https://repository.kopri.re.kr/handle/201206/5348 2026-04-15T00:15:29Z 2026-04-15T00:15:29Z Seo, Minji Lee, EunKyung Lee, Kyeong-sang Choi, Sungwon Jin, Dong-hyun Seong, Noh-hun Han, Hyeon-gyeong Kim, Hyun-cheol Han, Kyung-soo https://repository.kopri.re.kr/handle/201206/10820 2022-03-24T07:14:03Z 2018-12-01T00:00:00Z

Title: Comparative Analysis of Radiative Flux Based on Satellite over Arctic Authors: Seo, Minji; Lee, EunKyung; Lee, Kyeong-sang; Choi, Sungwon; Jin, Dong-hyun; Seong, Noh-hun; Han, Hyeon-gyeong; Kim, Hyun-cheol; Han, Kyung-soo Abstract: It is important to quantitatively analyze the energy budget for understanding of long-term climate change in Arctic. High-quality and long-term radiative parameters are needed to understand the energy budget. Since most of radiative flux components based on satellite are provide for a short period, several data must be used together. It is important to acquaint differences between data to link for conjunction with several data. In this study, we investigated the comparative analysis of Arctic radiative flux product such as CERES and GEWEX to provide basic information for data linkage and analysis of changes in Arctic climate. As a result, GEWEX was underestimated the radiative variables, and it difference between the two data was about 3 ~ 25 W/m2. In addition, the difference in high-latitude and sea ice regions have increased. In case of comparing with monthly means, the other variables except for longwave downward flux represent high difference of 9.26 ~ 26.71 W/m2 in spring-summer season. The results of this study can be used standard data for blending and selecting GEWEX and CERES radiative flux data due to recognition of characteristics according to ice-ocean area, season, and regions.

2018-12-01T00:00:00Z Han, Daehyeon Kim, Young Jun Im, Jungho Lee, Sanggyun Lee, Yeonsu Kim, Hyun-cheol https://repository.kopri.re.kr/handle/201206/10612 2022-03-24T07:14:21Z 2018-12-01T00:00:00Z

Title: The Estimation of Arctic Air Temperature in Summer Based on Machine Learning Approaches Using IABP Buoy and AMSR2 Satellite Data Authors: Han, Daehyeon; Kim, Young Jun; Im, Jungho; Lee, Sanggyun; Lee, Yeonsu; Kim, Hyun-cheol Abstract: It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2) satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed R2 of 0.84-0.88 and RMSE of 1.31-1.53°C. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

2018-12-01T00:00:00Z Chi, Junhwa Kim, Hyun-cheol https://repository.kopri.re.kr/handle/201206/10821 2022-03-24T07:14:04Z 2018-12-01T00:00:00Z

Title: Sea Ice Type Classification with Optical Remote Sensing Data Authors: Chi, Junhwa; Kim, Hyun-cheol Abstract: Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

2018-12-01T00:00:00Z Park, GwangSeob Kim, Hyun-cheol Lee, Taehee Son, Young Baek https://repository.kopri.re.kr/handle/201206/10817 2022-03-24T07:14:32Z 2018-12-01T00:00:00Z

Title: Tracing the Drift Ice Using the Particle Tracking Method in the Arctic Ocean Authors: Park, GwangSeob; Kim, Hyun-cheol; Lee, Taehee; Son, Young Baek Abstract: In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

2018-12-01T00:00:00Z