• Ocean Science Journal
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• v.42 no.4
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• pp.211-222
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• 2007

We compare insolation results calculated from two well-known empirical formulas (Socket and Beaudry's SB73 formula and the original Smithsonian (SMS) formula) and a radiative transfer model using input data predicted from meteorological weather-forecast models, and review the accuracy of each method. Comparison of annual mean daily irradiance values for clear-sky conditions between the two formulas shows that, relative to the SMS, the SB73 underestimates spring values by 9 W $m^{-2}$ in the northern Adriatic Sea, although overall there is a good agreement between the annual results calculated with the two formulas. We also elucidate the effect on SMS of changing the 'Sun-Earth distance factor (f)', a parameter which is commonly assumed to be constant in the oceanographic context. Results show that the mean daily solar radiation for clear-sky conditions in the northern Adriatic Sea can be reduced as much as 12 W $m^{-2}$ during summer due to a decrease in the f value. Lastly, surface irradiance values calculated from a simple radiative transfer model (GM02) for clear-sky conditions are compared to those from SB73 and SMS. Comparison with iu situ data in the northern Adriatic Sea shows that the GM02 estimate gives more realistic surface irradiance values than SMS, particularly during summer. Additionally, irradiance values calculated by GM02 using the buoy meteorological fields and ECMWF (The European Centre for Medium Range Weather Forecasts) meteorological data show the suitability of the ECMWF data usage. Through tests of GM02 sensitivity to key regional meteorological factors, we explore the main factors contributing significantly to a reduction in summertime solar irradiance in the Adriatic Sea.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.223-236
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• 2020

Purpose: In this study, a strong wind damage prediction function was developed in order to be used as a contingency during disaster management (preventive-preventive-response-recovery). Method: The predicted strong wind damage function proposed in this study took into account the re-enactment boy power, weather data and local characteristics at the time of damage. The meteorological data utilized the wind speed, temperature, and damage history observed by the Korea Meteorological Administration, the disaster year, and the recovery costs, population, vinyl house area, and farm water contained in the disaster report as factors to reflect the regional characteristics. Result: The function developed in this study reflected the predicted weather factors and local characteristics based on the history of strong wind damage in the past, and the extent of damage can be predicted in a short time. Conclusion: Strong wind damage prediction functions developed in this study are believed to be available for effective disaster management, such as decision making by policy-makers, deployment of emergency personnel and disaster prevention resources.

• Journal of Environmental Impact Assessment
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• v.15 no.4
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• pp.237-247
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• 2006

In case of studied area located around the sea, the data measured from the regional meteorological office is highly different from the local weather data because the diffusivity of fugitive dust varies considerably with meteorological conditions. Especially, it is very difficult to predict the amount of fugitive dust accurately as wind speed remains high frequently. In this study, the fluxes of suspended particulates as a function of the friction velocity were applied to consider the effect of wind speed on the amount of fugitive dust generated from the reclamation site. The amount of fugitive dust estimated as mentioned above was simulated by using ISCST3 model. As a result, in case of using only the Fugitive Dust Formula which is usually used in Environment Impact Assessment, the predicted $PM_{10}$ concentrations with points were $43.4{\sim}67.8{\mu}g/m^3$. However, in case of applying to the flux of suspended particulates, the predicted values of $PM_{10}$ with points were $43.3{\sim}69.1{\mu}g/m^3$, $49.5{\sim}90.4{\mu}g/m^3$ and $76.0{\sim}182.6{\mu}g/m^3$ with the wind speeds of 4.4, 5.8 and 7.7m/s, respectively. It could be possible to predict the amount of fugitive dust accurately because these predicted values were similar to the measured values. Consequently, we can establish alternatives for reduction of fugitive dust in this area damaged by fugitive dust which is caused by wind.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.79-89
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• 1997

In order to reduce the outbreaks of short-term high concentrations and its impacts, we developed the models which predicted tomorrow's maximum hourly concentrations of $O_3$, TSP, SO$_2$, NO$_2$ and CO. Statistical methods like multi regressions were used because it must be operated easily under the present conditions. 47 independent variables were used, which included observed concentrations of air pollutants, observed and forcasted meteorological data in 1994 at Seoul and its surrounding areas. We subdivided Seoul into 4 areas coinciding with the present ozone warning areas. 4 kinds of seasonal models were developed due to the seasonal variations of observed concentrations, and 2 kinds of data models for the unavailable case of forecasted meteorological data. By comparing the $R^2$and root mean square error(hearafter 'RMSE') of each model, we confirmed that the models including forecasted data showed higher accuracy than ones using observed only. It was also shown that the higher the seasonal mean concentrations, the larger the RMSE. There was no distinct difference between the results of 4 areal models. In case of test run using 1995's data, the models predicted well the trends of daily variation of concentrations and the days when the possibility of outbreak of high concentarion was high. This study showed that it was reasonable to use those models as operational ones, because the $R^2$ and RMSE of models were smaller than those of operational/research models such as in South Coast Air Basin, CA, USA.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.833-848
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• 2014

The assessment of the current and future climate change-induced potential wind energy is an important issue in the planning and operations of wind farm. Here, the authors analyze spatiotemporal characteristics and variabilities of wind energy over Korean Peninsula in the near future (2006-2040) using Representative Concentration Pathway(RCP) scenarios data. In this study, National Institute of Meteorological Research (NIMR) regional climate model HadGEM3-RA based RCP 2.6 and 8.5 scenarios are analyzed. The comparison between ERA-interim and HadGEM3-RA during the period of 1981-2005 indicates that the historical simulation of HadGEM3-RA slightly overestimates (underestimates) the wind energy over the land (ocean). It also shows that interannual and intraseasonal variability of hindcast data is generally larger than those of reanalysis data. The investigation of RCP scenarios based future wind energy presents that future wind energy density will increase over the land and decrease over the ocean. The increase in the wind energy and its variability is particularly significant over the mountains and coastal areas, such as Jeju island in future global warming. More detailed analysis presents that the changes in synoptic conditions over East Asia in future decades can influence on the predicted wind energy abovementioned. It is also suggested that the uncertainty of the predicted future wind energy may increase because of the increase of interannual and intra-annual variability. In conclusion, our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

• Asian Journal of Atmospheric Environment
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• v.5 no.1
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• pp.1-7
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• 2011

This study assesses the dispersion and emission rates of odor form industrial area source. CALPUFF and AERMOD Gaussian models were used for predicting downwind odor concentration and calculating odor emission rates. The studied region was Seobu industrial complex in Korea. Odor samples were collected five days over a year period in 2006. In-site meteorological data (wind direction and wind speed) were used to predict concentration. The BOOT statistical examination software was used to analyze the data. Comparison between the predicted and field sampled downwind concentration using BOOT analysis indicates that the CALPUFF model prediction is a little better than AERMOD prediction for average downwind odor concentrations. Predicted concentrations of AERMOD model have a little larger scatter than that of CALPUFF model. The results also show odor emission rates of Seobu industrial complex area were an order of 10 smaller than that of beef cattle feed lots.

• Atmosphere
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• v.31 no.2
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.283-284
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• 2019

• Structural Engineering and Mechanics
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• v.84 no.6
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• pp.831-849
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• 2022

One of the most prevalent causes of reinforced concrete (RC) structural deterioration is chloride-induced corrosion. This paper aims to provide a comprehensive insight into the environmental effect of RC's chloride ingress process. The first step is to investigate how relative humidity, temperature, and wind influence chloride ingress into concrete. The probability of initiation time of chloride-induced corrosion is predicted using a probabilistic model that considers these aspects. Parametric analysis is conducted on several factors impacting the corrosion process, including the depth of concrete cover, surface chloride concentration, relative humidity, and temperature to expose environmental features. According to the findings, environmental factors such as surface chloride concentration, relative humidity and temperature substantially impact on the time to corrosion initiation. The long- and short-distance impacts are also examined. The meteorological data from the National Meteorological Center of China are collected and used to analyze the environmental characteristics of the chloride ingress issue for structures along China's coastline. Finally, various recommendations are made for improving durability design against chloride attacks.