• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.76-82
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• 1998

Increase of greenhouse gas due to $CO_2$ and CH$_4$ gases would cause the global warming in the atmosphere. According to the global circulation model, it is pointed out in the Okhotsk Sea that the large increase of atmospheric temperature might be occurredin this region by global warming due to the doubling of greenhouse effectgases. Therefore, it is very important to monitor the sea ice extents in the Okhotsk Sea. To improve the sea ice extents and concentration with more highly accuracy, the field experiments have begun to comparewith Airborne Microwave Radiometer (AMR) and video images installed on the aircraft (Beach-200). The sea ice concentration is generally proportional to the brightness temperature and accurate retrieval of sea ice concentration from the brightness temperature is important because of the sensitivity of multi-channel data with the amount of open water in the sea ice pack. During the field experiments of airborned AMR the multi-frequency data suggest that the sea ice concentration is slightly dependending on the sea ice types since the brightness temperature is different between the thin and small piece of sea ice floes, and a large ice flow with different surface signatures. On the basis of classification of two sea ice types, it is cleary distinguished between the thin ice and the large ice floe in the scatter plot of 36.5 and 89.0GHz, but it does not become to make clear of the scatter plot of 18.7 and 36.5GHz Two algorithms that have been used for deriving sea ice concentrations from airbomed multi-channel data are compared. One is the NASA Team Algorithm and the other is the Bootstrap Algorithm. Intrercomparison on both algorithms with the airborned data and sea ice concentration derived from video images bas shown that the Bootstrap Algorithm is more consistent with the binary maps of video images.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.2
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• pp.210-226
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• 2013

Ever since the Arctic region has opened its mysterious passage to mankind, continuous attempts to take advantage of its fastest route across the region has been made. The Arctic region is still covered by thick ice and thus finding a feasible navigating route is essential for an economical voyage. To find the optimal route, it is necessary to establish an efficient transit model that enables us to simulate every possible route in advance. In this work, an enhanced algorithm to determine the optimal route in the Arctic region is introduced. A transit model based on the simulated sea ice and environmental data numerically modeled in the Arctic is developed. By integrating the simulated data into a transit model, further applications such as route simulation, cost estimation or hindcast can be easily performed. An interactive simulation system that determines the optimal Arctic route using the transit model is developed. The simulation of optimal routes is carried out and the validity of the results is discussed.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.519-530
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• 2008

This paper studies the multi-domain coupled system of one dimensional Arctic temperature field and establishes identification model about the thermodynamic parameters of sea ice (heat storage capacity, density and conductivity) by the so-called output least-square estimate according to the temperature data acquired by a monitor buoy installed in the Arctic ocean. By the optimal control theory, the existence and dependability of weak solution and the identifiability of identification model have been given. Moreover, necessary optimality condition is proposed. Furthermore, the optimal algorithm for the identification model is constructed. By using the optimal thermodynamic parameters of Arctic sea ice, the numerical simulation is implemented, and the numerical results of temperature distribution of Arctic sea ice are demonstrated.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.478-483
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• 2012

The speed performances of ice sea trial on the Arctic(2010 & 2011) area were shown different results depend on the ice floe size. Penetration phenomena of level ice was not happened on medium ice floe and tore up by the impact force because the mass of medium ice floe is similar to the mass of Araon which is Korean ice breaking research vessel and did not shut up by the ice ridge or iceberg. The sea trial on the Amundsen sea was performed at the big floe which is classified by WMO(World Meteorological Organization). Three measurements of ice properties and five results of speed trial were obtained with different ice thicknesses and engine powers. To evaluate speed of level ice trial and model test results at the same ice thickness and engine power, the correction method of HSVA(Hamburg Ship Model Basin) was used. The thickness, snow effect, flexural strength and friction coefficient were corrected to compare the speed of sea trial. The analyzed speed at 1.03m thickness of big floe was 5.85 knots at 10MW power and it's 6.10 knots at 1.0m ice thickness and the same power. It's bigger than the results of level ice because big floe was also slightly tore up by the impact force of vessel based on the observation of recorded video.

• Journal of Ocean Engineering and Technology
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• v.22 no.3
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• pp.64-70
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• 2008

A full-scale field experiment is an important part in the design of ships and offshore structures. Full-scale tests in the ice-covered sea, however, are usually very expensive and difficult tasks. Model tests in a refrigerated ice tank may substitute this difficulty of full-scale field tests. One of the major tasks to perform proper model tests in an ice towing tank is to select a realistic material for model ice which shows correct similitude with natural sea ice. This study focuses on the testing material properties and the selection of model ice material which will be used in an ice model basin. The first Korean ice model basin will be constructed at the Maritime & Ocean Engineering Research Institute (MOERI) in 2009. With an application to the MOERI ice model basin, in this study the material properties of EG/AD/S model ice of IOT (Institute for Ocean Technology) Canada, were tested. Through comprehensive bending tests, the elastic modulus and the flexural strength of EG/AD/S model ice were evaluated and the results were compared with published test results from Canada. Instead of using an ice model basin, a cold room facility was used for making a model ice specimen. Since the cold room adopts a different freezing procedure to make model ice, the strength of the model ice specimen differs from the published test results. The reason for this difference is discussed and the future development for a making model ice is recommended.

• Journal of the korean society of oceanography
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• v.34 no.2
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• pp.59-72
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• 1999

Cavitating fluid sea ice model of Plato and Hibler (1992) is applied to the Southern Ocean with an idealized, circular Antarctica. Using climatological atmospheric forcing fields averaged in the zonal direction, we show that oceanic heat flux and ice velocity have major effects on the seasonal change of ice edge, as other studies showed. In our model results, there appears a zone of free drift that contains a polynya zone. Thermodynamic forcing functions make dominant contributions to daily increments of ice thickness and compactness, except the zones of ice edge and polynya. The dominant contributions are also shown in distributions of the temperature on ice surface and several to terms in surface heat balance equation, and are also confirmed by those obtained from the thermodynamic-only model with the different locations of ice edge.

• Ocean and Polar Research
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• v.32 no.4
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• pp.379-386
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• 2010

This study examines changes in the Arctic sea ice associated with global warming by analyzing the climate coupled general circulation models (CGCMs) provided in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. We selected nine models for better performance under 20th century climate conditions based on two different criteria, and then estimated the changes in sea ice extent under global warming conditions. Under projected 21st century climate conditions, all models, with the exception of the GISS-AOM model, project a reduction in sea ice extent in all seasons. The mean reduction in summer (-63%) is almost four times larger than that in winter (-16%), resulting an enhancement of seasonal variations in sea ice extent. The difference between the models, however, becomes larger under the 21st century climate conditions than under 20th century conditions, thus limiting the reliability of sea-ice projections derived from the current CGCMs.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.3 s.153
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• pp.340-347
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• 2007

Main purpose of the study is to develop a transit model for icebreaking cargo vessels in the Northern Sea Route and to select optimum sea routes with the shortest navigation time and the lowest operation cost. This numerical model executed with basic information such as ship capabilities, transit directions and months of transit, can calculate total transit distance and elapsed time, mean speed, operation cost for each vessel. In the transit model. environment information such as the site-specific ice conditions, wave and wind states are utilized for four different months (April, June, August, and October) along the Northern Sea Route. The model also defines a necessary period of an icebreaker escort. Then the optimum sea routes are selected and visually displayed on the digital map using a commercial software ArcGIS. Usefulness of the selected sea routes is discussed.