• Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application 

      Fritzner, Sindre Markus; Graversen, Rune; Christensen, Kai Håkon (Journal article; Tidsskriftartikkel, 2020-10-17)
      In this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state‐of‐the‐art ...
    • Assimilation of high-resolution ice charts in a coupled ocean-sea-ice model 

      Fritzner, Sindre Markus; Christensen, Kai Håkon; Graversen, Rune; Wang, Keguang (Journal article; Tidsskriftartikkel, 2019)
      In this study, we show assimilation results from a coupled ocean sea-ice model. The model has a horizontal resolution of 2.5 km. In the assimilation system, we assimilate high-resolution ice charts, structured on a 1 km grid. We compare the assimilation of passive microwave observations with the assimilation of ice charts. It is shown that the ice charts have a larger impact on the assimilation ...
    • Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project 

      Camus, Lionel; Andrade, Hector; Aniceto, Ana Sofia; Aune, Magnus; Bandara, Kanchana; Basedow, Sünnje Linnéa; Christensen, Kai Håkon; Cook, Jeremy; Daase, Malin; Dunlop, Katherine Mary; Falk-Petersen, Stig; fietzek, Peter; Fonnes, Gro; Ghaffari, Peygham; Gramvik, Geir; Graves, Inger; Hayes, Daniel; Langeland, Tom; Lura, Harald; Marin, Trond Kristiansen; Nøst, Ole Anders; Peddie, David; Pederick, Joel; Pedersen, Geir; Sperrevik, Ann Kristin; Sørensen, Kai; Tassara, Luca; Tjøstheim, Sigurd; Tverberg, Vigdis; Dahle, Salve (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-12)
      Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved ...
    • Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard 

      Röhrs, Johannes; Gusdal, Yvonne; Rikardsen, Edel S. U.; Durán Moro, Marina; Brændshøi, Jostein; Kristensen, Nils Melsom; Fritzner, Sindre Markus; Wang, Keguang; Sperrevik, Ann Kristin; Idzanovic, Martina; Lavergne, Thomas; Debernard, Jens Boldingh; Christensen, Kai Håkon (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-22)
      An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue ...
    • Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation 

      Fritzner, Sindre Markus; Graversen, Rune; Wang, Keguang; Christensen, Kai Håkon (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-04-25)
      Increasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures ...
    • A dataset of direct observations of sea ice drift and waves in ice 

      Rabault, Jean; Müller, Malte; Voermans, Joey; Brazhnikov, Dmitry; Turnbull, Ian; Marchenko, Aleksey; Biuw, Martin; Nose, Takehiko; Waseda, Takuji; Johansson, Malin; Breivik, Øyvind; Sutherland, Graig; Hole, Lars Robert; Johnson, Mark; Jensen, Atle; Gundersen, Olav; Kristoffersen, Yngve; Babanin, Alexander; Tedesco, Paulina Souza; Christensen, Kai Håkon; Kristiansen, Martin; Hope, Gaute; Kodaira, Tsubasa; Martins de Aguiar, Victor Cesar; Taelman, Catherine Cecilia A; Quigley, Cornelius Patrick; Filchuk, Kirill; Mahoney, Andrew R. (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-03)
      Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 ...
    • Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system 

      Fritzner, Sindre Markus; Graversen, Rune; Christensen, Kai Håkon; Rostosky, Philip; Wang, Keguang (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-08)
      The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During ...