• Journal of Environmental Science International
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• v.26 no.5
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• pp.573-584
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• 2017

In order to simulate a typhoon precisely, the satellite observation data has been assimilated using WRF (Weather Research and Forecasting model) three-Dimensional Variational (3DVAR) data assimilation system. The observation data used in 3DVAR was GPS Radio Occultation (GPS-RO) data which is loaded on Low-Earth Orbit (LEO) satellite. The refractivity of Earth is deduced by temperature, pressure, and water vapor. GPS-RO data can be obtained with this refractivity when the satellite passes the limb position with respect to its original orbit. In this paper, two typhoon cases were simulated to examine the characteristics of data assimilation. One had been occurred in the Western Pacific from 16 to 25 October, 2015, and the other had affected Korean Peninsula from 22 to 29 August, 2012. In the simulation results, the typhoon track between background (BGR) and assimilation (3DV) run were significantly different when the track appeared to be rapidly change. The surface wind speed showed large difference for the long forecasting time because the GPS-RO data contained much information in the upper level, and it took a time to impact on the surface wind. Along with the modified typhoon track, the differences in the horizontal distribution of accumulated rain rate was remarkable with the range of -600~500 mm. During 7 days, we estimated the characteristics between daily assimilated simulation (3DV) and initial time assimilation (3DV_7). Because 3DV_7 demonstrated the accurate track of typhoon and its meteorological variables, the differences in two experiments have found to be insignificant. Using observed rain rate data at 79 surface observatories, the statistical analysis has been carried on for the evaluation of quantitative improvement. Although all experiments showed underestimated rain amount because of low model resolution (27 km), the reduced Mean Bias and Root-Mean-Square Error were found to be 2.92 mm and 4.53 mm, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.6
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• pp.811-815
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• 2006

The objective of this study was to evaluate the effect of electric fan installation for milk production improvement of dairy cattle in Thailand. The study was conducted using 2 small-holder dairy farms in Chiang Mai province, during April to August 2004. Electric fans were installed in front of each row of cows. Each of the two rows of cows in the barn was defined as an experimental unit, thus each farm had two experimental units. The fans were operated alternately in 7-day intervals between rows of cows within each farm during the day or between 8.00 am to 8.00 pm. Non-operation periods were used as control. Milk yields were recorded. Data on environmental temperature and humidity were obtained from Chiang Mai Meteorological Center. Result from statistically analysis of milk record suggested an interaction between lactation period and fan installation. Therefore, this interaction term of lactation period and fan installation (PERIOD_FAN) was added as a variable to the regression model. Due to the repeated data collection of milk yield from the same cow (alternate week), milk yield was analyzed by repeated measure analysis (Mixed model). Least square means were calculated for all levels and used to compare between each pair-wise values. The final data were collected from the total of 18 cows with 2,072 data. Overall means and SEM of milk yields and days in milk separated into farm were $14.7{\pm}0.06kg/day$ and $176.3{\pm}2.2days$, and $15.2{\pm}0.22kg/day$ and $202.5{\pm}3.7$ days for farm A and farm B, respectively. For multivariable analysis, only PERIOD_FAN and humidity were significantly associated with milk yield. Only the first period of lactation showed that the amount of milk yields during fan installation was higher than that of non-fan installation (p<0.05). Cows with fan installation produced approximately 1.2 kg/cow more milk than cows without fan installation during this period. In conclusion, the use of electric fan operated during the day time increased milk production of cows during the first period of lactation.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1235-1247
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• 2013

This study is to evaluate the climate change impact on future storage behavior of Chungju dam($2,750{\times}10^6m^3$) and the regulation dam($30{\times}10^6m^3$) using SWAT(Soil Water Assessment Tool) model. Using 9 years data (2002~2010), the SWAT was calibrated and validated for streamflow at three locations with 0.73 average Nash-Sutcliffe model Efficiency (NSE) and for two reservoir water levels with 0.86 NSE respectively. For future evaluation, the HadCM3 of GCMs (General Circulation Models) data by scenarios of SRES (Special Report on Emission Scenarios) A2 and B1 of the IPCC (Intergovernmental Panel on Climate Change) were adopted. The monthly temperature and precipitation data (2007~2099) were spatially corrected using 30 years (1977~2006, baseline period) of ground measured data through bias-correction, and temporally downscaled by Change Factor (CF) statistical method. For two periods; 2040s (2031~2050), 2080s (2071~2099), the future annual temperature were predicted to change $+0.9^{\circ}C$ in 2040s and $+4.0^{\circ}C$ in 2080s, and annual precipitation increased 9.6% in 2040s and 20.7% in 2080s respectively. The future watershed evapotranspiration increased up to 15.3% and the soil moisture decreased maximum 2.8% compared to baseline (2002~2010) condition. Under the future dam release condition of 9 years average (2002~2010) for each dam, the yearly dam inflow increased maximum 21.1% for most period except autumn. By the decrease of dam inflow in future autumn, the future dam storage could not recover to the full water level at the end of the year by the present dam release pattern. For the future flood and drought years, the temporal variation of dam storage became more unstable as it needs careful downward and upward management of dam storage respectively. Thus it is necessary to adjust the dam release pattern for climate change adaptation.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.85-95
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• 2020

The filtration characteristics of H₂O-C₆H₁₂O₆ solution at cell membrane model in renal tubule which irradiated by high energy x-ray(linac 6MV) was investigated. The cell membrane model used in this experiment was a polysulfonated copolymerized membrane of m-phenylene-diamine(MPD) and trimesoyl chloride(TMC)-hexane. They were used to two cell membrane models(CM-1, CM-2). The cell membrane model composed of 0.5 wt% TMC-hexane solution(CM-2) had higher permeate flux(Jv) and rejection coefficient(R) than composed of 0.1 wt% TMC-hexane solution(CM-1). The permeate flux(Jv) and rejection coefficient(R) of H₂O-C₆H₁₂O₆ solution in two cell membrane models(CM-1, CM-2) were increased with increase of pressure drop and effective pressure difference. In this experiment range(pressure 1.5-4 MPa, temperature 36.5 ℃), permeate flux(Jv) of H₂O solvent in irradiated membrane was found to be decreased about 20-30 times than non-irradiated membrane, permeate flux(Jv) and rejection coefficient(R) of H₂O-C₆H₁₂O₆ solution in irradiated membrane was found to be decreased about 2-13 times, about 4-6 times than non-irradiated membrane, respectively. The concentration increase of H₂O-C₆H₁₂O₆ solution at cell membrane model significantly was increased at rejection coefficient(R), was decreased at permeate flux(Jv). As the filtration of H₂O-C₆H₁₂O₆ solution in cell membrane model were abnormal, cell damages were appeared at cell.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.11-22
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• 2010

According to IPCC 4th Assessment Report, concentration of carbon dioxide has been increasing by 30% since Industrial Revolution. Most of IPCC $CO_2$ emission scenarios estimate that the concentration will reach up to double of its present level within 100-year if the current tendency continues. The global warming has resulted in the agro-climate change over the Korean Peninsula as well. Accordingly, it is necessary to understand the future agro-climate induced by the increase of greenhouse gases in terms of the agro-climatic indices in the Korean peninsula. In this study, the future climate is simulated by an atmosphere/ocean/land surface/sea ice coupled general circulation climate model, Pusan National University Coupled General Circulation Model(hereafter, PNU CGCM), and by a regional weather prediction model, Weather Research and Forecasting Model(hereafter, WRF) for the purpose of a dynamical downscaling. The changes of the vegetable period and the crop growth period, defined as the total number of days of a year exceeding daily mean temperature of 5 and 10, respectively, have been analyzed. Our results estimate that the beginning date of vegetable and crop growth periods get earlier by 3.7 and 17 days, respectively, in spring under the $CO_2$-doubled climate. In most of the Korean peninsula, the predicted frost days in spring decrease by 10 days. Climatic production index (CPI), which closely represent the productivity of rice, tends to increase in the double $CO_2$ climate. Thus, it is suggested that the future $CO_2$ doubled climate might be favorable for crops due to the decrease of frost days in spring, and increased temperature and insolation during the heading date as we expect from the increased CPI.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Agricultural Science
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• v.7 no.2
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• pp.145-155
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• 1980

An engineering design of the machines and equipment for processing grain as well as an understanding of processing itself need the knowledge of thermal properties of grain. Thermal properties of grain are thermal conductivity, thermal diffusivity and specific heat. Knowledge of any two and the bulk density of grain enables the third to be calculated. Several workers have investigated these properties, with special emphasis on thermal conductivity and diffusivity. However, some information is available on the specific heat of rough rice and barley but it is available only for a foreign variety of grain and for as a function of moisture content only. The objectives of this study were to develop a model for the specific heat of rough rice and barley which were a staple products in Korea as a function of initial temperature, moisture content and porosity of grain with cooling curve method, and to analyze the effect of these factors on the specific heat of rough rice and barley. The results of this study are summarized as follows; 1. The specific heat was $1.8209-2.7041kJ/kg\;^{\circ}K$ for Naked barley, 1.8862-2.5625 k.l/kg K for Covered barley, $1.5167-2.3779kJ/kg\;^{\circ}K$ for Japonica rice and $1.5260-2.3981kJ/kg\;^{\circ}K$ for Indica rice. 2. The model for the specific heat of rough rice and barley as a function of initial temperature, moisture content and porosity of grain was developed. 3. Specific heat of rough rice was decreased with initial temperature, but specific heat of barley was increased with initial temperature. 4. On the whole specific heat of sample grain was increased with moisture content of grain. 5. Specific heat of the grain was found to decrease with porosity except Indica rice.

• Korean journal of applied entomology
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• v.55 no.1
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• pp.17-26
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• 2016

Climate change has been regarded as one of main factors to change Korean insect pest fauna. Especially, a global warming model predicts to expand habitat for insect pests originated from tropical or subtropical regions. Two insect pests, the beet armyworm (Spodoptera exigua) and the diamondback moth (Plutella xylostella), are known to overwinter in some greenhouse conditions without diapause induction in Korea. There was a clear difference between these two insects in seasonal occurrence. P. xylostella occurred only at early spring and fall seasons, but did not occur during summer. In contrast, S. exigua maintained their occurrence from late spring to fall seasons. This study set up a hypothesis that the difference in the seasonal occurrence may be resulted from variation in susceptibility to high temperature. To test the hypothesis, heat tolerance was compared between these two insects. Exposure to $42^{\circ}C$ for 40 min killed 100% individuals of P. xylostella larvae. However, most larvae of S. exigua survived in response to $42^{\circ}C$ even for 80 min. Heat tolerance varied among developmental stages in both insects. Highest tolerant stages were $4^{th}$ instar larvae and adults for P. xylostella, but $1^{st}$ instar larvae for S. exigua. Pre-exposure to $37^{\circ}C$ for 30 min significantly increased heat tolerance in both insects. Induction of heat tolerance accompanied with significant increase of glycerol contents in the hemolymph in both insects and up-regulation of three heat shock protein expressions in S. exigua. These results suggest that the differential susceptibility to high temperature explains the disappearance of P. xylostella during summer, at which S. exigua maintains its occurrence.