Enhanced iceberg drift modelling in the Barents Sea with estimates of the release rates and size characteristics at the major glacial sources using Sentinel-1 and Sentinel-2

2020 ◽  
Vol 175 ◽  
pp. 103084 ◽  
Author(s):  
Dennis Monteban ◽  
Raed Lubbad ◽  
Ilija Samardzija ◽  
Sveinung Løset
Keyword(s):  
Barents Sea ◽  
Release Rates ◽  
The Barents Sea ◽  
Iceberg Drift ◽  
Sentinel 2

Parameterization of an Iceberg Drift Model in the Barents Sea

2009 ◽  
Vol 26 (10) ◽  
pp. 2216-2227 ◽  
Author(s):  
Intissar Keghouche ◽  
Laurent Bertino ◽  
Knut Arild Lisæter
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Form Drag ◽  
Drag Coefficients ◽  
Drag Forces ◽  
Drift Model ◽  
The Barents Sea ◽  
Iceberg Drift

Abstract The problem of parameter estimation is examined for an iceberg drift model of the Barents Sea. The model is forced by atmospheric reanalysis data from ECMWF and ocean and sea ice variables from the Hybrid Coordinate Ocean Model (HYCOM). The model is compared with four observed iceberg trajectories from April to July 1990. The first part of the study focuses on the forces that have the strongest impact on the iceberg trajectories, namely, the oceanic, atmospheric, and Coriolis forces. The oceanic and atmospheric form drag coefficients are optimized for three different iceberg geometries. As the iceberg mass increases, the optimal form drag coefficients increase linearly. A simple balance between the drag forces and the Coriolis force explains this behavior. The ratio between the oceanic and atmospheric form drag coefficients is similar in all experiments, although there are large uncertainties on the iceberg geometries. Two iceberg trajectory simulations have precisions better than 20 km during two months of drift. The trajectory error for the two other simulations is less than 25 km during the first month of drift but increases rapidly to over 70 km afterward. The second part of the study focuses on the sea ice parameterization. The sea ice conditions east of Svalbard in winter 1990 were too mild to exhibit any sensitivity to the sea ice parameters.

scholarly journals Modeling of iceberg drift in the marginal ice zone of the Barents Sea

2019 ◽  
Vol 88 ◽  
pp. 210-222 ◽  
Author(s):  
A. Marchenko ◽  
N. Diansky ◽  
V. Fomin
Keyword(s):  
Barents Sea ◽  
The Barents Sea ◽  
Marginal Ice Zone ◽  
Iceberg Drift

Modeling Iceberg Drift in the Barents Sea from Field Data

2018 ◽  
Vol 43 (5) ◽  
pp. 313-322 ◽  
Author(s):  
N. A. Diansky ◽  
A. V. Marchenko ◽  
I. I. Panasenkova ◽  
V. V. Fomin
Keyword(s):  
Field Data ◽  
Barents Sea ◽  
The Barents Sea ◽  
Iceberg Drift

Method application of optimal multi-parameter analysis to assess the distribution of water masses as an example of CTD data of the Barents Sea in summer 2017

2019 ◽  
pp. 38-51
Author(s):  
Valeriy G. Yakubenko ◽  
Anna L. Chultsova
Keyword(s):  
Barents Sea ◽  
Field Measurements ◽  
Structural Similarity ◽  
Water Masses ◽  
Parameter Analysis ◽  
Water Dynamics ◽  
Phosphorus Concentration ◽  
Nitrogen And Phosphorus ◽  
The Barents Sea ◽  
Expert Assessments

Identification of water masses in areas with complex water dynamics is a complex task, which is usually solved by the method of expert assessments. In this paper, it is proposed to use a formal procedure based on the application of the method of optimal multiparametric analysis (OMP analysis). The data of field measurements obtained in the 68th cruise of the R/V “Academician Mstislav Keldysh” in the summer of 2017 in the Barents Sea on the distribution of temperature, salinity, oxygen, silicates, nitrogen, and phosphorus concentration are used as a data for research. A comparison of the results with data on the distribution of water masses in literature based on expert assessments (Oziel et al., 2017), allows us to conclude about their close structural similarity. Some differences are related to spatial and temporal shifts of measurements. This indicates the feasibility of using the OMP analysis technique in oceanological studies to obtain quantitative data on the spatial distribution of different water masses.

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