• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.969-977
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• 2016

Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model

• Advances in environmental research
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• v.9 no.2
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• pp.97-107
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• 2020

The indiscriminate growth in global population poses a threat to the world in handling and disposal of Municipal solid waste. Rapid urban growth increases the production, consumption and generation of Municipal solid waste which leads to a drastic change in the environment. The methane produced from the Municipal Solid waste accounts for up to 11% global anthropogenic emissions, which is a major cause for global warming. This study reports the methane emission estimation using IPCC default, TNO, LandGEM, EPER and close flux chamber from open dump yards at Perungudi and Kodungaiyur in Chennai, India. The result reveals that the methane emission using close flux chamber was in the range of 8.8 Gg/yr-11.3 Gg/yr and 6.1Gg/yr to 9.1 Gg/yr at Kodungaiyur and Perungudi dump yard respectively. The per capita waste generation was estimated based on waste generation and population. The waste generation potential was projected using linear regression model for the period 2017-2050. The trend of CH₄ emission in the actual field measurement were increased every year, similarly the emission trend also increased in IPCC default method (mass balance approach), EPER Germany (zero order decay model) where as TNO and Land GEM (first order decay model) were decreased. The present study reveals that Kodungaiyur dump yard is more vulnerable to methane emission compared to Perungudi dump yard and has more potential in waste to energy conversion mechanisms than compare to Perungudi dump yard.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.210-223
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• 2022

This study explores the criteria of climate change model evaluation by preservice Earth science teachers. The participants in this study were 25 preservice Earth science teachers who attended lectures on modeling-based science learning for 3 weeks in an Earth science education major course. The evaluation criteria of climate change models were categorized inductively using reports written by preservice Earth science teachers and post-interviews. The results showed that preservice Earth science teachers used various epistemic and communicative criteria to evaluate climate change models. Implications for modeling-based climate-change learning were suggested based on these results.

• Atmosphere
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• v.18 no.3
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• pp.199-206
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• 2008

This study employs a structural time series method in order to model and estimate stochastic trend of surface temperatures of the globe, Northern Hemisphere, and Northeast Asia ($20^{\circ}N{\sim}60^{\circ}N$, $100^{\circ}E{\sim}150^{\circ}E$). For this study the reanalysis data CRUTEM3 (CRU/Hadley Centre gridded land-surface air temperature Version 3) is used. The results show that in these three regions range from $0.268^{\circ}C$ to $0.336^{\circ}C$ in 1997, whereas these vary from $0.423^{\circ}C$ to $0.583^{\circ}C$ in 2007. The annual mean temperature over Northeast Asia has increased by $0.031^{\circ}C$ in 2007 compared to 1997. The climate change in surface temperatures over Northeast Asia is slightly higher than that over the Northern Hemisphere.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.169-174
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• 2005

The purpose of this study is to investigate the influence of perceived job insecurity, organizational commitment, and social climate on safety behavior and accidents. The research on this subject was conducted in the Korean National Railroad. The cross-level model was hypothesized to study causal relations among these variables. The cross level model combines individual level approach with group level approach. In this model supervisors(group level) affect organizational commitment(individual level) and at the same time safety climate(group level) influences safety behavior(individual level) positively. Traditionally operators have been blamed as accident causer. This study, however, shows that organizational and systematic factors are as much critical factors determining safety behavior and accidents.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.107-117
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• 2005

Zohar(1980) emphasized the influence of social, organizational, and psychological context in occupational safety and health study. With this research trend, Neal, Griffin, and Hart(2000) developed a sequential safety climate model. In this paper, author examined the usability of their model the relationships among safety climates, safety knowledge, safety motivation, and safety performance(safety compliance and safety participation). The author conducted a survey to 207 manufacturing workers, and the chief results of statistical analysis are as follows : 1) the leadership has positive effects on safety knowledge and motivation, 2) the precaution activities has only positive on safety knowledge, 3) the safety system has only on safety motivation, 4) the safety knowledge and motivation have positive effects on safety compliance and participation.

• Bulletin of the Korean Mathematical Society
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• v.59 no.6
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• pp.1439-1470
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• 2022

In this paper, we study the uniform attractor of the 2D nonautonomous tropical climate model in an arbitrary unbounded domain on which the Poincaré inequality holds. We prove that the uniform attractor is compact not only in the L²-spaces but also in the H¹-spaces. Our proof is based on the concept of asymptotical compactness. Finally, for the quasiperiodical external force case, the dimension estimates of such a uniform attractor are also obtained.

• Korean Journal of Ecology and Environment
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• v.44 no.2
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• pp.187-194
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• 2011

This study analyzed the A1B climate change scenario provided by National Institute of Meteorological Research (NIMR), Korea, to investigate potential climate changes in watersheds of 15 multi-purpose dams in South Korea. The A1B climate change scenario is produced by Regional Climate Model (RCM) with 27 km horizontal grid spacings using a one-way nesting technique with Global Climate Model (GCM). Relative to present climate conditions (1971~ 2000), the modeled 10-year averaged daily temperatures at the watersheds of the 15 multi-purpose dams continuously increased to year 2100, whereas precipitation changes were varied regionally (north, central, and south regions of South Korea). At two watersheds located in Gangwon-province (north region), the modeled temporal variations of precipitation rapidly increased in the 2090's after a slow decrease that had occurred since the 2050's. At seven watersheds in the central region, including Gyeongsangbuk-province to Jeollanam-province, the modeled temporal variations of precipitation increase showed 10-year periodic changes. At six watersheds in the south region, the modeled temporal variations of precipitation increased since the 2070's after a rapid decrease in the 2060's. Compared to the climate conditions of the late of 20th century (1971~2000), the number of rainy days and precipitation intensity increased (3% and 6~12%, respectively) in the late 21st century (2071~2100). The frequency of precipitation events tended to increase with precipitation intensity in all regions. The frequency of heavy precipitation events (>50 mm $d^{-1}$) increased with >100% in the north region, 60~100% in the central region, and 20~60% in the south region.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.581-594
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• 2012

The goal of this study is to investigate the effects of the chemical lateral boundary conditions (CLBCs) on Community Multi-scale Air Quality (CMAQ) simulations of tropospheric ozone for East Asia. We developed linking tool to produce CLBCs of CMAQ from Goddard Earth Observing System-Chemistry (GEOS-Chem) as a global chemistry model. We examined two CLBCs: the fixed CLBC in CMAQ (CLBC-CMAQ) and the CLBC from GEOS-Chem (CLBC-GEOS). The ozone fields by CMAQ simulation with these two CLBCs were compared to Tropospheric Emission Spectrometer (TES) satellite data, ozonesonde and surface measurements for May and August in 2008. The results with CLBC-GOES showed a better tropospheric ozone prediction than that with CLBC-CMAQ. The CLBC-GEOS simulation led to the increase in tropospheric ozone concentrations throughout the model domain, due to be influenced high ozone concentrations of upper troposphere and near inflow western and northern boundaries. Statistical evaluations also showed that the CLBC-GEOS case had better results of both the index of Agreement (IOA) and mean normalized bias. In the case of IOA, the CLBC-GEOS simulation was improved about 0.3 compared to CLBC-CMAQ due to the better predictions for high ozone concentrations in upper troposphere.