• Journal of Environmental Science International
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• v.25 no.1
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• pp.67-83
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• 2016

In order to improve the prediction of the regional air quality modeling in the Seoul metropolitan area, a sensitivity analysis using two PBL and microphysics (MP) options of the WRF model was performed during four seasons. The results from four sets of the simulation experiments (EXPs) showed that meteorological variables (especially wind field) were highly sensitive to the choice of PBL options (YSU or MYJ) and no significant differences were found depending on MP options (WDM6 or Morrison) regardless of specific time periods, i.e. day and night, during four seasons. Consequently, the EXPs being composed of YSU PBL option were identified to produce better results for meteorological elements (especially wind field) regardless of seasons. On the other hand, the accuracy of all simulations for summer and winter was somewhat lower than those for spring and autumn and the effect according to physics options was highly volatile by geographical characteristics of the observation site.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.1
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• pp.59-65
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• 2013

In this study, the effects of the compressor for the air conditioning system on the fuel economy were experimentally investigated in an actual automobile. This study aims to analyze the level of contribution of the driving torque of the compressor to the fuel economy. A torque sensor, which is directly set on the clutch of the compressor, is developed to obtain data about the compressor load, which influences the fuel efficiency, and then, the reliability of the torque sensor is verified by comparing the results with those of a torque meter in a bench test. An actual automobile equipped with the compressor and torque sensor is operated in a climate wind tunnel in which appropriate facilities are set up to evaluate the fuel efficiency. The compressor driving torque resulting from the difference in the compressor displacement is found to influence the fuel economy, and the fuel economy is found to be worsened by up to 2~3% with an around 11% increase in the compressor displacement under the same conditions.

• Korean Journal of Remote Sensing
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• v.19 no.3
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• pp.223-231
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• 2003

With the negative influences and damage from Asian dust increasing, it's getting important to investigate the climate and soil condition of the source region of Asian dust. There is a high possibility that the desertification and the drastic decrease of plants in China and Mongolia make worse the situation (bad effects of Asian Dust). To detect the movement of Asian dust caused by air circulation, we need to watch the state of the source region to get useful information for the prevention of the dust pollution, and to predict what part of China will become the source region. Therefore, using TOMS aerosol index data, NCEP reanalysis data that is Remote Sensing data from 1981 to 2000 (except 1993~1996, 4 years), for 16 years, examined the relation between the dust occurrence and weather elements. Dust occurrence appeared much in spring season from March to May in study areas. It had a dry climate during that season as follows : relative humidity about 20~40%, temperature about -5~5$^{\circ}C$, precipitation about 33-180 mm, wind speed about 4-10 ms-1. Dust occurrence and weather element annual change in study areas decreased gradually till 1990, but in Gobi desert the incidence of dust occurrence increased since 1997. As a result, found out that the more the precipitation, the less dust occurrence, because the precipitation and surface wind speed had a direct influence on the soil of the source region of dust.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Journal of the Korean Applied Science and Technology
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• v.39 no.1
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• pp.132-145
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• 2022

The emission factor and activity data of fugitive dust in the domestic agricultural field have been applied to the US inventory system without reflecting the domestic environmental conditions (wind speed, humidity, etc.) and agricultural characteristics. In this study, the temporal resolution was improved for each region by deriving a monthly distribution factor through the application of wind speed and dry season and the spatial resolution was improved for each region by subdivided into dong and ri from ci·gun·gu. Through this study, it is judged that it can be used as an important data for improving the emission and activity data of fugitive dust in the agricultural field that currently exist.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1B
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• pp.71-80
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• 2010

This study aimed to figure out the effect of urbanization on meteorological variables (air temperature, wind speed, relative humidity, solar radiation and precipitation) and reference evapotranspiration (RET). The research area of 6 urban areas and 6 rural areas near each urban area was selected. The monthly average daily data were collected from 12 ground stations operated by Korea Meteorological Administration (KMA) and the changes in climate variables were analyzed. Results of annual analysis have shown that the reference evapotranspiration (RET) tends to increase in urban areas while decreasing in rural areas. In particular, due to rising RET in urban areas and decreasing RET in rural areas, we can infer that the urbanization has affected to the RET. Results of monthly analysis showed that the urbanization has effects on the RET of July compared to other months (January, April and October). The yearly and monthly effects of urbanization on RET were closely related to solar radiation, relative humidity and change in temperature, and related to wind speed.

• Journal of Korean Society of Rural Planning
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• v.22 no.4
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• pp.71-80
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• 2016

Meanwhile, reference evapotranspiration(ET0) is important information for agricultural management including irrigation planning and drought assessment, the database of reference evapotranspiration for future periods was rarely constructed especially at districts unit over the country. The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides several meteorological data such as precipitation, average temperature, humidity, wind speed, and radiation for long-term future period at daily time-scale. This study aimed to build a database for reference evapotranspiration using the climate forecasts at high resolution (the outputs of HadGEM3-RA provided by Korea Meteorological Administration (KMA)). To estimate reference evapotranspiration, we implemented four different models such as FAO Modified Penman, FAO Penman-Monteith, FAO Blaney-Criddle, and Thornthwaite. The suggested database system has an open architecture so that user could add other models into the database. The database contains 5,050 regions' data for each four models and four Representative Concentration Pathways (RCP) climate change scenarios. The developed database system provides selecting features by which the database users could extract specific region and period data.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.176-179
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• 2009

In this study, Urban Climate Simulation was performed using 3-Dimensional Urban Canopy Model. The characteristics of urban thermal environment was analyzed by classifying land coverage and increasing natural land coverage ratio. The results are as follows. The characteristics of the land coverage on urban thermal environment formation can be summarized by the effects like higher temperature on the artificial coverage, and the contrary effects on the natural coverage. When the water coverage 100% was made up, maximum temperature was declined by $5.5^{\circ}C$, humidity by the 6.5g/kg, wind velocity by 0.6m/s, convective sensible heat by $400W/m^2$ and the evaporative latent heat was increased by $370W/m^2$ compared to when artificial coverage 100% was formed. These simulation results need to be constructed as DB which shows urban quantitative thermal characters by the urban physical structure. These can be quantitative base for suggesting combinations of the building and urban planning features at the point of the desirable urban thermal environment as well as analysing urban climate phenomenon.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.367-379
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• 2010

Future climate changes over the Seoul metropolitan area (SMA) were predicted by the Weather Research and Forecasting (WRF) model using future land-use data from the urban growth model (SLEUTH) and forecast fields from ECHAM5/MPI-OM1 GCM (IPCC scenario A1B). Simulations from the SLEUTH model with GIS information (slope, urban, hill-shade, etc.) derived from the water management information system (WAMIS) and the intelligent transportation systems-standard nodes link (ITS-SNL) showed that considerable increase by 17.1% in the fraction of urban areas (FUA) was found within the SMA in 2020. To identify the effects of the urban growth on the temperature and wind variations in the future, WRF simulations by considering urban growth were performed for two seasons (summer and winter) in 2020s (2018~2022) and they were compared with those in the present (2003~2007). Comparisons of model results showed that significant changes in surface temperature (2-meter) were found in an area with high urban growth. On average in model domain, positive increases of $0.31^{\circ}C$ and $0.10^{\circ}C$ were predicted during summer and winter, respectively. These were higher than contributions forced by climate changes. The changes in surface temperature, however, were very small expect for some areas. This results suggested that surface temperature in metropolitan areas like the SMA can be significantly increased only by the urban growth during several decades.

• The Journal of Fisheries Business Administration
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• v.52 no.2
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• pp.55-67
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• 2021

Global warming affects critical natural resources, one of which is the oceans that occupy 70% of the total cover of the earth. In other words, ocean warming is a subset of global warming which needs to be addressed urgently. Purple laver (pyropia spp.) is one of the most vulnerable items to climate change although it is a major export product of Korean fisheries. The purpose of this study is to analyze the causality of how climate change caused by global warming affects the increase or decrease of PLP (purple laver production). The target area for analysis was set to Maro-hae between Jindo-gun and Haenam-gun. We selected marine environmental factors and meteorologic factors that could affect PLP as variables, as well as co-integration tests to determine long-term balance, and the Granger causticity tests. As a result, PLP and marine environmental factors WT (water temperature), pH, and DO confirmed that long-term equilibrium relationships were established, respectively. However, there is only causality with WT and it is confirmed that there is only a correlation between pH and DO (dissolved oxygen). There was no long-term equilibrium relationship between PLP and HDD (heating degree days) and there is a causal effect that HDD affects PLP; however, it was less clear than that of WT. The relationship between PLP and RF (rainfall), WS (wind speed), SS (percentage of sunshine), and FF (farm facilities) was all balanced in the long term, and causality exists. Based on the results of the analysis, policy proposals were made.