• 대한원격탐사학회지
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• 제26권2호
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• pp.167-173
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• 2010

본 논문은 통신해양기상위성에 탑재된 해양탑재체의 관측자료를 지상에서 처리하는 영상전처리 시스템을 소개하는 것으로, 주요 기능, 개발 과정, 운영 계획으로 나누어 기술한다. 해양탑재체 영상전처리시스템은 주 시스템과 백업 시스템이 해양위성센터 (한국해양연구원)와 위성운영센터 (한국항공우주연구원)에 각각 설치되어 있으며, 현재 모든 시험을 완료하고 위성 발사 전의 최종 시험 운영 중에 있다. 해양탑재체 영상전처리시스템이 제공할 통신해양기상위성의 해양데이터는 정지궤도에서 연속적으로 한반도 주변을 관측한 것으로서, 해수 온도 변화나 해양 생태계 등의 해양환경연구에 중요한 자료로 활용 가능할 것으로 기대되고 있다.

• 한국농공학회논문집
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• 제61권6호
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• 농업과학연구
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• 제46권1호
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• 대기
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• 제28권4호
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• pp.479-490
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• 2018

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

• 대기
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• 제29권1호
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• pp.13-20
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• 2019

Since 1985, the Dobson Spectrophotometer has been operated at Yonsei University, and this instrument has monitored the daily representative total ozone in Seoul. Climatological value for total ozone in Seoul is updated by using the daily representative observation data from 1985 to 2017. After updating the daily representative total ozone data, seasonal and inter-annual variation of total ozone in Seoul is also estimated after calculating inter-comparison between ground (Dobson Spectrophotometer) and satellite [Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI)] observations. The global average of total ozone measured by satellite is 297 DU, and its recent amount is about 3.5% lower than the global amount in 1980s. In Seoul, daily representative total ozone is ranged from 225 DU to 518 DU with longterm mean value of 324.3 DU. In addition, monthly mean total ozone is estimated from 290 DU (October) to 362 DU (March), and yearly average of total ozone have been continuously increased since 1985. For the long-term trend of total ozone in Seoul, this study is considered the seasonal variation, Solar Cycle, and Quasi-Biennial Oscillation. In addition to the natural oscillation effect, this study also considered to the long-term variation of sudden increase of total ozone due to the secondary ozone peak. By considering these natural effects, the long-term total ozone trends from 1985 to 2017 are estimated to be 1.11~1.46%/decade.

• 대기
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• 제29권1호
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• pp.41-51
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• 2019

The characteristics of the sea breeze were investigated using the wind and temperature data collected from 300-m tower at Boseong from May 2014 to April 2018. Sea breeze day was detected using following criteria: 1) the presence of a clear change in wind direction near sunrise (between 1 hour after sunrise and 5 hours before sunset) and sunset (from 1500 LST to midnight), 2) presence of thermal forcing of sea breeze and 3) no heavy precipitation (rain < $10mm\;d^{-1}$). Sea breeze days occurred on 569 days for 4 years. The monthly distribution of sea breeze day occurrence shows maxima in May and September and minimum in December. The average onset and cessation times of the sea breeze are 0942 LST and 1802 LST, respectively. Although the 10-m wind shows clockwise rotation with time in the afternoon, the observed hodograph does not show an ideal elliptical shape and has different characteristics depending on the upper synoptic wind direction. Vertical structure of sea breeze shows local maximum of wind speed and local minimum of virtual potential temperature at 40 m in the afternoon for most synoptic conditions except for southeasterly synoptic wind ($60^{\circ}{\sim}150^{\circ}$) which is in the same direction as onshore flow. The local minimum of temperature is due to cold advection by sea breeze. During daytime, the intensity of inversion layer above 40 m is strongest in westerly synoptic wind ($240^{\circ}{\sim}330^{\circ}$) which is in the opposite direction to onshore flow.

• 한국환경과학회지
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• 제31권7호
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• pp.637-652
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• 2022

In this study, the contributions of emissions (foreign and domestic) and atmospheric physical and chemical processes to PM_2.5 concentrations were evaluated during a high PM_2.5 episode (March 24-26, 2018) observed on the Jeju Island in the spring of 2018. These analyses were performed using the community multi-scale air quality (CMAQ) modeling system using the brute-force method and integrated process rate (IPR) analysis, respectively. The contributions of domestic emissions from South Korea (41-45%) to PM_2.5 on the Jeju Island were lower than those (81-89%) of long-range transport (LRT) from China. The substantial contribution of LRT was also confirmed in conjunction with the air mass trajectory analysis, indicating that the frequency of airflow from China (58-62% of all trajectories) was higher than from other regions (28-32%) (e.g., South Korea). These results imply that compared to domestic emissions, emissions from China have a stronger impact than domestic emissions on the high PM_2.5 concentrations in the study area. From the IPR analysis, horizontal transport contributed substantially to PM_2.5 concentrations were dominant in most of the areas of the Jeju Island during the high PM_2.5 episode, while the aerosol process and vertical transport in the southern areas largely contributed to higher PM_2.5 concentrations.

• 대기
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• 제32권3호
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• pp.223-233
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• 2022

Recent studies have shown that Madden-Julian Oscillation (MJO) is modulated by Quasi-Biennial Oscillation (QBO) during the boreal winter; MJO becomes more active and predictable during the easterly phase of QBO (EQBO) than the westerly phase (WQBO). Despite growing evidences, climate models fail to capture the QBO-MJO connection. One of the possible reasons is a weak static stability change in the upper troposphere and lower stratosphere (UTLS) by neglecting QBO-induced ozone change in the model. Here, we investigate the possible impact of the ozone-radiative feedback in the tropical UTLS on the QBO-MJO connection by integrating the Global Seasonal Forecasting System 5 (GloSea5) model. A set of experiments is conducted by prescribing either the climatological ozone or the observed ozone at a given year for the EQBO-MJO event in January 2006. The realistic ozone improves the temperature simulation in the UTLS. However, its impacts on the MJO are not evident. The MJO phase and amplitude do not change much when the ozone is prescribed with observation. While it may suggest that the ozone-radiative feedback plays a rather minor role in the QBO-MJO connection, it could also result from model biases in UTLS temperature and not-well organized MJO in the model.

• 한국지하수토양환경학회지:지하수토양환경
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• 제27권5호
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• pp.1-9
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• 2022

A correlation analysis was performed to investigate differences in the response of surface water and groundwater to drought using the Standardized Precipitation Index (SPI). Water level data of 20 agricultural reservoirs, 4 dams, 2 rivers, and 8 groundwater observation wells were used for the analysis. SPI was calculated using precipitation data measured at a nearby meteorological station. The water storage of reservoirs and dams decreased significantly as they responded sensitively to the drought from 2014 to 2016, showing high correlation with SPI of the relatively long accumulation period (AP). The responses of rivers varied greatly depending on the presence of an upstream dam. The water level in rivers connected to an upstream dam was predominantly influenced by the dam discharge, resulting in very weak correlation with SPI. On the contrary, the rivers without dam exhibited a sharp water level rise in response to precipitation, showing higher correlation with SPI of a short-term AP. Unlike dams and reservoirs, the responses of groundwater levels to precipitation were very short-lived, and they did not show high correlation with SPI during the long-term drought. In drought years, the rise of groundwater level in the rainy season was small, and the lowered water level in the dry season did not proceed any further and was maintained at almost the same as that of other normal years. Conclusively, it is confirmed that groundwater is likely to persist longer than surface water even in the long-term drought years.

• 한국환경과학회지
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• 제31권10호
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• pp.815-824
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• 2022

It is necessary to understand the amount of snowfall and area of snow cover of Mt. Halla to ensure the safety of mountaineers and to protect the ecosystem of Mt. Halla against climate change. However, there are not enough related studies and observation posts for monitoring snow load. Therefore, to supplement the insufficient data, this study proposes an analysis of snow load and snow cover using normalized-difference snow index. Using the images obtained from the Sentinel2 satellite, the normalized-difference snow index image of Mt. Halla could be acquired. This was examined together with the meteorological data obtained from the existing observatory to analyze the change in snow cover for the years 2020 and 2021. The normalized-difference snow index images showed a smaller snow pixel number in 2021 than that in 2020. This study concluded that 2021 may have been warmer than 2020. In the future, it will be necessary to continuously monitor the amount of snow and the snow-covered area of Mt. Halla using the normalized-difference snow index image analysis method.