• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.153-156
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• 2006

북극과 남극의 해빙을 촬영한 Kompsat-1 EOC 영상을 SSM/I Sea Ice Concentration(SIC)과 비교하였다. EOC 영상은 2005년 $7{\sim}8$월 북극 해빙지역의 가장자리를 지나는 10개 궤도(624 영상)와 $9{\sim}11$월 남극대륙의 가장자리를 지나는 11개 궤도(676 영상)에서 얻어졌다. 그 중 구름의 영향이 없는 약 12%의 영상으로부터 감독분류와 육안분류를 통해 Multi-year ice와 First-year ice(M+F), Young ice(Y), New ice(N)로 해빙의 유형을 구분하여 SIC를 계산하였으며, 이를 NASA Team Algorithm(NTA)으로 계산된 SSM/I SIC와 비교하였다. 북극의 여름철에는 해빙의 시공간적 변화가 매우 크기 때문에 EOC SIC(M+F+Y+N)와 SSM/I SIC의 상관계수는 0.671로 잘 일치하지 않았다. 남극의 봄철에 N을 제외한 EOC SIC(M+F+Y)의 경우 SSM/I SIC와 0.873의 높은 상관계수를 가졌다. 이로부터 NTA로 계산된 남극의 SSM/I SIC가 M과 F를 비롯하여 Y도 포함하는 것을 알 수 있었다.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.333-338
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• 2017

We compared sea ice concentration(SIC) and sea ice extent(SIE) using EUMETSAT Ocean and Sea Ice Satellite Application Facilities(OSI SAF) and NASA Team(NT) sea ice algorithm in the Arctic during 1980-2010 to investigate the difference between sea ice data applied different algorithms. SIC and SIE of the two data showed different consistency by season and by sea area. Seasonally, SIC of OSI SAF was 0.85 % overall, 0.48 % in spring, 0.97 % in summer, 1.38 % in autumn and 0.66 % in winter higher than NT SIC. By sea area, OSI SAF SIC was 2.7 %, SIE was $198,000km^2$ higher than NT in Arctic Ocean, but in Lincoln Sea, OSI SAF SIC was 2.3 %, SIE was $20,000km^2$ lower than NT.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.633-633
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• 2002

We investigated the distributions of sea ice using various microwave remote sensing techniques in the part of Drake passage, Antarctica, between the area 45-75$^{\circ}$W and 55-66$^{\circ}$S. We used Topex/Poseidon(T/P) radar altimeter, ERS-1 altimeter, ERS-2 scatterometer, Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), and DMSP Special Sensor Microwave/Imager(SSM/I) data. The sea ice distributions were estimated between May and Jun., 1995 and Oct. and Nov., 1998. The two altimeter measurements (T/P and ERS-1) showed good coherence with the results from the radiometer data in the given period when the ice concentration of 20% and greater was selected. The scatterometer data also showed good correlation with altimetry-implied sea ice surface. The maximum and minimum values of sea ice distribution were appeared in Aug. and Feb., respectively. In general, the sea ice distributions estimated from radar altimeter, radioneter, and scatterometer are well correlated.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.82-89
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• 2015

In this study, the characteristics of sea ice initial conditions generated from a global ocean and sea ice prediction system, the Nucleus for European Modeling of the Ocean (NEMO) - Los Alamos Sea Ice Model (CICE)/NEMOVAR were analyzed for the period June 2013 to May 2014 over the Arctic region. For the purpose, the observed and reanalyzed data were used to compare with the sea ice initial conditions. Results indicated that the variability of the monthly sea ice extent and thickness in model initial conditions were well represented as compared to the observation, while it was found that the extent and thickness of Arctic sea ice in initial data were narrower and thinner than those in reanalysis and observation for the period. The reason for the narrower sea ice extent in model initial conditions seems to be due to the fact that the initial sea ice concentration at the boundary area of sea ice was about 20 percent less than the reanalysis data. Also, the reason for the thinner sea-ice thickness in the Arctic region is due to the underestimation of Arctic sea ice thickness (about 60 cm) of the model initial conditions in the Arctic Ocean area adjacent to Greenland and Arctic archipelago where thick sea ice appears all the year round.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.423-434
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• 2009

The Arctic environment is sensitive to change of sea-ice distribution. The increase and decrease of sea ice work to an index of globe warming progress. In order to predict the progress of hereafter earth global warming, continuous monitoring regarding a change of the sea ice area in the Arctic should be performed. The remote sensing based on an artificial satellite is most effective on the North Pole. The sea ice observation using a passive microwave sensor has been continued from 1970's. The determination of sea ice extent and ice type is one of the great successes of the passive microwave imagers. In this paper, to investigate the seasonal and inter-annual variation of sea-ice distribution we used here the sea ice data from July 2002 to May 2009 around the Arctic within $60^{\circ}N$ for the AMSR-E 12.5km sea-ice concentration, a passive microwave sensor. From an early analysis of these data, the arctic sea-ice extent has been steadily decreasing at a rate of about 3.1%, accounting for about $2{\times}10^5\;km^2$, which was calculated for the sea-ice cover reaching its minimum extent at the end of each summer. It is also revealed that this trend corresponds to a decline in the multi-year ice that is affected mainly by summer sea surface and air temperature increases. The extent of younger and thinner (first-year) ice decreased to the 2007 minimum, but rapidly recovered in 2008 and 2009 due to the dramatic loss in 2007. Seasonal variations of the sea-ice extent show significant year-to-year variation in the seasons of January-March in the Barents and Labrador seas and August-October in the region from the East Siberian and Chukchi seas to the North Pole. The spatial distribution of multi-year ice (7-year old) indicates that the perennial ice fraction has rapidly shrunk recently out of the East Siberian, Laptev, and Kara seas to the high region of the Arctic within the last seven years and the Northeast Passage could become open year-round in near future.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.901-915
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• 2017

Recently, global climate change has caused a catastrophic event in the Arctic Ocean, directly and indirectly. The air-sea interaction has caused the significant sea-ice reduction in the Arctic Ocean, and has been accelerating the Arctic warming. Many scientists are worried about the Arctic environment change, suggesting that many of anomalous events will produce direct or indirect biophysical effects on the Arctic. The aim of this study is to understand the inter-annual variability of the Arctic Ocean in wide-view using multi-satellite-derived measurements. Sea surface temperature (SST) and sea ice concentration (SIC) data were obtained from Optimum Interpolation Sea Surface Temperature (OISST) and ECMWF ERA-Interim, respectively. Chlorophyll-a concentration (CHL) was obtained from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Aqua sensor from MODerate resolution Imaging Spectroradiometer (MODIS-Aqua) sensor which has continuously observed since 1998. From 1998 to 2016 summer in the Arctic Ocean which was defined as regions over $60^{\circ}N$ in this study, there were three consequences that CHL increase ($0.15mg\;m^{-3}\;decade^{-1}$), SST warming ($0.43^{\circ}C\;decade^{-1}$) and SIC decrease ($-5.37%\;decade^{-1}$). While SST and SIC highly correlated each other (r = -0.76), a relationship between CHL and SIC was very low ($r={\pm}0.1$) because of data limitations. And a relationship between CHL and SST shows meaningful results ($r={\pm}0.66$) with regional differences.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.107-113
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• 2014

The icebreaking research vessel, ARAON had her second ice trial in the Arctic Sea from 16th July to 12th August 2010. During the voyage, the local ice loads acting on the bow of port side were measured from 14 strain gauges. The measurements were also carried out in ice waters with various ice concentration ratio as well as the icebreaking performance tests. In this study, the ice loads measured during the 'general' operation in ice waters were analyzed. As a first step, the relationship between the location of strain gauges and the ice loads were investigated, and then the possibility for observation of higher ice loads was estimated based on the probability density function. The relationship between the ship speed and the ice load was also investigated. 718 peak stresses data higher than 20 MPa obtained from strain gauges array attached in longitudinally and vertically was analyzed. In general, the ice load increases as the ship speed increases in the low ship speed range, and ice load decreases as the ship speed is greater than a certain speed.

• Journal of Ocean Engineering and Technology
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• v.32 no.3
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• pp.184-191
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• 2018

Recently, as sea ice in the Arctic has been decreasing due to global warming, it has become easier to develop oil and gas resources buried in the Arctic region. As a result, Russia, the United States, and other Arctic coastal states are increasingly interested in the development of oil and gas resources, and the demand for offshore structures to support Arctic sea resources development is expected to significantly increase. Since offshore structures operating in Arctic regions need to secure safety against various drifting ice conditions, the concept of an ice-strengthened design is introduced here, with a priority on calculation of ice load. Although research on the estimation of ice load has been carried out all over the world, most ice-load studies have been limited to estimating the ice load of the icebreaker in a non-oblique state. Meanwhile, in the case of Arctic offshore structures, although it is also necessary to estimate the ice load according to oblique angles, the overall research on this topic is insufficient. In this paper, we suggest algorithms for calculating the ice load of managed ice (pack ice, 100% concentration) in an oblique state, and discuss validity. The effect of oblique angle according to estimated ice load with various oblique angles was also analyzed, along with the impact of ship speed and ice thickness on ice load.