• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2818-2826
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• 2010

To study the spatial and temporal variations of sea surface temperature(SST) in the Northeast Asia sea during the period of 1985 to 2009. At first, the buoy data from Korea Meteorological Administration(KMA) and the satellite data have been matched up eight points. The root mean square error and the bias were increased towards the coastal shallow region. The study area which is divided 7 regions from Japan Meteorological Agency(JMA). We analyzed NOAA/AVHRR data by harmonic analysis which is comparison and analysis the center of the each regions. The mean SST varied between $8^{\circ}C$ to $26.0^{\circ}C$. The annual amplitude varied between $7^{\circ}C$ to $24^{\circ}C$. And the annual phase varied between end of July to end of August. Cross-correlation coefficients of mean SST, annual amplitude, and annual phase varied 0.57 to 0.85, -0.04 to 0.81 and 0.35 to 0.80 at all study area, respectively.

• Journal of the Korean earth science society
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• v.34 no.7
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• pp.662-668
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• 2013

We examine the effects of El Ni$\tilde{n}$o on tropospheric ozone through the simulation of a Climate-Chemistry model for a 40-year period (1971-2010). The Empirical Orthogonal Function (EOF) analysis reveals that the tropospheric ozone concentration in the central-eastern Pacific decreases when the El Ni$\tilde{n}$o occurs, which is consistent with the observation. However, the increase of ozone over Indian Ocean-Indonesia regions is weak in the simulation compared to the observations. We analyze details of the 2006 El Ni$\tilde{n}$o event to understand the mechanism that caused the change of ozone due to El Ni$\tilde{n}$o. It is found that enhanced convection as well as higher water vapor followed by shortened lifetime has led to lower the tropospheric ozone. Downward motion induced by the changes of atmospheric circulation due to sea surface temperature forcing, together with the decrease of water vapor, has brought ozone produced in the upper troposphere over the Indian Ocean.

• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Journal of the Korean earth science society
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• v.28 no.1
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• pp.35-44
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• 2007

This paper investigated the relationship between El $Ni\widetilde{n}o-Southern$ Oscillation (ENSO) and Indian Ocean Dipole (IOD) mode events and the impacts of these two phenomena on the climate, temperature and precipitation, of the Korean Peninsula. Data gathered from 1954 to 2004 were used for analysis, which included NINO 3 index, IOD index, and monthly mean precipitation and temperature at eleven locations in Korea. Statistical results showed that the IOD and ENSO were significantly correlated in Spring and Fall. It was clearly shown that the distribution of the sea surface temperature in the Indian Ocean has seen the Southern and Northern Oscillation in El $Ni\widetilde{n}o$ year, and Eastern and Western in IOD year. On the other hand, in El $Ni\widetilde{n}o$ you, the mean temperature of the Korea Peninsula was lower than normal in Summer and higher in Winter and its precipitation was more than normal in both Summer and Winter. However, significant correlation was not found in IOD year. In addition, the global atmospheric circulations during the major IOD years are less influential, unlike those of El $Ni\widetilde{n}o$ events.

• Journal of the Korean Geographical Society
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• v.51 no.1
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• pp.23-40
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• 2016

In this study, long-term changes in means and extreme events of precipitation during summer rainy period called Changma (late June~early September) are examined based on rainfall data observed by Chukwooki during Joseon Dynasty (1777~1907) and by modern rain-gauge onward (1908~2015) in Seoul, Korea. Also, characterizations of the relevant changes in synoptic climate fields in East Asia are made by the examination of the NCEP-NCAR reanalysis I data. Analyses of 239-year time series of precipitation data demonstrate that the total precipitation as well as their inter-annual variability during the entire Changma period (late June~early September) has increased in the late 20th century and onward. Notably, since the early 1990s the means and extreme events during the summer Changma period (late June~mid-July) and Changma break period (late July~early August) has significantly increased, resulting in less clear demarcations of sub-Changma periods. In this regard, comparisons of synoptic climate fields before and after the early 1990s reveal that in recent decades the subtropical high pressure has expanded in the warmer Pacific as the advection of high-latitude air masses toward East Asia was enhanced due to more active northerly wind vector around the high pressure departure core over Mongolia. Consequently, it is suggested that the enhancement of rising motions due to more active confluence of the two different air masses along the northwestern borders of the Pacific might lead to the increases of the means and extreme events of Changma precipitation in Seoul in recent decades.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.49-63
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• 2016

In this study, we analyzed the effects of regional hydrologic variability during warm season (June-September) in South Korea due to ENSO (El $Ni{\tilde{n}}o$-Southern Oscillation) pattern changes over the Tropical Pacific Ocean (TPO). We performed composite analysis (CA) and statistical significance test by Student's t-test using observed hydrologic data (such as, precipitation and streamflow) in the 113 sub-watershed areas over the 5-Major River basin, in South Korea. As a result of this study, during the warm-pool (WP) El $Ni{\tilde{n}}o$ year shows a significant increasing tendency than normal years. Particularly, during the cold-tongue (CT) El $Ni{\tilde{n}}o$ decaying years clearly decreasing tendency compared to the normal years was appeared. In addition, the La $Ni{\tilde{n}}a$ years tended to show a slightly increasing tendency and maintain the average year state. In addition, from the result of scatter plot of the percentage anomaly of hydrologic variables during warm season, it is possible to identify the linear increasing tendency. Also the center of the scatter plot shows during the WP El $Ni{\tilde{n}}o$ year (+17.93%, +26.99%), the CT El $Ni{\tilde{n}}a$ year (-8.20%, -15.73%), and the La $Ni{\tilde{n}}a$ year (+8.89%, +15.85%), respectively. This result shows a methodology of the tele-connection based long-range water resources prediction for reducing climate forecasting uncertainty, when occurs the abnormal SSTA (such as, El $Ni{\tilde{n}}o$ and La $Ni{\tilde{n}}a$) phenomenon in the TPO region. Furthermore, it can be a useful data for water managers and end-users to support long-range water-related policy making.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.