Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard

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. T...

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Bibliographic Details
Published in:Geoscientific Model Development Vol. 16; no. 18; pp. 5401 - 5426
Main Authors: Röhrs, Johannes, Gusdal, Yvonne, Rikardsen, Edel S. U, Durán Moro, Marina, Brændshøi, Jostein, Kristensen, Nils Melsom, Fritzner, Sindre, Wang, Keguang, Sperrevik, Ann Kristin, Idžanovic, Martina, Lavergne, Thomas, Debernard, Jens Boldingh, Christensen, Kai H
Format: Journal Article
Language:English
Published: Katlenburg-Lindau Copernicus GmbH 22-09-2023
Copernicus Publications
Subjects:
Analysis
Bathymetry
Daily forecasts
Data assimilation
Data collection
Emergency preparedness
Energy
Forecasting models
Hydrography
Ice forecasting
Icebergs
Kalman filters
Lead time
Mathematical models
Modelling
Ocean circulation
Ocean currents
Oceans
Polar environments
Prediction models
Sea currents
Sea ice
Sea ice concentrations
Sea ice forecasting
Sea surface
Sea surface temperature
Surface temperature
Topography
Viscosity
Weather forecasting
Barents Sea
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Description
Summary: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 applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66 h. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed.
Bibliography:Geoscientific Model Development
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-16-5401-2023