• Horticultural Science & Technology
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• v.31 no.5
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• pp.531-537
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• 2013

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

• 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.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.504-515
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• 2021

To examine the relationship between environmental factors influencing the habitat of Aconitum austrokoreense Koidz., this study employed the MexEnt model to evaluate 21 environmental factors. Fourteen environmental factors having an AUC of at least 0.6 were found to be the age of stand, growing stock, altitude, topography, topographic wetness index, solar radiation, soil texture, mean temperature in January, mean temperature in April, mean annual temperature, mean rainfall in January, mean rainfall in August, and mean annual rainfall. Based on the response curves of the 14 descriptive factors, Aconitum austrokoreense Koidz. on the Baekun Mountain were deemed more suitable for sites at an altitude of 600 m or lower, and habitats were not significantly affected by the inclination angle. The preferred conditions were high stand density, sites close to valleys, and distribution in the northwestern direction. Under the five-age class system, the species were more likely to be observed for lower classes. The preferred solar radiation in this study was 1.2 MJ/m². The species were less likely to be observed when the topographic wetness index fell below the reference value of 4.5, and were more likely observed above 7.5 (reference of threshold). Soil analysis showed that Aconitum austrokoreense Koidz. was more likely to thrive in sandy loam than clay. Suitable conditions were a mean January temperature of - 4.4℃ to -2.5℃, mean April temperature of 8.8℃-10.0℃, and mean annual temperature of 9.6℃-11.0℃. Aconitum austrokoreense Koidz. was first observed in sites with a mean annual rainfall of 1,670- 1,720 mm, and a mean August rainfall of at least 350 mm. Therefore, sites with increasing rainfall of up to 390 mm were preferred. The area of potential habitats having distributive significance of 75% or higher was 202 ha, or 1.8% of the area covered in this study.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.215-224
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• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.

• KSBB Journal
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• v.11 no.2
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• pp.151-158
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• 1996

For the effective ethanol fermentation, the high concentration of sugar as the substrate of microbial fermentation is required. The most important reason in the inefficient hydrolysis; the easy deactivation of enzyme by temperature or shear stress and the severe inhibition effects of its products. In our work, we comprehended the kinetic characteristics of cellulose and ${\beta}$-glucosidase in the progress of hydrolysis, and observed the potential inhibitory effects of the hydrolyzed products and the deactivation of enzymes. We also tried to present the kinetic model of enzymatic hydrolysis of cellulose, which is applicable to process at the high concentration of sugar. Cellulase and ,${\beta}$-glucosidase exhibit diverse kinetic behaviors. At a level of only 5g/$\ell$ of glucose, the ${\beta}$-glucosidase activity was reduced by more than 70%. This result means that ${\beta}$-glucosldase was the most severely inhibited by glucose. Also at l0g/$\ell$ of cellobiose, the cellulose lost approximately 70% of its activity. ${\beta}$-glucosldase was more sensitive to deactivation than cellulose by about 1.6 times. The comprehensive kinetic model in the range of confidence was obtained and the agreement between the model prediction and the experimental data was reasonably good, testifying to the validity of the model equations used and the associated parameters.

• Journal of the Korean Society for Marine Environment & Energy
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• v.7 no.1
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• pp.22-29
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• 2004

An international project, known as Argo, for collecting data on temperature, salinity and velocity of currents in the world's oceans, has been started in the year 2000 and the full Argo array of approximately 3000 floats will be deployed by 2006. 18 countries deployed 1,023 floats, which are operating in the ocean of the world as of December 2003. In the present study, we tried to predict float distribution and a rate of drifting ashore of the floats after their termination based upon a product of the ocean general circulation model of JAMSTEC (Japan Marine Science and Technology Center). We first evaluated reliability of the model prodilct quantitatively by comparing trajectories of surface buoys of WOCE Surface Velocity Program (SVP) and those predicted by the model surface current field. It is found that the model is acceptable for practical application to deploy floats and to estimate those trajectories. 653 particles at 3-degree spacing are used to investigate the ratio of floats drifted ashore, given that during the first 4 years floats cycle between the surface and 2000m for 10 days and then floats are on just the surface for 100 years. The simulation indicates that about 29% of deployed floats will be drifted ashore within 100-year.

• Journal of the Korean Society for Precision Engineering
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• v.3 no.1
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• pp.40-49
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• 1986

Crack, craze and void are common defects which may be found in the bulk of polymeric materials such as either themoplastics or thermosets. The healing phenomena, autohesion, of these defects are known to be a intrinsic material property of various polymeric materials. However, only a few experimental and theoretical investigations on crack, void and craze healing phenomena for various polymeric materials have been reported up to date [1, 2, 3]. This may be partly due to the complications of healing processes and lacking of appropriate theoretical developments. Recently, some investigators have been urged to study the healing phenomena of various polymenic materials since the significance of the use of polymer based alloys or composites has been raised in terms of specific strength and energy saving. In the earlier published reports [1, 2, 3, 4], the crack and void healing velocity, healing toughness and some other healing mechanical and physical properties were measured experimentally and compared with predicted values by utilizing a simple model such as the reptation model under some resonable assumptions. It seems, however, that the general acceptance of the proposed modeling analyses is yet open question. The crack healing processes seem to be complicate and highly dependent on the state of virgin material in terms of mechanical and physical properties. Furthermore, it is also strongly dependent on the histories of crack, craze and void development including fracture suface morphology, the shape of void and the degree of disentanglement of fibril in the craze. The rate of crack healing may be a function of environmental factors such as healing temperature, time and pressure which gives different contact configurations between two separated surfaces. It seems to be reasonable to assume that the crack healing processes may be divided in several distinguished steps like stress relaxation with molecular chain arrangement, surface contact (wetting), inter- diffusion process and com;oete healing (to obtain the original strength). In this context, it is likely that we no longer have to accept the limitation of cumulative damage theories and fatigue life if it is probable to remove the defects such as crack, craze and void and to restore the original strength of polymers or polymer based compowites by suitable choice of healing histories and methods. In this paper, we wish to present a very simple and intuitive theoretical model for the prediction of healed fracture toughness of cracked or defective polymeric components. The central idea of this investigation, thus, may be the modeling of behavior of chain molecules under healing conditions including the effects of chain scission on the healing processes. The validity of this proposed model will be studied by making comparisons between theoretically predicted values and experimentally determined results in near future and will be reported elsewhere.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.95-100
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• 2009

Fine fuel moisture code (FFMC), a main component of forest fire weather index(FWI) in the Canadian forest fire danger rating system(CFFDRS), indicated a probability of ignition through expecting a dryness of fine fuels. According to this code, a rising of temperature and wind velocity, a decreasing of precipitation and decline of humidity in a weather condition showed a rising of a danger rate for the forest fire. In this study, we analyzed a weather condition during 5 years in Kangwon province, calculated a FFMC and examined an application of FFMC. Very low humidity and little precipitation was a characteristic during spring and fall fire season in Kangwon province. 75% of forest fires during 5 years occurred in this season and especially 90% of forest fire during fire season occurred in spring. For developing of the prediction model for a forest fire occurrence probability, we used a logistic regression function with forest fire occurrence data and classified mean FFMC during 10 days. Accuracy of a developed model was 63.6%. To improve this model, we need to deal with more meteorological data during overall seasons and to associate a meteorological condition with a forest fire occurrence with more research results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.298-306
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• 2016

Planting date shift is one of the means of adapting to climate change in Kimchi Cabbage growers in major production areas in Korea. This study suggests a method to estimate the potential yield of Kimchi Cabbage based on daily temperature accumulation during the growth period from planting to maturity which is determined by a plant phenology model tuned to Kimchi Cabbage. The phenology model converts any changes in the thermal condition caused by the planting date shift into the heat unit accumulation during the growth period, which can be calculated from daily temperatures. The physiological maturity is estimated by applying this model to a variable development rate function depending either on growth or heading stage. The cabbage yield prediction model (Ahn et al., 2014) calculates the potential yield of summer cabbage by accumulating daily heat units for the growth period. We combined these two models and applied to the 1km resolution climate scenario (2000-2100) based on RCP8.5 for South Korea. Potential yields in the current normal year (2001-2010) and the future normal year (2011-2040, 2041-2070, and 2071-2100) were estimated for each grid cell with the planting dates of July 1, August 1, September 1, and October 1. Based on the results, we divided the whole South Korea into 810 watersheds, and devised a three - dimensional evaluation chart of the time - space - yield that enables the user to easily find the optimal planting date for a given watershed. This method is expected to be useful not only for exploring future new cultivation sites but also for developing cropping systems capable of adaptation to climate change without changing varieties in existing production areas.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.111-120
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• 2021

When a fire occurs in a commercial building, the evacuation route is complicated and the direction of smoke and flame is similar to that of the egress route of occupants, resulting in many casualties. Performance-based evacuation design for buildings is essential to minimize human casualties. In order to apply the performance-based evacuation design to buildings, it requires a complex fire simulation for each building, demanding a large amount of time and manpower. In order to supplement this, it would be very useful to develop an Available Safe Egress Time (ASET) prediction model that can rationally derive the ASET without performing a fire simulation. In this study, the correlations between fire temperature with visibility and toxic gas concentration were investigated through a fire simulation on a commercial building, from which databases for the training of artificial neural networks (ANN) were created. Based on this, an ANN model that can predict the available safe egress time was developed. In order to examine whether the proposed ANN model can be applied to other commercial buildings, it was applied to another commercial building, and the proposed model was found to estimate the available safe egress time of the commercial building very accurately.