Our operational ocean models are built on the Regional Ocean Modeling System (ROMS). ROMS solves the Reynolds averaged, hydrostatic primitive equations using a bottom-following coordinate system with free surface (Shchepetkin and McWilliams, 2005).
The operational archive of the model is available on THREDDS. All analysis runs (to create a continuous time series) as well as the latest forecast are included.
The Barents-2.5 model is a coupled ocean and sea ice model covering the Barents Sea and areas around Svalbard. It is MET Norway’s main forecasting model for sea ice in the Barents Sea. The model is based on the METROMS (GitHub) framework which implements the coupling between the ocean component (ROMS) and the sea ice component (CICE). The model employs a regular grid in the horizontal with 2.5km resolution, and an irregular topography-following vertical coordinate system for the ocean consisting of 42 layers, while the ice is modelled in 5 thickness categories, each with 7 vertical layers and a single snow layer on top. The ocean and sea ice is forced by atmospheric fields from MET Norway’s in-house 2.5km AROME-Arctic model, a great advantage as the atmosphere forcing is on the same domain and resolution as the ocean and sea ice. Furthermore, boundary conditions comes from TOPAZ4, tides from TPXO tidal model, river runoff climatology from NVE data (mainland Norway) and AHYPE hydrological model (Svalbard+Russia) and the bottom topography is taken from the IBCAO v3 dataset. The model runs a 24 hours analysis for assimilating AMSR2 sea ice concentration from the University of Bremen and then runs a subsequent 66 hours forecast from the produced analysis. Daily updated validation results for the sea ice forecast are available here.
Duarte, P., Brændshøi, J., Shcherbin, D., Barras, P., Albretsen, J., Gusdal, Y., Szapiro, N., Martinsen, A., Samuelsen, A., Wang, K., Debernard, J.B, 2022. Implementation and evaluation of open boundary conditions for sea ice in a regional coupled ocean (ROMS) and sea ice (CICE) modeling system. Geosci. Model Dev., 15, 4373–4392. https://doi.org/10.5194/gmd-15-4373-2022
Fritzner, S.M., Graversen, R.G., Wang, K., Christensen, K.H., 2018. Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation. Journal of Glaciology 64, 387–396. https://doi.org/10.1017/jog.2018.33
Fritzner, S.M., Graversen, R., Christensen, K.H., Rostosky, P., Wang, K., 2019. Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system. Ocean Sci. 13, 491–509. https://doi.org/10.5194/tc-13-491-2019
The stormsurge model at MET Norway is based on the ROMS model in barotropic mode (2D). The domain covers an area from Bretagne in France, through the North Sea, around the British Isles, the Norwegian coast, the Barents Sea and parts of the eastern coast of Greenland and the Nordic Sea. The model has a horizontal resolution of 4km and is forced by atmospheric forcing from the MEPS 2.5km atmospheric model. Five days Forecasts (120 hours) produced and updated every 6 hour. The Forecasts are postprocessed and nudged towards observations from Norwegian coastal stations.
In addition to this setup, a similar setup is run in ensemble mode (EPS). This produces a 50+1 forecast for +120h twice per day based on the full ECMWF ensemble forecast system. Results are made available through api.met.no (https://api.met.no/weatherapi/tidalwater/1.1/documentation) and can also be viewed on SeHavnivå.
Kristensen, N.M., Røed, L.P., Sætra, Ø., 2022. A forecasting and warning system of storm surge events along the Norwegian coast. Environmental Fluid Mechanics. https://doi.org/10.1007/s10652-022-09871-4
Norkyst-800 is used as the main forecast tool for ocean forecasting at the coast of Norway. This includes forecasting of sea surface temperatures and ocean currents in oil spill preparedness modeling, Search-and-Rescue preparedness models, and plankton dispersion models. All analysis runs are available on THREDDS.
Norkyst-800 is our coastal and shelf sea ocean circulation model. The setup is based on ROMS with a horizontal resolution of 800m and 35 vertical layers, hence permitting mesoscale eddies. The most dominant features of the coastline are resolved including all major fjords, yet not the smallest islands and bays. It receives tidal forcing from a global inverse barotropic model of ocean tides,TPXO7.26, through prescription of amplitude and phase for sea surface elevation and currents for the major eight primary harmonic constituents (M2, S2, N2, K1, K2, O1, P1, Q1) of diurnal and semidiurnal frequencies. Atmospheric forcing is provided by surface fields from AROME-MetCoOP. Lateral boundary conditions for Norkyst-800 are provided by Topaz and the CMEMS Baltic sea model for temperature and salinity in the Kattegat area. "For the Norwegian rivers, real-time runoff data based on daily observed runoff are collected from The Norwegian Water Resources and Energy Directorate (NVE)". Norkyst-800 calculates atmospheric fluxes through a bulk formula formulation based on atmospheric variables, and vertical turbulence is parameterized through the k-gen GLS mixing scheme.
The NorKyst800 model is a collaboration project between the Institute of Marine Research (IMR) and the Norwegian Meteorological Institute.
Albretsen, J., Sperrevik, A.K., Staalstrøm, A., Sandvik, A.D., Vikebø, F., Asplin, L., 2011. NorKyst-800 Rapport nr. 1 : Brukermanual og tekniske beskrivelser. NorKyst-800 Report No. 1 : User Manual and technical descriptions.
Röhrs, J., Christensen, K.H., Vikebø, F.B., Sundby, S., Saetra, O., Broström, G., 2014. Wave-induced transport and vertical mixing of pelagic eggs and larvae. Limnol. Oceanogr. 59(4), 1213–1227. https://doi.org/10.4319/lo.2014.59.4.1213
Idžanović, M., Ophaug, V., Andersen, O.B., 2017. The coastal mean dynamic topography in Norway observed by CryoSat-2 and GOCE. Geophysical Research Letters 44, 5609–5617. https://doi.org/10.1002/2017GL073777
Myksvoll, M.S., Sandvik, A.D., Albretsen, J., Asplin, L., Johnsen, I.A., Karlsen, Ø., Kristensen, N.M., Melsom, A., Skardhamar, J., Ådlandsvik, B., 2018. Evaluation of a national operational salmon lice monitoring system—From physics to fish. PLOS ONE 13, e0201338. https://doi.org/10.1371/journal.pone.0201338
Asplin, L., Albretsen, J., Johnsen, I.A., Sandvik, A.D., 2020. The hydrodynamic foundation for salmon lice dispersion modeling along the Norwegian coast. Ocean Dynamics 70, 1151–1167. https://doi.org/10.1007/s10236-020-01378-0
A setup of Norkyst-800 domain with reduced horizontal resolution is used for data assimilation. The Norkyst-DA setup uses a horizontal resolution of 2.4km and 42 vertical levels and is based on the Regional Ocean Modeling System (ROMS) with a physical space 4D-variational (4D-Var) DA scheme. The horizontal model resolution of 2.4km has been chosen to suit the scale of the available observations, and to compromise the need to resolve high resolution eddy dynamics while confining nonlinearities that limit the 4D-Var DA capabilities. Norkyst-DA assimilates satellite sea surface temperature, in-situ observations from ARGOS drifters, CTD sections and ferry boxes and HF-radar surface currents. Assimilation of sea level anomaly and SAR currents is currently in a development and testing stage. Norkyst-DA observation and performance tracking are presented HERE. The data are archived on THREDDS
Röhrs, J., Sperrevik, A.K., Christensen, K.H., 2018. NorShelf: An ocean reanalysis and data-assimilative forecast model for the Norwegian Shelf Sea (No. ISSN 2387-4201 04/2018), MET Report. Norwegian Meteorological Institute, Oslo, Norway. https://doi.org/10.5281/zenodo.2384124
Sperrevik, A.K., Christensen, K.H., Röhrs, J., 2015. Constraining energetic slope currents through assimilation of high-frequency radar observations. Ocean Sci 11, 237–249. https://doi.org/10.5194/os-11-237-2015
Sperrevik, A.K., Röhrs, J., Christensen, K.H., 2017. Impact of data assimilation on Eulerian versus Lagrangian estimates of upper ocean transport. J. Geophys. Res. Oceans 122, 5445–5457. https://doi.org/10.1002/2016JC012640
Röhrs, J., Sutherland, G., Jeans, G., Bedington, M., Sperrevik, A.K., Dagestad, K.-F., Gusdal, Y., Mauritzen, C., Dale, A., LaCasce, J.H., 2021. Surface currents in operational oceanography: Key applications, mechanisms, and methods. Journal of Operational Oceanography 0, 1–29. https://doi.org/10.1080/1755876X.2021.1903221