• Geophysics and Geophysical Exploration
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• v.15 no.4
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• pp.209-218
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• 2012

Repeatability and reproducibility in solid weight and effective porosity measurements have been discussed using 8 core samples with different diameters, lengths, rock types, and effective porosities. Further, the effect of temperature on the effective porosity measurement has been discussed as well. Effective porosity of each sample has been measured 7 times with vacuum saturation method with vacuum pressure of 1 torr and vacuum time of 80 minutes. Firstly, effective porosity of each sample is measured one by one, so that it can provide a reference value. Then for reproducibility check, effective porosity measurements with vacuum saturation of 2, 4, and 8 samples simultaneously have been performed. And finally, repeated measurements for 3 times for each sample are made for repeatability check. Average deviation from the reference set in solid weight showed 0.00 $g/cm^3$, which means perfect repeatability and reproducibility. For effective porosity, average deviations are less than 0.07% and 0.05% in repeatability and reproducibility test sets, respectively, which are in good agreement too. Most of porosities measured in reproducibility test lies within the deviation range in repeatability test sets. Thus, simultaneous vacuum saturation of several samples has little impact on the effective porosity measurement when high vacuum pressure of 1 torr is used. Air temperature can cause errors on submerged weight read and even effective porosity, because it is closely related to the temperature, density, and buoyancy of water. Consequently, for accurate measurement of effective porosity in a laboratory, efforts for maintaining air or water temperature constant during the experiment, or a temperature correction from other information are needed.

• Korean Journal of Medicinal Crop Science
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• v.4 no.4
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• pp.301-307
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• 1996

The experiment was conducted to clarify the effect of water stress treatment on growth character of Ligusticum chuanxiong Hort. The water stress treatment was imposed artificially on seedling, flowering and rhizome enlargement stage of the plant. The decrease ratio of leaf area compare with control decreased to 24.4% by water stress treatment at seedling stage and to 41.6% at rhizome enlargement stage. The reduction rate of chlorophyll content at the end of water stress treatment was 41.2% at the see­dling stage and no difference at the flowering stage. The chlorophyll content of water stress treatment on seedling and flowering stage was recovered to 95% at harvest time. The ratio of rootlet distribution from top soil to l0cm depth showed maximum to 90% at the seedling stage and to 20cm depth showed max­imum to 6.4% at the rhizome enlargement stage. The dry weight of rootlet was decreased to $19.3{\sim}40.3%$ by water stress treatment. Dry weight of aerial part and underground part of the plant decreased in the order of seedling, flowering, rhizome enlargement and control and the dry weight of aerial part decrease more severely than underground part.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Korean Journal of Plant Resources
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• v.34 no.4
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• pp.287-296
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• 2021

Recently, the pace of global climate change has tremendously increased, causing extreme damage to crop production. Here, we aimed to examine the growth characteristics and useful components of Angelica gigas under extreme heat stress, providing fundamental data for its efficient cultivation. Plants were exposed to various experimental temperatures (28℃, 34℃, and 40℃), and their growth characteristics and content of useful components were analyzed. At the experimental site, the ambient and soil temperature were 19.38℃ and 21.34℃, ambient and soil humidity were 81.3 % and 0.18 m³/m³, solar radiation was 162.05 W/m². Moreover, the soil was sandy-clay-loam (pH 6.65), with 2.66% organic matter, 868.52 mg/kg soil available phosphate, and 0.14% nitrogen. Values of most growth characteristics, including the survival rate (85%), plant height (38.66cm), and fresh and dry weight (41.3 g and 14.24 g), were the highest at 28℃. Although the highest content of useful components was observed at 34℃ (3.24%), there were no significant differences across temperatures. Growth characteristics varied across temperatures due to detrimental effects of heat stress, such as accelerated tissue aging, reduced photosynthesis, and delay of growth. Similar content of useful components across temperatures may be due to poor accumulation of anabolic products caused by impaired growth at extremely high temperatures.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.2
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• pp.161-167
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• 2020

Objectives : Although the seasonality of suicide is a well-known phenomenon, little is reported about the seasonality of non-suicidal self-injury. The purpose of this study was to identify the seasonality of wristcutting behavior and to examine its relationship with meteorological factors. Methods : To identify the presence of seasonality, we investigated whether there was a difference in the average number of visits per month to an emergency room (ER) of an urban hospital for 226 patients with wrist-cutting behavior enrolled between December 2014 and May 2019. To ascertain significant meteorological factors, we used the multiple Poisson regression using generalized additive model with time, monthly temperature, monthly sunshine hour, and atmospheric pressure in the prior month as explanatory variables. Results : In males, the average number of monthly visits to the ER for wrist cutting behavior differed by month and was the highest in September (male : p=0.048, female : p=0.21, total : p=0.28). As a result of multiple regression analysis, the average number of patients admitted to the ER for wrist cutting behavior was related to the interaction between atmospheric pressure in the prior month and temperature in males (p=0.010), and showed a positive correlation with sunlight in females [p=0.044, β=4.70×10-3, 95% CI=(1.19×10-4, 9.27×10-3)]. Conclusions : Wrist cutting behavior shows seasonality in male, which is associated with changes in meteorological variables.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.3
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• pp.170-177
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• 2007

In this study, the long-term predictability of El Nino and La Nina events of Pusan National University Coupled General Circulation Model(PNU/CME CGCM) developed from a Research and Development Grant funded by Korea Meteorology Administration(KMA) was examined in terms of the correlation coefficients of the sea surface temperature between the model and observation and skill scores at the tropical Pacific. For the purpose, long-term global climate was hindcasted using PNU/CME CGCM for 12 months starting from April, July, October and January(APR RUN, JUL RUN, OCT RUN and JAN RUN, respectively) of each and every years between 1979 and 2004. Each 12-month hindcast consisted of 5 ensemble members. Relatively high correlation was maintained throughout the 12-month lead hindcasts at the equatorial Pacific for the four RUNs starting at different months. It is found that the predictability of our CGCM in forecasting equatorial SST anomalies is more pronounced within 6-month of lead time, in particular. For the assessment of model capability in predicting El Nino and La Nina, various skill scores such as Hit rates and False Alarm rate are calculated. According to the results, PNU/CME CGCM has a good predictability in forecasting warm and cold events, in spite of relatively poor capability in predicting normal state of equatorial Pacific. The predictability of our CGCM was also compared with those of other CGCMs participating DEMETER project. The comparative analysis also illustrated that our CGCM has reasonable long-term predictability comparable to the DEMETER participating CGCMs. As a conclusion, PNU/CME CGCM can predict El Nino and La Nina events at least 12 months ahead in terms of NIino 3.4 SST anomaly, showing much better predictability within 6-month of leading time.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.4
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• pp.386-408
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• 1988

A study was conducted to develop a model for estimating evapotranspiration and yield of Chinese cabbages from meteorological factors from 1981 to 1986 in Suweon, Korea. Lysimeters with water table maintained at 50cm depth were used to measure the potential evapotranspiration and the maximum evapotranspiration in situ. The actual evapotranspiration and the yield were measured in the field plots irrigated with different soil moisture regimes of -0.2, -0.5, and -1.0 bars, respectively. The soil water content throughout the profile was monitored by a neutron moisture depth gauge and the soil water potentials were measured using gypsum block and tensiometer. The fresh weight of Chinese cabbages at harvest was measured as yield. The data collected in situ were analyzed to obtain parameters related to modeling. The results were summarized as followings: 1. The 5-year mean of potential evapotranspiration (PET) gradually increased from 2.38 mm/day in early April to 3.98 mm/day in mid-June, and thereafter, decreased to 1.06 mm/day in mid-November. The estimated PET by Penman, Radiation or Blanney-Criddle methods were overestimated in comparison with the measured PET, while those by Pan-evaporation method were underestimated. The correlation between the estimated and the measured PET, however, showed high significance except for July and August by Blanney-Criddle method, which implied that the coefficients should be adjusted to the Korean conditions. 2. The meteorological factors which showed hgih correlation with the measured PET were temperature, vapour pressure deficit, sunshine hours, solar radiation and pan-evaporation. Several multiple regression equations using meteorological factors were formulated to estimate PET. The equation with pan-evaporation (Eo) was the simplest but highly accurate. PET = 0.712 + 0.705Eo 3. The crop coefficient of Chinese cabbages (Kc), the ratio of the maximum evapotranspiration (ETm) to PET, ranged from 0.5 to 0.7 at early growth stage and from 0.9 to 1.2 at mid and late growth stages. The regression equation with respect to the growth progress degree (G), ranging from 0.0 at transplanting day to 1.0 at the harvesting day, were: $$Kc=0.598+0.959G-0.501G^2$$ for spring cabbages $$Kc=0.402+1.887G-1.432G^2$$ for autumn cabbages 4. The soil factor (Kf), the ratio of the actual evapotranspiration to the maximum evapotranspiration, showed 1.0 when the available soil water fraction (f) was higher than a threshold value (fp) and decreased linearly with decreasing f below fp. The relationships were: Kf=1.0 for $$f{\geq}fp$$ Kf=a+bf for f$$I{\leq}Esm$$ Es = Esm for I > Esm 6. The model for estimating actual evapotranspiration (ETa) was based on the water balance neglecting capillary rise as: ETa=PET. Kc. Kf+Es 7. The model for estimating relative yield (Y/Ym) was selected among the regression equations with the measured ETa as: Y/Ym=a+bln(ETa) The coefficients and b were 0.07 and 0.73 for spring Chinese cabbages and 0.37 and 0.66 for autumn Chinese cabbages, respectively. 8. The estimated ETa and Y/Ym were compared with the measured values to verify the model established above. The estimated ETa showed disparities within 0.29mm/day for spring Chinese cabbages and 0.19mm/day for autumn Chinese cabbages. The average deviation of the estimated relative yield were 0.14 and 0.09, respectively. 9. The deviations between the estimated values by the model and the actual values obtained from three cropping field experiments after the completion of the model calibration were within reasonable confidence range. Therefore, this model was validated to be used in practical purpose.