• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.721-732
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• 2016

The accuracy and error characteristics of microwave Sea Surface Temperature (SST) measurements in the Northwest Pacific were analyzed by utilizing 162,264 collocated matchup data between GCOM-W1/AMSR2 data and oceanic in-situ temperature measurements from July 2012 to August 2016. The AMSR2 SST measurements had a Root-Mean-Square (RMS) error of about $0.63^{\circ}C$ and a bias error of about $0.05^{\circ}C$. The SST differences between AMSR2 and in-situ measurements were caused by various factors, such as wind speed, SST, distance from the coast, and the thermal front. The AMSR2 SST data showed an error due to the diurnal effect, which was much higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. In addition, the RMS error tended to be large in the winter because the emissivity of the sea surface was increased by high wind speeds and it could induce positive deviation in the SST retrieval. Low sensitivity at colder temperature and land contamination also affected an increase in the error of AMSR2 SST. An analysis of the effect of the thermal front on satellite SST error indicated that SST error increased as the magnitude of the spatial gradient of the SST increased and the distance from the front decreased. The purpose of this study was to provide a basis for further research applying microwave SST in the Northwest Pacific. In addition, the results suggested that analyzing the errors related to the environmental factors in the study area must precede any further analysis in order to obtain more accurate satellite SST measurements.

• Journal of the Korean GEO-environmental Society
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• v.15 no.5
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• pp.47-56
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• 2014

In winter season, the pore water inside the ground freezes and thaws repetitively due to the cold air temperature. When the freezing-thawing processes are repeated on the ground, the change in soil particle structure occurs and thus the damage of the infrastructure may be following. This study was performed in order to investigate the stiffness change of soils due to the freeze-thaw by using elastic waves. Sand-silt mixtures are prepared with in the silt fraction of 40 %, 60 % and 80 % in weight and in the degree of saturation of 40 %. The specimens are placed into the square freezing-thawing cell by the temping method. For the measurement of the elastic waves, a pair of the bender elements and a pair of piezo disk elements are installed on the cell, and a thermocouple is inserted into soils for the measurement of the temperature. The temperature of the mixtures is decreased from $20^{\circ}C$ to $-10^{\circ}C$ during freezing, is maintained at $-20^{\circ}C$ for 18 hours, is gradually increased up to the room temperature of $20^{\circ}C$ to thaw the specimens. The shear waves, the compressional waves and the temperature are measured during the freeze-thaw process. The experimental result indicates that the shear and the compressional wave velocities after thawing are smaller than those of before freezing. The velocity ratio of after thawing to before freezing of shear wave is smaller than that of the compressional wave. As silt fraction increases from 40 % to 80 %, the shear and compressional wave velocities are gradually increased. This study suggests that the freezing-thawing process in unsaturated soil loosens the soil particle structure, and the shear wave velocity reflects the effect of freezing-thawing more sensitively than the compressional wave velocity.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.953-968
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• 2018

In order to understand the change of surface water temperature in the East China Sea (ECS), this study analyzed the relationship between sea surface temperature (SST), air temperature (AT) and heat flux using satellite and model reanalysis data from 2003 to 2017. SST in the ECS showed the lowest (average : $13.72^{\circ}C$) in March and the highest (average : $28.12^{\circ}C$) in August. AT is highly correlated with SST and shows a similar seasonal change. In August, SST is higher than AT and then continuously higher than AT until winter. To analyze the change of the summer SST in the ECS, we used the SST anomaly value in August to classify the periods with positive (04', 06', 07', 13', 16', 17') and negative (03', 05', 08', 09', 10', 11', 12', 14', 15') values. Spatial similarity between the two periods indicates that SSTs are relatively larger variations in the northern part than in the southern part, and in the western part than in the eastern part in the study area. AT and net heat flux values also show similar changes with SST. However, the periods of the positive SST anomaly have the relatively increasing SST, AT and heat flux values compared to the periods of the negative SST anomaly in the summer season of the ECS. Although the change of SST in the summer season generally well correlates with AT, there were the periods when it was different from general trends between SST and AT (10', 12', 15', 16'). SST in August 2010 and 2012 decreased by $0.5^{\circ}C$ from AT. It suggests that the decreasing SST was considered to be caused by the effects of the typhoon passing through the study area. In August 2015, AT was relatively lower than SST (> $0.5^{\circ}C$), which is might be weakening of the East Asian Summer Monsoon. In August 2016, SST and AT show the highest values during the whole study periods, but SST is higher than AT (> $1^{\circ}C$). From satellite and heat flux data, the variations of SST have been shown to be relatively higher in the area of the expansion Changjiang Diluted Water (CDW) originated from the China coast. More research is needed to analyze this phenomenon, it is believed as not only the effect of rising AT but also the expansion of the low-salinity water.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.357-364
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• 2022

We investigated the early growth characteristics of Quercus rubra planted in six regions (Hwaseong, Yangpyeong, Pyeongchang, Samcheok, Chungju, and Gimje) in South Korea in relation to soil physicochemical properties and meteorological factors. Q. rubra (1-0) were planted at a density of 3,000 trees ha^-1. The average height, root collar diameter (RCD), and volume of 8-year-old Q. rubra planted in 2014 were 3.52 m, 3.84 cm, and 0.0023 m³, respectively. The growth parameters of Q. rubra were the highest and lowest in Hwaseong and Pyeongchang, respectively. Correlation analysis among the soil physicochemical properties, meteorological factors, and plantation growth characteristics found that pH was the only soil factor negatively correlated with RCD, and the other soil factors were not significantly correlated with the growth characteristics. However, growth characteristics were positively correlated to average temperature from March to October and daily maximum temperature; and they were negatively correlated to altitude, topology, and the number of rainy days from March to October. In particular, the trees planted in Hwaseong area showed the best early growth characteristics because this area had the highest daily maximum temperature, the x average temperature from March to October, the low altitude, and it is located close to the foot of a mountain. In Pyeongchang, the early growth characteristics were negatively affected by winter cold damage because of the high altitude, low daily minimum temperature, and damage by wild animals. In Hwaseong, meteorological factors such as temperature and altitude were more highly correlated to growth characteristics of Q. rubra than the physicochemical soil properties. These results will provide useful information for determining suitable sites for Q. rubra plantations and for predicting early growth characteristics in response to environmental factors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• The Korean Journal of Mycology
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• v.14 no.2
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• pp.109-119
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• 1986

The correlations between environmental influences on microorganisms in soil and its effects on disease development in ginseng field were studied to obtain some useful data for increasing ginseng production and effective preventive measures against the root rot caused by soil-borne pathogens. The diseased replanted ginseng fields were selected as the diseased field and the healthy plot in first planted field selected as control in three major Korean ginseng producing areas such as Kumsan, Goesan and Poonggi. The physicochemical characteristics of the soil were analyzed and microorganisms susceptible for root rot of ginseng, such as Fusarium spp. and general fungi were investigated for their population density in various soil conditions. Correlations between soil microbial populations and environmental factors were investigated. The numbers of Fusarium spp. propagules were abundant in fall in both soil conditions. The numbers of Fusarium spp. were 1.9 to 2.6 times higher in replanted field than first planted field except Goesan area. Relative ratio of Fusarium spp. to total fungi propagules in replanted field was 1.6 times higher in replanted field than first planted field indicating higher numbers of Fusarium spp. distributed in replanted field of soil. The numbers of propagules of total fungi were increased in June and July and there was no sensitive variation according to the temperature. There was no significant difference in vertical distributions of total fungi according to soil depth, while the total fungi were abundant in the surface layer and $10{\sim}15\;cm$ layer. The contents of organic matter and phosphate in healthy field were somewhat high, and phosphate/organic matter ratio and Mg contents were high in diseased field. All of the soils showed a weak acidic pH of 4.5 to 5.7. Soil moisture contents were increased during winter season, but did not show any significant changes during the growing periods, showing 24.6% in healthy field and 19.5% in diseased field respectively. Soil temperature was the highest in July and August and the lowest in January and February.

• Atmosphere
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• v.28 no.1
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• pp.99-112
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• 2018

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m ($0.49^{\circ}C$) was much lower than that of HYCOM ($0.73^{\circ}C$). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, $0.61^{\circ}C$; HYCOM, $0.72^{\circ}C$), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.2
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• pp.95-111
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• 2012

Relationship between plankton assemblage and environmental factors in a semi-closed Chunsu Bay was examined. Temporal changes in phytoplankton assemblage was rather drastic than those found in most Korean coastal area in the Yellow Sea primarily due to the seawater temperature (T) and nutrient input from the dikes nearby. Freshwater discharge seemed to cause winter time increase of Diatoms (February) and summer time increase of Dinoflagellates at surface (July to August). Structural change in cell size with time was also found in Diatom. Zooplankton community structure was also changed with season probably due to the food concentration, seawater temperature and salinity (S). From principal component analysis (PCA) of zooplankton distribution, it was postulated that seasonal environmental changes such as T and S could explain about 32% of variability in zooplankton distribution along with phytoplankton cell numbers, while freshwater discharge could explain about 17%. Comparing with past data of 1985-1986, 1991-1992, the distributional patterns and percent composition of major species, Acartia hongi, Paracalanus parvus sensu lato and Centropages abdominalis, were similar. However, the abundances have been increased more than three times. The composition of other taxa than copepods showed significant changes.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.2
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• pp.105-116
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• 2000

In order to investigate the seasonal variation of watermass in the central coast of the southern sea of korea, oceanographic observation on the fishing grounds were carried out by the trainingship of Yosu University on May, Aug. and Nov. in 1998 and Feb. in 1999. The resultes obtained are summerized as follows : 1). The watermass in the fishing ground were divided into the coastal water(30.0~31.6$\textperthousand$ ), mixing water(31.7~33.4$\textperthousand$) and the offshore water(33.5~35.0$\textperthousand$) according to the distribution of salinity from T-S diagram plotted all salinity data observed on May, Aug. and Nov. in 1998 and Feb. in 1999. 2) The ranges of temperature and salinity were from 14.1$^{\circ}C$ to 18.8$^{\circ}C$ and from 32.2$\textperthousand$ to 34.9$\textperthousand$ in spring(May), from 14.2$^{\circ}C$ to 27.7$^{\circ}C$ and from 29.0$\textperthousand$ to 34.7$\textperthousand$ in summer(August), from 13.4$^{\circ}C$ to 21.3$^{\circ}C$ and from 31.45$\textperthousand$ to 34.5$\textperthousand$ in autumn(November) and from 8.2$^{\circ}C$ to 14.8$^{\circ}C$ and from 33.9$\textperthousand$ to 34.6$\textperthousand$ in winter(February), respectively. 3) The distribution of watermass in the fishing ground varied largely each seasons, but a general tendency on the distribution was obtained. That is, in spring and autumm the offshore water was distributed most widely and in summer the coastal and mixing water occupied the fishing ground but in winter the offshore water prevailed. 4) Variation of temperature and salinity were appeared between the surface and 30m in the coastal region and between the surface and 50m in the open ocaen region. Therefore, in the summer the thermocline and halocline were made between surface and 30m layer with vertical gradients of 10.5$^{\circ}C$/30m and 4.0$\textperthousand$/30m in the coastal region and in the open ocean region the thermocline and halocline were made between surface and 50m layer with vertical gradients of 13.$0^{\circ}C$/50m and 3.8$\textperthousand$/50m.