• KIEAE Journal
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• v.15 no.2
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• pp.27-35
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• 2015

Purpose: Healthcare is on the whole a personal and critical service that consumer's use, whereas hospitalization is as a rule painful, because nature nurtures and Sun Light Luminosity for healthcare settings is considered healing. The performance and design of climate responsive buildings such as AKU requires a detailed study of attributes of climate both at micro as well as macro level. The therapeutic value of contact with nature through window view, greenery and landscape is calculated there. Method: A two prong strategy is been devised for this article, at micro level three typical morphologies are analysed by creating same environment of neighboring building on sun shading chart, radiation and temperature range. Since the analysis of local climate helps to determine the design strategies for hospital Healing Environment which is suitable for Karachi climate; in order to track the macro climatic behaviour, a considerable analysis of psychometrics chart for AKU Karachi are designed on Climate Consultant (CC) and analysed by Machine Learning. Climate Consultant proposes different design strategies suitable for Karachi. And on the other hand time wise illumination sources for clinical area which are then measured on psychrometric chart- according to singular space: multi patient admission, secondly: acute ambulatory ward, and tertiary: multi windowed space according to the mushrabiyah and sky light pattern. Result: Our findings support the hypothesis that windowed wall is 75-80% more healing wall; an accelerated evidence was found for healing at macro level if the form of the hospital is designed according to the climatologically preferences, whereas at micro level: the light resource becomes the staff attentiveness determinant. In Conclusion evidence was provided that the actual form of luminosity results consequently in satisfaction while light entering from several set of windows and other sources might be valued if design according to the healing environment. The data added on the sun shading chart to calculate rays entraining into space in patient room equal to 124416.21 Watts/ meter $m^2$ is calculated as precise healing rate-and is confirmed by questionnaire from patients belonging from each clinical stage having different illnesses.

• Atmosphere
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• v.29 no.2
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• pp.115-129
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• 2019

Surface winds over the ocean influence not only the climate change through air-sea interactions but the coastal erosion through the changes in wave height and direction. Thus, demands on a reliable projection of future changes in surface winds have been increasing in various fields. For the future projections, climate models have been widely used and, as a priori, their simulations of surface wind are required to be evaluated. In this study, we evaluate the climatological mean surface winds over the Korean Waters simulated by five regional climate models participating in Coordinated Regional Climate Downscaling Experiment (CORDEX) for East Asia (EA), an international regional climate model inter-comparison project. Compared with the ERA-interim reanalysis data, the CORDEX-EA models, except for HadGEM3-RA, produce stronger wind both in summer and winter. The HadGEM3-RA underestimates the wind speed and inadequately simulate the spatial distribution especially in summer. This summer wind error appears to be coincident with mean sea-level pressure in the North Pacific. For wind direction, all of the CORDEX-EA models simulate the well-known seasonal reversal of surface wind similar to the ERA-interim. Our results suggest that especially in summer, large-scale atmospheric circulation, downscaled by regional models with spectral nudging, significantly affect the regional surface wind on its pattern and strength.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.112-113
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• 2018

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.48-51
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• 2017

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.615-631
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• 2012

This study provides an evaluation for capability of Integrated Climate and Air quality Modeling System (ICAMS) on future regional scale climate projection. Temperature and precipitation are compared between ground-level observation data and results of regional models (MM5) for the past 30 years over the Korean peninsula. The ICAMS successfully simulates the local-scale spatial/seasonal variation of the temperature and precipitation. The probability distribution of simulated daily mean and minimum temperature agree well with the observed patterns and trends, although mean temperature shows a little cold bias about $1^{\circ}C$ compared to observations. It seems that a systematic cold bias is mostly due to an underestimation of maximum temperature. In the case of precipitation, the rainfall in winter and light rainfall are remarkably simulated well, but summer precipitation is underestimated in the heavy rainfall phenomena of exceeding 20 mm/day. The ICAMS shows a tendency to overestimate the number of washout days about 7%. Those results of this study indicate that the performance of ICAMS is reasonable regarding to air quality predication over the Korean peninsula.

• Atmosphere
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• v.33 no.2
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• pp.155-171
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• 2023

This paper aims to examine from various perspectives how domestic research studies and projects related to climate change have been conducted to mark the 60th anniversary of the Korean Meteorological Society (KMS). The 『50-year History of the Korean Meteorological Society』, published more than a decade ago, has never dealt with the history of development of individual fields of meteorology such as climate change. Therefore, it is of significance to look at the history of research activities and studies achieved by KMS members in the area of climate change over the past 60 years. The research on climate change in KMS is classified by era from the beginning to the latest and the contents are examined by major research projects at that time. During the past 60 years, climatological research in KMS has been mainly focused on general climate, synoptic climate, and applied climate (urban climate) until the 2000s. However, since the 1990s, climate change has become an important area for climate research. The 2000s are the beginning era of climate change research, since the major projects and researches for climate change has begun in the period. The 2010s can be a time when climate change prediction and monitoring are expanded and refined to meet the rapidly increasing demands for climate information from a wide range of areas. We concluded that the development of the research capabilities of the society over the past 60 years, in particular in the past two decades, in the field of climate change research is remarkable.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.2
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• pp.112-126
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• 1991

The atmospheric carbon dioxide concentration is ever-increasing and expected to reach about 600 ppmv some time during next century. Such an increase of $CO_2$ may cause a warming of the earth's surface of 1.5 to 4.5$^{\circ}C$, resulting in great changes in natural and agricultural ecosystems. The climatic scenario under doubled $CO_2$ projected by general circulation model of Goddard Institute for Space Studies(GISS) was adopted to evaluate the potential impact of climate change on agroclimatic resources, net primary productivity and rice productivity in Korea. The annual mean temperature was expected to rise by 3.5 to 4.$0^{\circ}C$ and the annual precipitation to vary by -5 to 20% as compared to current normal climate (1951 to 1980), resulting in the increase of possible duration of crop growth(days above 15$^{\circ}C$ in daily mean temperature) by 30 to 50 days and of effective accumulated temperature(EAT=∑Ti, Ti$\geq$1$0^{\circ}C$) by 1200 to 150$0^{\circ}C$. day which roughly corresponds to the shift of its isopleth northward by 300 to 400 km and by 600 to 700 m in altitude. The hydrological condition evaluated by radiative dryness index (RDI =Rn/ $\ell$P) is presumed to change slightly. The net primary productivity under the 2$\times$$CO_2$ climate was estimated to decrease by 3 to 4% when calculated without considering the photosynthesis stimulation due to $CO_2$ enrichment. Empirical crop-weather model was constructed for national rice yield prediction. The rice yields predicted by this model under 2 $\times$ $CO_2$ climatic scenario at the technological level of 1987 were lower by 34-43% than those under current normal climate. The parameters of MACROS, a dynamic simulation model from IRRI, were modified to simulate the growth and development of Korean rice cultivars under current and doubled $CO_2$ climatic condition. When simulated starting seedling emergence of May 10, the rice yield of Hwaseongbyeo(medium maturity) under 2 $\times$ $CO_2$ climate in Suwon showed 37% reduction compared to that under current normal climate. The yield reduction was ascribable mainly to the shortening of vegetative and ripening period due to accelerated development by higher temperature. Any simulated yields when shifted emergence date from April 10 to July 10 with Hwaseongbyeo (medium maturity) and Palgeum (late maturity) under 2 $\times$ $CO_2$ climate did not exceed the yield of Hwaseongbyeo simulated at seedling emergence on May 10 under current climate. The imaginary variety, having the same characteristics as those of Hwaseongbyeo except growth duration of 100 days from seedling emergence to heading, showed 4% increase in yield when simulated at seedling emergence on May 25 producing the highest yield. The simulation revealed that grain yields of rice increase to a greater extent under 2$\times$ $CO_2$-doubled condition than under current atmospheric $CO_2$ concentration as the plant type becomes more erect.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.

• Safety and Health at Work
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• v.6 no.2
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• pp.97-103
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• 2015

Background: This paper describes the development of a scale for measuring safety climate. Methods: This study was conducted in six manufacturing companies in Iran. The scale developed through conducting a literature review about the safety climate and constructing a question pool. The number of items was reduced to 71 after performing a screening process. Results: The result of content validity analysis showed that 59 items had excellent item content validity index (${\geq}0.78$) and content validity ratio (> 0.38). The exploratory factor analysis resulted in eight safety climate dimensions. The reliability value for the final 45-item scale was 0.96. The result of confirmatory factor analysis showed that the safety climate model is satisfactory. Conclusion: This study produced a valid and reliable scale for measuring safety climate in manufacturing companies.