10 research outputs found
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled oceanâsea ice modelling system
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 the last decade, an
increasing number of sea-ice related variables have become available, which
include sea-ice thickness and snow depth, which are both important parameters
in the numerical sea-ice models. In the present study, a coupled
oceanâsea-ice model is used to assess the assimilation impact of sea-ice
thickness and snow depth on the model. The model system with the assimilation
of these parameters is verified by comparison with a system assimilating only
ice concentration and a system having no assimilation. The observations
assimilated are sea ice concentration from the Ocean and Sea Ice Satellite
Application Facility, thin sea ice from the European Space Agency's
(ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from
ESA's CryoSat-2 satellite, and a new snow-depth product derived from the
National Space Agency's Advanced Microwave Scanning Radiometer
(AMSR-E/AMSR-2) satellites. The model results are verified by comparing
assimilated observations and independent observations of ice concentration
from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge
campaign. It is found that the assimilation of ice thickness strongly
improves ice concentration, ice thickness and snow depth, while the snow
observations have a smaller but still positive short-term effect on snow
depth and sea-ice concentration. In our study, the seasonal forecast showed
that assimilating snow depth led to a less accurate long-term estimation of
sea-ice extent compared to the other assimilation systems. The other three
gave similar results. The improvements due to assimilation were found to last
for at least 3â4Â months, but possibly even longer.</p
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 at MET Norway for short-term forecasting at the coast off Northern Norway, the Barents Sea, and waters around Svalbard. Primary forecast parameters are the sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model is also a substantial input for drift modeling of pollutants, ice berg, and in search-and-rescue pertinent 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 lead time up to 66 hours. While the ocean circulation is not directly constrained by assimilation of ocean currents, the model ensemble represents the given uncertainty in the short-term current field by retaining the current state for each member throughout forecast cycles. Here we present the model setup and a validation in terms of SIC, SST and in-situ hydrography. The performance of the ensemble to represent the models uncertainty, and the performance of the EnKF to constrain the model state are discussed, in addition to the model’s forecast capabilities for SIC and SST.</p