• Proceedings of the Korean Institute of Building Construction Conference
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• 2005.11a
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• pp.89-92
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• 2005

Our country has experienced variations in temperature as belong to the area of the continental climate that shows four significant seasons. These occur quality of construction. As the hydration of cement processes, the strength of concrete is developed. In order to improve the quality of concrete, various conditions including temperature and humidity should be maintained appropriately and concrete itself should be cured sufficiently This paper is basic experiment for estimating influence of strength by seasonal mock-up concrete's heat of hydration and estimate relationship of compressive strength development by curing temperature. And show basic document as quality control.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2003.06a
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• pp.161-163
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• 2003

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2001.11a
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• pp.65-65
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• 2001

• Proceedings of the KSCN Conference
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• 2000.11a
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• pp.78-78
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• 2000

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.107-115
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• 2006

To elucidate the relation of leaf development stage to the antioxidative function in leaves of Alnus firma Sieb. et Zucc. growing in polluted areas, we investigated seasonal changes of pigment content and antioxidant enzyme activities from January to June. In abandoned mine and industrial complex areas, antioxidant function against stress of trees was changed with leaf expansion, and antioxidant activity in leaves was highest in June. Among antioxidants, carotenoid, SOD and CAT were correlated with seasonal change. Carotenoid and SOD represented a positive correlation but CAT represented a negative correlation with leaf development. APX and CAT, which remove $H_{2}O_2$, had a complementary function in the antioxidant system. The lowest antioxidant activity was observed in April, and the damage level in leaves, shown as MDA content, was also lowest in April.

• Atmosphere
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• v.31 no.2
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• pp.215-227
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• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.3
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• pp.285-296
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• 2003

The measurement of the radiation energy, trunk temperature, leaf area index (LAI), air temperature, vapor pres-sure, and precipitation has been conducted under a mixed forest at Kwangneung Arboretum during the period of 2001. Characteristics of the diurnal and seasonal variation of the radiative energy were investigated. The aerodynamic roughness length was determined as about 1.6 m and the mean albedo was about 0.1 The downward short-wave radiation was linearly correlated with the net radiation and its correlation coefficient was about 0.96. From this linear relation, the heating coefficient was calculated and its annual mean value was about 0.21 The albedo and heating coefficient was varied with season, surface characteristics, and meteorological conditions. The diurnal and seasonal variations of radiation energy were discussed in terms of the surface characteristics and meteorological conditions. In the daytime, during clear skies, net radiation was dominated by the shortwave radiation. In presence of clouds and fog, the radiation energy was diminished. At night, the net radiation was entirely dominated due to the net longwave radiation. There was no distinct diurnal variation in net radiation flux during the overcast or rainy days. The net radiation was strongest in spring and weakest in winter. The seasonal development in leaf area was also reflected in a strong seasonal pattern of the radiation energy balance. The timing, duration, and maximum leaf area and trunk temperature were found to be an important control on radiation energy budget. The trunk temperature was either equal or warmer than air temperature during most of the growing season because the canopy could absorb a substantial amount of sunlight. After autumn (after the middle of October), the trunk temperature was consistently cooler than air temperature.

• Proceedings of the Korean Society of Community Living Science Conference
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• 2006.05a
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• pp.89-95
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• 2006

• 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.