• Atmosphere
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• v.19 no.4
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• pp.297-307
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• 2009

The synoptic structures and the dynamic and thermodynamic characteristics of Changma in 2007 are investigated using the ECMWF analysis data and the radiosonde data from KEOP-2007 IOP. The enhancement of the North-Pacific High into the Korean peninsula and the retreat of the Okhotsk High are shown during the onset of Changma and the change of wind component from southwesterly to northwesterly is appeared during the end of Changma. The baroclinic atmosphere is dominant during Changma at most regions over the Korean peninsula except at Gosan and Sokcho. The quasi-barotropic atmosphere is induced at Gosan by warm air mass and Sokcho by cold air mass. Precipitation in the Korean peninsula occurs when dynamic instability is strengthened as the baroclinic and qusi-barotropic structure is weakened. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in Changma. The first EOF explains the onset of Changma. The second EOF is related to the discrimination for existence and nonexistence of precipitation during Changma period according to the alternation of equivalent potential temperature between middle and lower atmosphere.

• Journal of Environmental Health Sciences
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• v.42 no.2
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• pp.92-101
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• 2016

Objectives: Outdoor workers are exposed to thermally stressful work environments. In this study, heat stress indices for harbor workers in summer were calculated to evaluate thermal comfort based on a human heat balance model. These indices are Physiological Subjective Temperature (PST), Dehydration Risk (DhR), and Overheating Risk (OhR) according to respective stage of cargo work in a harbor. In addition, we constructed a forecast system to provide heat stress information. Methods: Thermophysiological indices in this study were calculated using the MENEX model (i.e. the human heat balance model), which used as inputs the meteorological parameters, clothing insulation, and metabolic rate for each stage of cargo work in the harbor of Masan over the course of seven days, including a four-day heat wave. The forecast heat stress information constructed for Masan harbor was based on meteorological data supported by the Dong-Nae Forecast from the KMA (Korea Metrological Administration) and other input parameters. Results: According to higher metabolic rate, thermophysiological indices showed a critical level. In particular, PST was evaluated as reaching the 'Very hot' or 'Hot' level during all seven days, despite the heat occurring over only four. It is important in a regard to consider the work environment conditions (i.e. labor intensity and clothing in harbor). On a webpage, the forecast thermophysiological indices show as infographics to be easily understand. This webpage is comprised of indices for both current conditions and the forecast, with brief guidance. Conclusion: Thermophysiological indices show the risk level to health during a heat wave period. Heat stress information could help to protect the health of harbor workers. Further, this study could extend the applicability of these indices to a variety of outdoor workers in consideration of work environments.

• Fire Protection Technology
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• s.15
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• pp.11-21
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• 1993

This report is announced about thermal lag of painted heat detectors. Thermal lag is forecast result from painting the part of heat receiving. Test is simulated that heat detector is painted by synthetic resin emulsion paint according to the cricumstance of installation, corrosion, temperature, dust.

• Atmosphere
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• v.21 no.3
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• pp.257-271
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• 2011

The aim of this study is to examine weather modification by urbanization and human activities. The characteristics of the urban heat island (UHI) and precipitation in Seoul metropolitan area of Korea are investigated to demonstrate that cities can change or modify local and nearby weather and climate, and to confirm that cities can initiate convection, change the behavior of convective precipitation, and enhance downstream precipitation. The data used in this study are surface meteorological station data observed in Seoul and its nearby 5 cities for the period of 1960 to 2009, and 162 Automatic Weather System stations data observed in the Seoul metropolitan area from 1998 to 2009. Air temperature and precipitation amount tend to increase with time, and relative humidity decreases because of urbanization. Similar to previous studies for other cities, the average maximum UHI is weakest in summer and is strong in autumn and winter, and the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is enhanced for clear skies. Relatively warm regions extend in the east-west direction and relatively cold regions are located near the northern and southern mountains inside Seoul. The satellite cities in the outskirts of Seoul have been rapidly built up in recent years, thus exhibiting increases in near-surface air temperature. The yearly precipitation amount during the last 50 years is increased with time but rainy days are decreased. The heavy rainfall events of more than $20mm\;hr^{-1}$ increases with time. The substantial changes observed in precipitation in Seoul seem to be linked with the accelerated increase in the urban sprawl in recent decades which in turn has induced an intensification of the UHI effect and enhanced downstream precipitation. We also found that the frequency of intense rain showers has increased in Seoul metropolitan area.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.157-164
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• 2016

Solar power generation influenced by the weather. Using the weather forecast information, it is possible to predict the short-term solar power generation in the future. However, in limited circumstances such as islands or mountains, it can not be use weather forecast information by the disconnection of the network, it is impossible to use solar power generation prediction model using weather forecast. Therefore, in this paper, we propose a system that can predict the short-term solar power generation by using the information that can be collected by the system itself. We developed a short-term prediction model using the prior information of temperature and power generation amount to improve the accuracy of the prediction. We showed the usefulness of proposed prediction model by applying to actual solar power generation data.

• Journal of the Korean earth science society
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• v.35 no.5
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• pp.342-353
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• 2014

Data for model analysis derived from the finite volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4) and the Land Data Assimilation System (LDAS) have been utilized in a mesoscale model. These data are tested to provide initial conditions and lateral boundary forcings to the Purdue Mesoscale Model (PMM) for a case study of the Midwestern flood that took place from 21-23 May 1998. The simulated results with fvGCM and LDAS soil moisture and temperature data are compared with that of ECMWF reanalysis. The initial conditions of the land surface provided by fvGCM/LDAS show significant differences in both soil moisture and ground temperature when compared to ECMWF control run, which results in a much different atmospheric state in the Planetary Boundary Layer (PBL). The simulation result shows that significant changes to the forecasted weather system occur due to the surface initial conditions, especially for the precipitation and temperature over the land. In comparing precipitation, moisture budgets, and surface energy, not only do the intensity and the location of precipitation over the Midwestern U.S. coincide better when running fvGCM/LDAS, but also the temperature forecast agrees better when compared to ECMWF reanalysis data. However, the precipitation over the Rocky Mountains is too large due to the cumulus parameterization scheme used in the PMM. The RMS errors and biases of fvGCM/LDAS are smaller than the control run and show statistical significance supporting the conclusion that the use of LDAS improves the precipitation and temperature forecast in the case of the Midwestern flood. The same method can be applied to Korea and simulations will be carried out as more LDAS data becomes available.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.46.2-46.2
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• 2011

Planck is already in active operation, and in a few years a detailed CMB anisotropy map will be compiled, surpassing WMAP both in temperature and polarization. The E mode - E mode autocorrelation power spectrum at large scales contains weak but sizable information on the history of cosmic reionization. We show our latest advance of our own simulation of cosmic reionization that incorporates Pop III stars, and provide a forecast for Planck polarization measurement.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.1 no.1
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• pp.55-59
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• 1968

The relation of air temperature between early summer and early autumn from 1916 to 1966 was investigated. The data are brought by the statistical analysis for the purpose of the long range weather forecast. he results are summarized as follows : 1. The air temperature in early autumn at Mokpo is largely influenced by that of early summer. That is, when the air temperature in early summer is higher than the average, the temperature in early autumn has the possibility of being higher temperature in early autumn than average, the possibility being as much as $60\%$. On the contrary, when the former is lower, the latter has a possibility of becoming $74\%$ below the normal year. 2. The monthly ranges of forcastable mean air temperature in early autumn will be computed by the types of total variation in early summer and the standard deviation in early autumn.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.