Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

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Estimation of Degree of Sea Ice Ridging Based on Dual-Polarized C-band SAR Data

10.5194/tc-2017-127 ◽

2017 ◽

Cited By ~ 1

Author(s):

Alexandru Gegiuc ◽

Markku Similä ◽

Juha Karvonen ◽

Mikko Lensu ◽

Marko Mäkynen ◽

...

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Large Scale ◽

Winter Season ◽

Texture Features ◽

Route Optimization ◽

Sea Ice Concentration ◽

Essential Information ◽

Ice Concentration ◽

Dual Polarized

Abstract. For navigation in Baltic Sea ice during winter season, parameters such as ice edge, ice concentration, ice thickness, ice drift and degree of ridging are usually reported daily in the manually prepared Ice Charts, which provide icebreakers essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times and detailed analysis is not possible for large scale maps (e.g. Arctic Ocean). Here, we propose a method for automatic estimation of degree of ridging density in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and the sea ice concentration information extracted from the Finnish Ice Charts. The SAR images were first segmented and then several texture features were extracted for each  segment. Using the Random Forest classification, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized Ice Charts. The overall agreement between the ice chart based degree of ice ridging (DIR) and the automated results varied monthly, being 83 %, 63 % and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice riding of the manual Ice Charts and the actual ridge density was good when this issue was studied based on an extensive field campaign data in March 2011.

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Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v62i4.15706 ◽

2010 ◽

Author(s):

Nina MaaÃŸ ◽

Lars Kaleschke

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Coastal Areas ◽

Passive Microwave ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Baltic

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Sea Ice Concentration Analyses for the Baltic Sea and Their Impact on Numerical Weather Prediction

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2376.1 ◽

2006 ◽

Vol 45 (7) ◽

pp. 982-994 ◽

Cited By ~ 15

Author(s):

Matthias Drusch

Keyword(s):

Heat Flux ◽

Sea Ice ◽

Baltic Sea ◽

Latent Heat ◽

Latent Heat Flux ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

The Baltic

Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset exhibits less spatial and temporal variability during the winter of 2003/04. Because of the coarse resolution of the NCEP dataset, ice extent is generally larger than in the SMHI analysis. Mean sea ice concentrations derived from both datasets are in reasonable agreement during the ice-growing and ice-melting periods in January and April, respectively. For February and March, during which the sea ice extent is largest, mean sea ice concentrations are lower in the NCEP dataset relative to the SMHI product. Ten-day weather forecasts based on the NCEP sea ice concentrations and the SMHI dataset have been performed, and they were compared on the local, regional, and continental scales. Turbulent surface fluxes have been analyzed based on 24-h forecasts. The differences in sea ice extent during the ice-growing period in January cause mean differences of up to 30 W m−2 for sensible heat flux and 20 W m−2 for latent heat flux in parts of the Gulf of Bothnia and the Gulf of Finland. The comparison between spatially aggregated fluxes yields differences of up to 36 and 20 W m−2 for sensible and latent heat flux, respectively. The differences in turbulent fluxes result in different planetary boundary height and structure. Even the forecast cloud cover changes by up to 40% locally.

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Comparison of the main sea ice parameters received from satellite measurements and based on numerical simulation for the Baltic Sea region

10.5194/egusphere-egu2020-19573 ◽

2020 ◽

Author(s):

Jaromir Jakacki ◽

Maciej Muzyka ◽

Marta Konik ◽

Anna Przyborska ◽

Jan Andrzejewski

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Baltic Sea ◽

Melting Time ◽

Ice Thickness ◽

Satellite Measurements ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Baltic

During the last decades remote sensing observations as well as modelling tools has been developed and become key elements of oceanographic research. One of the main advantages of both tools is a possibility of measuring large-scale areas.The remote sensing measurements deliver only snapshots of the ice situation with no information about backgroundconditions. Moreover, providing picture of the whole area requires sometimes combining various datasets that increases uncertainties. &#160;Modelling simulations provide full history of external conditions, but they also introduce errors that are the result of parameterizations. Also, an inaccuracy provided by forcing fields at the top and bottom boundaries are accumulated in the model.In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both &#8211; satellite measurements and modelling has been compared. Numerical simulations were performed using standalone Community Ice Code (CICE) model (v. 6.0). It is a descendant of the basin scale dynamic-thermodynamic and thickness distribution sea ice model. The model is well known by scientific community and was widely used in a global as well as regional research, even operationally. The satellite derived ice thickness products were based on the C band HH-polarized SAR measurements originating from the satellites Sentinel-1 and RADARSAT-2. The sea ice concentration maps contain also visual and infrared information from MODIS and NOAA.The ice extent, thickness and volume were compared in several regions within the Baltic Sea.&#160; Seasonal changes were analyzed with a particular attention to ice formation and melting time. The sea ice extent datasets were compatible. Inconsistencies were observed for the sea ice thickness delivered by satellite measurements, especially during the ice melt. The work presents direction for ignoring satellite data with an error related to ice melting that allows for excluding erroneous satellite maps and obtain reliable intercalibration.&#160;This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159

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Numerical Simulations of Sea Ice Conditions in the Baltic Sea for 2010–2016 Winters Using the 3D CEMBS Model

Polish Maritime Research ◽

10.2478/pomr-2018-0094 ◽

2018 ◽

Vol 25 (3) ◽

pp. 35-43 ◽

Cited By ~ 1

Author(s):

Maciej Janecki ◽

Artur Nowicki ◽

Alicja Kańska ◽

Maria Golenko ◽

Lidia Dzierzbicka-Głowacka

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Ecosystem Model ◽

Ice Thickness ◽

The Baltic Sea ◽

Current State ◽

Ice Concentration ◽

Ice Conditions ◽

The Baltic ◽

Modelling Approach

Abstract Sea ice conditions in the Baltic Sea during six latest winters – 2010/2011 to 2015/2016 are analysed using coupled ice–ocean numerical model 3D CEMBS (3D Coupled Ecosystem Model of the Baltic Sea). Simulation results are compared with observations from monitoring stations, ice charts and satellite data. High correlation between model results and observations has been confirmed both in terms of spatial and temporal approach. The analysed period has a high interannual variability of ice extent, the number of ice days and ice thickness. Increasing number of relatively mild winters in the Northern Europe directly associated with climate change results in reduced ice concentration in the Baltic Sea. In this perspective, the implementation and development of the sea ice modelling approach (in addition to standard monitoring techniques) is critical to assess current state of the Baltic Sea environment and predict possible climate related changes in the ecosystem and their influence for human marine–related activities, such as fishery or transportation.

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Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

Ocean Science ◽

10.5194/os-7-257-2011 ◽

2011 ◽

Vol 7 (2) ◽

pp. 257-276 ◽

Cited By ~ 9

Author(s):

A. Herman ◽

J. Jedrasik ◽

M. Kowalewski

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Air Temperature ◽

Ice Cover ◽

Surface Fluxes ◽

Rate Of Change ◽

The Baltic Sea ◽

Ice Dynamics ◽

Ice Concentration ◽

The Baltic

Abstract. In this paper, a numerical dynamic-thermo-dynamic sea-ice model for the Baltic Sea is used to analyze the variability of ice conditions in three winter seasons. The modelling results are validated with station (water temperature) and satellite data (ice concentration) as well as by qualitative comparisons with the Swedish Meteorological and Hydrological Institute ice charts. Analysis of the results addresses two major questions. One concerns effects of meteorological forcing on the spatio-temporal distribution of ice concentration in the Baltic. Patterns of correlations between air temperature, wind speed, and ice-covered area are demonstrated to be different in larger, more open sub-basins (e.g., the Bothnian Sea) than in the smaller ones (e.g., the Bothnian Bay). Whereas the correlations with the air temperature are positive in both cases, the influence of wind is pronounced only in large basins, leading to increase/decrease of areas with small/large ice concentrations, respectively. The other question concerns the role of ice dynamics in the evolution of the ice cover. By means of simulations with the dynamic model turned on and off, the ice dynamics is shown to play a crucial role in interactions between the ice and the upper layers of the water column, especially during periods with highly varying wind speeds and directions. In particular, due to the fragmentation of the ice cover and the modified surface fluxes, the ice dynamics influences the rate of change of the total ice volume, in some cases by as much as 1 km3 per day. As opposed to most other numerical studies on the sea-ice in the Baltic Sea, this work concentrates on the short-term variability of the ice cover and its response to the synoptic-scale forcing.

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Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea

Tellus A Dynamic Meteorology and Oceanography ◽

10.1111/j.1600-0870.2010.00452.x ◽

2010 ◽

Vol 62 (4) ◽

pp. 393-410 ◽

Cited By ~ 14

Author(s):

NINA MAAß ◽

LARS KALESCHKE

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Coastal Areas ◽

Passive Microwave ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Baltic

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SAR Algorithm for Sea Ice Concentration—Evaluation for the Baltic Sea

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2012.2186280 ◽

2012 ◽

Vol 9 (5) ◽

pp. 938-942 ◽

Cited By ~ 14

Author(s):

A. Berg ◽

L. E. B. Eriksson

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

The Baltic Sea ◽

Sea Ice Concentration ◽

Ice Concentration ◽

The Baltic

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Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

Ocean Science Discussions ◽

10.5194/osd-8-113-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 113-157

Author(s):

A. Herman ◽

J. Jedrasik ◽

M. Kowalewski

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Air Temperature ◽

Ice Cover ◽

Surface Fluxes ◽

Rate Of Change ◽

The Baltic Sea ◽

Ice Dynamics ◽

Ice Concentration ◽

The Baltic

Abstract. In this paper, a numerical dynamic-thermodynamic sea-ice model for the Baltic Sea is used to analyze the variability of ice conditions in three winter seasons. The modelling results are validated with station (water temperature) and satellite data (ice concentration) as well as by qualitative comparisons with the Swedish Meteorological and Hydrological Institute ice charts. Analysis of the results addresses two major questions. One concerns effects of meteorological forcing on the spatio-temporal distribution of ice concentration in the Baltic. Patterns of correlations between air temperature, wind speed, and ice-covered area are demonstrated to be different in larger, more open sub-basins (e.g., the Bothnian Sea) than in the smaller ones (e.g., the Bothnian Bay). Whereas the correlations with the air temperature are positive in both cases, the influence of wind is pronounced only in large basins, leading to increase/decrease of areas with small/large ice concentrations, respectively. The other question concerns the role of ice dynamics in the evolution of the ice cover. By means of simulations with the dynamic model turned on and off, the ice dynamics is shown to play a crucial role in interactions between the ice and the upper layers of the water column, especially during periods with highly varying wind speeds and directions. In particular, due to the fragmentation of the ice cover and the modified surface fluxes, the ice dynamics influences the rate of change of the total ice volume, in some cases by as much as 1 km3 per day. As opposed to most other numerical studies on the sea-ice in the Baltic Sea, this work concentrates on the short-term variability of the ice cover and its response to the synoptic-scale forcing.

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