• Atmosphere
• /
• v.28 no.2
• /
• pp.153-162
• /
• 2018

The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering $El-Ni{\tilde{n}}o$ Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of interannual variability in the troposphere and stratosphere, enhance the seasonal prediction over the Northern Hemispheric surface air temperature, even though their impacts are dependent on the predicted month and region. In particular, the prediction of Korean surface air temperature in midwinter is substantially improved. In December, ENSO improved about 10% of prediction skill compared without it. In January, ENSO and QBO jointly helped to enhance prediction skill up to 36%. These results suggest that wintertime surface air temperature in Korea can be better predicted by considering not only high-latitude surface conditions (i.e., Eurasian snow cover extent and Arctic sea ice concentration) but also equatorial sea surface temperature and stratospheric circulation.

• Korean Journal of Metals and Materials
• /
• v.50 no.6
• /
• pp.427-432
• /
• 2012

This paper presents the material properties of carbon steels for ships, and offshore structures (ASTM A131) are tested under a series of arctic and cryogenic temperature conditions. For material tension tests, among the ASTM 131 steels, Grades A and B of mild steel and Grade AH of high tensile steel have been used. The obtained mechanical properties of the materials from the material tension tests were applied in a 13,000TEU class container ship to define the effect of low temperature on the ultimate longitudinal strength of the target structure by using the ALPS/HULL intelligent supersize finite element method. The tensile coupon test results showed increased strength and nonuniform fracture strain behaviors within different grades and temperatures. Increasing the material strength resulted in increasing the ultimate longitudinal strength of the ship.

• Journal of Digital Convergence
• /
• v.16 no.2
• /
• pp.103-115
• /
• 2018

The potential competitiveness for new routes and resources has been theoretically discussed with regards to the Arctic route but is gradually becoming a reality as global warming increases. In June of 2017, China officially included the Northern Sea Route (NSR) as part of the Belt and Road Initiative (BRI), and major countries' interests in the NSR are greatly expanding. This paper presents the general characteristics of the NSR, NSR development in China, the expected relationship between the NSR and the BRI, and this relationship's implications for Korea. The NSR has poor facilities and information infrastructure and is not economically viable for commercial navigation due to its high-cost conditions compared to competitive routes. In order to explore the Arctic and develop the NSR, large-scale projects must be funded over a long period of time; this has caused major difficulties in development. However, as the NSR is included in the BRI, there could be an opportunity to utilize BRI funds, such as Asian Infrastructure Investment Bank (AIIB). Further, China's NSR development and the NSR development of partner countries, such as Korea, should be further stimulated. As Korea has strengths in terms of its shipbuilding technology and geographical location, which is located at the core of the NSR, Korea would have chances to expand the economic cooperation and business opportunities with China and Russia.

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

• Proceedings of the Korean Society of Plant Biotechnology Conference
• /
• 2005.04a
• /
• pp.153-153
• /
• 2005

• Microbiology and Biotechnology Letters
• /
• v.44 no.2
• /
• pp.171-179
• /
• 2016

Recent succession of soil microorganisms and vegetation has occurred in the glacier foreland, because of glacier thawing. In this study, whole microbial communities, including bacteria, archaea, and eukaryotes, from the glacier foreland of Midtre Lovénbreen in Svalbard were analyzed by metagenome sequencing, using the Ion Torrent Personal Genome Machine (PGM) platform. Soil samples were collected from two research sites (ML4 and ML7), with different exposure times, from the ice. A total of 2,798,108 and 1,691,859 reads were utilized for microbial community analysis based on the metagenomic sequences of ML4 and ML7, respectively. The relative abundance of microbial communities at the domain level showed a high proportion of bacteria (about 86−87%), whereas archaeal and eukaryotic communities were poorly represented by less than 1%. The remaining 12% of the sequences were found to be unclassified. Predominant bacterial groups included Proteobacteria (40.3% from ML4 and 43.3% from ML7) and Actinobacteria (22.9% and 24.9%). Major groups of Archaea included Euryarchaeota (84.4% and 81.1%), followed by Crenarchaeota (10.6% and 13.1%). In the case of eukaryotes, both ML4 and ML7 samples showed Ascomycota (33.8% and 45.0%) as the major group. These findings suggest that metagenome analysis using the Ion Torrent PGM platform could be suitably applied to analyze whole microbial community structures, providing a basis for assessing the relative importance of predominant groups of bacterial, archaeal, and eukaryotic microbial communities in the Arctic glacier foreland of Midtre Lovénbreen, with high resolution.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1177-1186
• /
• 2021

As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed.

• Journal of the Korean Geographical Society
• /
• v.49 no.5
• /
• pp.675-689
• /
• 2014

This study applied Ensemble Empirical Mode Decomposition(EEMD), a new methodology to define the timing of spring onset over the Republic of Korea and to examine its spatio-temporal change. Also this study identified the relationship between spring onet timing and some atmospheric variations, and figured out synoptic factors which affect the timing of spring onset. The averaged spring onset timing for the period of 1974-2011 was 11th, March in Republic of Korea. In general, the spring onset timing was later with higher latitude and altitude regions, and it was later in inland regions than in costal ones. The correlation analysis has been carried out to find out the factors which affect spring onset timing, and global annual mean temperature, Arctic Oscillation(AO), Siberian High had a significant correlation with spring onset timing. The multiple regression analysis was conducted with three indices which were related to spring onset timing, and the model explained 64.7%. As a result of multiple regression analysis, the effect of annual mean temperature was the greatest and that of AO was the second. To find out synoptic factors affecting spring onset timing, the synoptic analysis has been carried out. As a result the intensity of meridional circulation represented as the major factor affect spring onset timing.

• Ocean and Polar Research
• /
• v.27 no.3
• /
• pp.299-309
• /
• 2005

The community structure of meiobenthos was studied in the sediment of Kongfjorden, Spitsbergen of Svalbard Island in the Arctic Sea. Samples of meiobenthos were collected in August, 2003. Meiobenthic organisms were collected by SCUBA and van veen grab or acryl sub-corers 34mm in internal diameter, and were taken from upper sediment to a depth of 3cm at each station. A total of 26 meiofaunal groups were found in the sediment of Spitsbergen in Svalbard Island. Nematodes were the most dominant faunal group. Sarcomastigophorans, benthic harpacticoids, and nauplius larvae of crustaceans, were also important components of the meiobenthic community of Kongsfjorden. All of these low faunal groups were comprised of more than 90% of total meiobenthos at every station. The total density of meiobenthos at each station was highest at station MeG 6 $(3,583{\pm}1,137inds./10cm^2)$, and lowest at station $MeG9(28{\pm}1inds./10cm^2)$. Meiobenthos in general showed the highest density in the upper 1cm layer. This may be associated with food and oxygen supply to subsurface. Harpacticoids showed extreme preference at the surface and little presence in layers deeper than 2cm. These animals may be less resistant to oxygen deficiency, and nauplius also showed the same trend. However, in St. MeG 8 and 9, meiobenthos were dense at depths of more than 0-1cm, at especially at depths of 2-3m because of relatively easy penetration of oxygen. Based on the results of cluster analysis, three meiobenthos assemblages were distinguished: one was in the outer and two were in the inner fjord. Station SCU 5 was grouped with the meiobenthos assemblage located in the outer fjord. The outer ford community was characterised by : 1) a relatively low mean number of meiobenthos taxa, 2) a relatively high density of harpacticods and nauplius. One of the inner ford communities (a group of four nation: MeG 2, 3, 8, 9) was in the proximity of the glaciers. Specifically, it was characterised by : 1) a low mean number of meiobenthos taxa, 2) a low density. The other inner ford community was characterised by both a high density and great mean number of meiofaunal taxa.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.999-1009
• /
• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.