, Denmark
Lars Axell
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Vasily Korabel
Danish Meteorological Institute, Copenhagen, Denmark
Jens Murawski
Danish Meteorological Institute, Copenhagen, Denmark
Ilja Maljutenko
Tallinn University of Technology, Tallinn, Estonia
Anja Lindenthal
Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany
Simon Jandt-Scheelke
Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany
Svetlana Verjovkina
Tallinn University of Technology, Tallinn, Estonia
Ina Lorkowski
Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany
Priidik Lagemaa
Tallinn University of Technology, Tallinn, Estonia
Jun She
Danish Meteorological Institute, Copenhagen, Denmark
Laura Tuomi
Finnish Meteorological Institute, Helsinki, Finland
Adam Nord
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Vibeke Huess
Danish Meteorological Institute, Copenhagen, Denmark
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Cited
19 citations as recorded by crossref.
- Wind-driven salinity tongue migration in the Gulf of Finland according to NEMO and ERA5 reanalyses V. Zhurbas et al. 10.1016/j.jmarsys.2023.103932
- Examining the performance of along-track multi-mission satellite altimetry – A case study for Sentinel-6 L. Heinoja et al. 10.1515/jogs-2022-0159
- Phase-field chemo-mechanical modelling of corrosion-induced cracking in reinforced concrete subjected to non-uniform chloride-induced corrosion E. Korec et al. 10.1016/j.tafmec.2023.104233
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
- Transport dynamics in a complex coastal archipelago E. Miettunen et al. 10.5194/os-20-69-2024
- Treatment of Tide Gauge Time Series and Marine GNSS Measurements for Vertical Land Motion with Relevance to the Implementation of the Baltic Sea Chart Datum 2000 S. Varbla et al. 10.3390/rs14040920
- An Initial Field Intelligent Correcting Algorithm for Numerical Forecasting Based on Artificial Neural Networks under the Conditions of Limited Observations: Part I—Focusing on Ocean Temperature K. Mao et al. 10.3390/jmse10030311
- A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters C. Liu et al. 10.1016/j.oceaneng.2022.112914
- Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations J. Schulz-Stellenfleth et al. 10.3390/jmse11091817
- Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea M. Lorenz & U. Gräwe 10.5194/os-19-1753-2023
- Towards realistic dynamic topography from coast to offshore by incorporating hydrodynamic and geoid models V. Jahanmard et al. 10.1016/j.ocemod.2022.102124
- Efficient Optimization of a Regional Water Elevation Model With an Automatically Generated Adjoint T. Kärnä et al. 10.1029/2022MS003169
- Iterative data assimilation approach for the refinement of marine geoid models using sea surface height and dynamic topography datasets S. Varbla & A. Ellmann 10.1007/s00190-023-01711-7
- NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China Y. Yan et al. 10.5194/gmd-15-1269-2022
- Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data L. Chaudhary et al. 10.1007/s11831-023-09970-5
- Refined estimates of water transport through the Åland Sea in the Baltic Sea A. Westerlund et al. 10.5194/os-18-89-2022
- Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography S. Varbla et al. 10.3390/rs14102368
- An ocean–wave–trajectory forecasting system for the eastern Baltic Sea: Validation against drifting buoys and implementation for oil spill modeling S. Pärt et al. 10.1016/j.marpolbul.2023.115497
- Quantification of hydrodynamic model sea level bias utilizing deep learning and synergistic integration of data sources V. Jahanmard et al. 10.1016/j.ocemod.2023.102286
19 citations as recorded by crossref.
- Wind-driven salinity tongue migration in the Gulf of Finland according to NEMO and ERA5 reanalyses V. Zhurbas et al. 10.1016/j.jmarsys.2023.103932
- Examining the performance of along-track multi-mission satellite altimetry – A case study for Sentinel-6 L. Heinoja et al. 10.1515/jogs-2022-0159
- Phase-field chemo-mechanical modelling of corrosion-induced cracking in reinforced concrete subjected to non-uniform chloride-induced corrosion E. Korec et al. 10.1016/j.tafmec.2023.104233
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
- Transport dynamics in a complex coastal archipelago E. Miettunen et al. 10.5194/os-20-69-2024
- Treatment of Tide Gauge Time Series and Marine GNSS Measurements for Vertical Land Motion with Relevance to the Implementation of the Baltic Sea Chart Datum 2000 S. Varbla et al. 10.3390/rs14040920
- An Initial Field Intelligent Correcting Algorithm for Numerical Forecasting Based on Artificial Neural Networks under the Conditions of Limited Observations: Part I—Focusing on Ocean Temperature K. Mao et al. 10.3390/jmse10030311
- A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters C. Liu et al. 10.1016/j.oceaneng.2022.112914
- Fit-for-Purpose Information for Offshore Wind Farming Applications—Part-II: Gap Analysis and Recommendations J. Schulz-Stellenfleth et al. 10.3390/jmse11091817
- Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea M. Lorenz & U. Gräwe 10.5194/os-19-1753-2023
- Towards realistic dynamic topography from coast to offshore by incorporating hydrodynamic and geoid models V. Jahanmard et al. 10.1016/j.ocemod.2022.102124
- Efficient Optimization of a Regional Water Elevation Model With an Automatically Generated Adjoint T. Kärnä et al. 10.1029/2022MS003169
- Iterative data assimilation approach for the refinement of marine geoid models using sea surface height and dynamic topography datasets S. Varbla & A. Ellmann 10.1007/s00190-023-01711-7
- NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China Y. Yan et al. 10.5194/gmd-15-1269-2022
- Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data L. Chaudhary et al. 10.1007/s11831-023-09970-5
- Refined estimates of water transport through the Åland Sea in the Baltic Sea A. Westerlund et al. 10.5194/os-18-89-2022
- Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography S. Varbla et al. 10.3390/rs14102368
- An ocean–wave–trajectory forecasting system for the eastern Baltic Sea: Validation against drifting buoys and implementation for oil spill modeling S. Pärt et al. 10.1016/j.marpolbul.2023.115497
- Quantification of hydrodynamic model sea level bias utilizing deep learning and synergistic integration of data sources V. Jahanmard et al. 10.1016/j.ocemod.2023.102286
Latest update: 25 Apr 2024
Short summary
We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model...
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