• Atmosphere
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• v.26 no.2
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• pp.347-356
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• 2016

Radiosonde is an observation equipment that measures pressure (geopotential height), temperature, relative humidity and wind by being launched up from the ground. Radiosonde data which serves as an important element of weather forecast and research often causes a bias in a model output due to accuracy and sensitivity between the different manufacturers. Although Korean Meteorological Administration (KMA) and several institutes have conducted routine and intensive radiosonde observations, very few studies have been done before on the characteristics of radiosonde performance. Analyzing radiosonde observation data without proper understanding of the unique nature of those sensors may lead to a significant bias in the analysis of results. To evaluate performance and reliability of radiosonde, we analyzed the differences between two sensors made by the different manufacturers, which have been used in the campaign of Experiment on Snow Storm At Yeongdong (ESSAY). We improved a couple of methods to launch the balloon being attached with the sensors. Further we examined cloud-layer impacts on temperature and humidity differences for the analysis of both sensors' performance among various weather conditions, and also compared daytime and nighttime profiles to understand temporal dependence of meteorological sensors. The overall results showed that there are small but consistent biases in both temperature and humidity between different manufactured sensors, which could eventually secure reliable precisions of both sensors, irrespective of accuracy. This study would contribute to an improved sounding of atmospheric vertical states through development and improvement of the meteorological sensors.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.69-82
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• 2023

Representative meteorological data of the rural water district, which is the spatial unit of the study, was produced using the grid-based national standard RCP scenario rainfall data provided by the Korea Meteorological Administration. The retrospective reproducibility of the climate model scenario data was analyzed, and the change in climate characteristics in the water district unit for the future period was presented. Finally the data characteristics and differences of each meteorological element according to various spatial resolution conversion and post-processing methods were examined. As a main result, overall, the distribution of average precipitation and R95p of the grid data, has reasonable reproducibility compared to the ASOS observation, but the maximum daily rainfall tends to be distributed low nationwide. The number of rainfall days tends to be higher than the station-based observation, and this is because the grid data is generally calculated using the area average concept of representative rainfall data for each grid. In addition, in the case of coastal regions, there is a problem that administrative districts of islands and rural water districts do not match. and In the case of water districts that include mountainous areas, such as Jeju, there was a large difference in the results depending on whether or not high rainfall in the mountainous areas was reflected. The results of this study are expected to be used as foundation for selecting data processing methods when constructing future meteorological data for rural water districts for future agricutural water management plans and climate change vulnerability assessments.

• Journal of Integrative Natural Science
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• v.17 no.2
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• pp.33-41
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• 2024

Abnormal climate is a phenomenon in which meteorological factors such as temperature and precipitation are significantly higher or lower than normal, and is defined by the World Meteorological Organization as a 30-year period. However, over the past 30 years, abnormal climate phenomena have occurred more frequently around the world than in the past. In Korea, abnormal climate phenomena such as abnormally high temperatures on the Korean Peninsula, drought, heatwave and heavy rain in summer are occurring in March 2023. Among them, heatwaves are expected to increase in frequency compared to other abnormal climates. This suggests that heatwave should be recognised as a disaster rather than just another extreme weather event. According to several previous studies, greenhouse gases and meteorological factors are expected to affect heatwaves, so this paper uses logistic regression and discriminant analysis on meteorological element data and greenhouse gas data in Gwangju from 2008 to 2022. We analyzed the impact of heatwaves. As a result of the analysis, greenhouse gases were selected as effective variables for heatwaves compared to the past, and among them, chlorofluorocarbons were judged to have a stronger effect on heatwaves than other greenhouse gases. Since greenhouse gases have a significant impact on heatwaves, in order to overcome heatwaves and abnormal climates, greenhouse gases must be minimized to overcome heatwaves and abnormal climates.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.13-22
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• 2018

At the time when zero energy building is expected to be generalized, this study examines whether the investment in the passive element or the active element is more effective in terms of energy in the construction process of zero energy building. In other words, the effect of energy demand by passive element and the change of energy generation by active element are examined in terms of the same investment cost. The purpose of this study is to examine the change of energy demand by passive element and the change of energy generation by active element in zero energy building and to make reasonable investment decision by comparing energy with cost aspect. For this purpose, we selected the buildings to be subjected to energy simulation and derive the required energy amount and energy generation amount by using meteorological data of four regions in Korea. The change of energy demand and energy generation according to the change of application condition was derived. In order to compare and analyze the changes in energy demand and generation at the same cost standard through price survey and quotation of window and photovoltaic power generation equipment.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.945-950
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• 2000

Coastal concrete structure is harmed by physical and chemical action of sea water, impact load, meteorological effect and etc. especially, premature reinforcement corrosion in concrete exposed to sea water has an important problem. In this study, the behavior of chloride ions penetrated through the coastal concrete structure with ordinary portland cement or ground granulated blast furnace slag(GGBFS) was modeled. The physicochemical processes including the diffusion of chloride and the chemical reaction of chloride ion with calcium silicate hydrate and the other constituents of hardened cement paste such as$C_3A$ and $C_4AF$were analyzed by using the Finite Element Method. From analysis result, the corrosion of concrete structure with GGBFS begins 1.69~1.76 times later than that of concrete structure with ordinary portland cement.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.365-370
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• 2017

This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.162-168
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• 2010

Information is known to be a key element for the successful operation of a supply chain, which is required of the efficient ordering strategies and accurate predictions of demands. This study proposes a method to effectively utilize the meteorological forecast information in order to make decisions about ordering and prediction of demands by using the Taguchi experimental design. It is supposed that each echelon in a supply chain determines the order quantity with the prediction of precipitation in the next day based on probability forecast information. The precipitation event is predicted when the probability of the precipitation exceeds a chosen threshold. Accordingly, the choice of the threshold affect the performances of a supply chain. The Taguchi method is adopted to deduce a set of thresholds for echelons which is least sensitive to changes in environmental conditions, such as variability of demand distributions and production periods. A simulation of the beer distribution game was conducted to show that the set of thresholds found by the Taguchi method can reduce the cumulative chain cost, which consists of inventory and backlog costs.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.61-64
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• 2003

This study was conducted to investigate the chemical characteristics of $PM^{10}$ at Anmyeon-do during the periods from January 1998 to December 2001. The $PM^{10}$ samples ($PM^{10}$) were collected by High Volume Air sampler (HVAS). The measured items were mass concentration of $PM^{10}$ with the major ions ($Cl^{-}$, ${SO_{4}}^{2-}$, ${NO_3}^{-}$, ${Mg}^{2+}$, ${Ca}^{2+}$, ${K}^{+}$etc.) and metallic elements (AI, Fe, Mn, Cr, Zn, Pb etc.). The chemical analysis of major ion components were made by Ion Chromatography (DX-500) and that of metallic elements were made by Inductively Coupled Plasma Spectrometer (ICP-AES, ICP-Mass). The average mass concentration of $PM^{10}$ increased substantially during the heavy dust periods (Asian Dust cases). For water-soluble ions, concentrations of ${Ca}^{2+}$, ${SO_{4}}^{2-}$ and ${NO_3}^{-}$ were remarkably enhanced. Concentrations and mass fraction of crustal elements such as Na, Mg, Ca, Fe, Mn were highly elevated, but those of pollution-derived heavy metals were appreciably decreased. The factor analysis was conducted in order to make the large and diverse data set as manageable levels and to qualitatively examine the relationship between the variables.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.3
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• pp.351-360
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• 2011

In order to reduce the casualties of fishing boats, the author analyzed the fishing boat accident on the 412 cases in Korean maritime safety tribunal for the 2005~2009, and then studied the relation between the weather element and the accidents. According to this studies, the occurring ratio of sea casualty for fishing boat in fog weather was appeared 1 boat per 1.6 days. It means that the restricted visibility condition gives the most influence on the fishing boat accident. The casualties in winter season from November to next January occurred 139(33.7%), and small boats less than 50tons broke out more casualties with 68.4%. From this we can find that small fishing boats are very deeply affected on the sea weather condition. According to the boat types for fishing the casualty of jig boat was ranked first, and collision accident account for first with 77.9% for the types of casualties. As mentioned above, most sea casualties for small fishing boats were resulted from the human factors such as poor watch keeping in invisibility and the bad sea condition, it is necessary for navigation operators and the manager to take more attention to the meteorological factors.

• Journal of IKEEE
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• v.24 no.4
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• pp.1136-1140
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• 2020

As air pollution caused by fine dust becomes serious, interest in the spread of fine dust and prediction of air quality is increasing. The causes of fine dust are very diverse, and some fine dust naturally occurs through forest fires and yellow dust, but most of them are known to be caused by air pollutants from burning fossil fuels such as petroleum and coal or from automobile exhaust gas. In this paper, the CALPUFF model recommended by the US EPA is used, and CALPUFF diffusion modeling is performed by generating a wind field through the CALMET model as a meteorological preprocessing program that generates a three-dimensional wind field, which is a meteorological element required by CALPUFF. Through this, we propose a fine dust diffusion modeling and air quality prediction system that reflects complex topography.