• Atmosphere
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• v.26 no.2
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• pp.243-256
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• 2016

Total number concentration of aerosols larger than 10 nm ($N_{CN10}$), 3 nm ($N_{CN3}$), and cloud condensation nuclei ($N_{CCN}$) were measured during four different ship cruises over the Yellow Sea. Average values of $N_{CN10}$ and $N_{CCN}$ at 0.6% supersaturation were 6914 and $3353cm^{-3}$, respectively, and the minimum value of $N_{CN10}$ was $2000cm^{-3}$, suggesting significant anthropogenic influence even at relatively clean marine environment. Although $N_{CN10}$ and $N_{CN3}$ increased near the coast due to anthropogenic influence, $N_{CCN}$ was relatively constant and therefore $N_{CCN}/N_{CN10}$ ratio tended to decrease, suggesting that coastal aerosols were relatively less hygroscopic. In general $N_{CN10}$, $N_{CN3}$, and $N_{CCN}$ during the cruises seemed to be significantly influenced by wet scavenging effects (e.g. fog) and boundary layer height variation. Only one new particle formation (NPF) event was observed during the measurement period. Interestingly, the NPF event occurred during a dust storm event and spatial scale of the NPF event was estimated to be larger than 100 km. These results demonstrate that aerosol and CCN concentration over the Yellow Sea can vary due to various different factors.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.333-342
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• 2012

We studied the characteristics of spatial distribution of global wave height and carried out the modelsensitivity test by changing the input field, model resolution and physical factor (effective wind factor) since the spatial and temporal resolution in wind wave forecasting is one of most important factors. Comparisons among the different cases, and also between model, buoy and satellite data have been made. As a results of the wind-wave model run using the high resolution wind field, the bias of significant wave height showed the positive tendency and the Root-Mean Square Error(RMSE) was a bit decreased based on the comparison with buoy data. When the model resolution was changed to higher, the bias and RMSE was increased, and as the effective wind factor was smaller than default value(= 1.4) the bias and RMSE showed also decreasing pattern.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.1-12
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• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• Journal of the Korean Society of Tobacco Science
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• v.29 no.1
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• pp.1-7
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• 2007

This study was conducted in order to predict the chemical and physical properties by climatic factors during the growing season of flue-cured tobacco as soon as possible. The data of eight chemical and five physical properties were collected from "Analysis of physical and chemical properties on farm leaf tobacco" conducted at KT&G Central Research Institute from 1987 through 2006. Data of climatic factors from April to July in 10 districts were collected from Korea Meteorological Adminstration. Except for yellowness(b), all probabilities of linear regression equations between the climatic factors(X) and the average contents of twelve grades(whole plant) for chemical and physical properties(Y) were significant($P{\leq}0.05$). The predicable probabilities within ${\pm}20%$ range of difference were 100% in ether extract content, in nicotine content, and in filling value, 90% in total nitrogen content, and 70% in total sugar content. These results suggest that the regression equations may be useful to predict the average content of twelve grades for eight chemical and four physical properties by climatic factors during the growing season of flue-cured tobacco at the beginning of August.

• Journal of the Korean Society of Tobacco Science
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• v.29 no.1
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• pp.8-13
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• 2007

This study was conducted in order to predict the chemical and physical properties by climatic factors during the growing season of burley tobacco as soon as possible. The data of six chemical and five physical properties were collected from "Analysis of chemical and physical properties on farm leaf tobacco" conducted at KT&G Central Research Institute from 1987 to 2006. Data of climatic factors from April to July in 6 districts were collected from Korea Meteorological Adminstration. Except for total nitrogen, total nitrogen/nicotine and yellowness(b), all probabilities of linear regression equations between the climatic factors(X) and the average contents of twelve grades(whole plant) for chemical and physical properties(Y) were significant($P{\leq}0.05$). The predicable probabilities within ${\pm}20%$ range of difference were 100% in ether extract content, 95% in nicotine content, and 90% in filling value. These results suggest that the regression equations may be useful to predict the average content of twelve grades for four chemical and four physical properties by climatic factors during the growing season of burley tobacco at the beginning of August.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.145-156
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• 2021

Forest fires have frequently occurred around the world, and the damages are increasing. In Korea, most forest fires are initiated by human activities, but climate factors such as temperature, humidity, and wind speed have a great impact on combustion environment of forest fires. In this study, therefore, based on statistics of forest fires in Gyeonggi-do over the past five years, meteorological and hydrological factors (i.e., temperature, humidity, wind speed, precipitation, and drought) were selected in order to quantitatively investigate causal relationships with forest fire. We applied a partial least squares structural equation model (PLS-SEM), which is suitable for analyzing causality and predicting latent variables. The overall results indicated that the measurement and structural models of the PLS-SEM were statistically significant for all evaluation criteria, and meteorological factors such as humidity, temperature, and wind speed affected by amount of -0.42, 0.23 and 0.15 of standardized path coefficient, respectively, on forest fires, whereas hydrological factor such as drought had an effect of 0.23 on forest fires. Therefore, as a practical method, the suggested model can be used for analyzing and evaluating influencing factors of forest fire and also for planning response and preparation of forest fire disasters.

• Asian Journal of Atmospheric Environment
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• v.12 no.3
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• pp.244-254
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• 2018

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard ($50{\mu}g/m^3$) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60-80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average $PM_{2.5}$ concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.

• New & Renewable Energy
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• v.20 no.1
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• pp.79-87
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• 2024

This study introduces a method for estimating Aerosol Optical Depth (AOD) using Broadband Aerosol Optical Depth (BAOD) derived from direct normal irradiance and meteorological factors observed between 2016 and 2017. Through correlation analyses between BAOD and atmospheric components such as Rayleigh scattering, water vapor, and tropospheric nitrogen dioxide, significant relationships were identified, enabling accurate AOD estimation. The methodology demonstrated high correlation coefficients and low Root Mean Square Errors (RMSE) compared to actual AOD₅₀₀ measurements, indicating that the attenuation effects of water vapor and the direct impact of tropospheric nitrogen dioxide concentration are crucial for precise aerosol optical depth estimation. The application of BAOD for estimating AOD₅₀₀ across various time scales-hourly, daily, and monthly-showed the approach's robustness in understanding aerosol distributions and their optical properties, with a high coefficient of determination (0.96) for monthly average AOD₅₀₀ estimates. This study simplifies the aerosol monitoring process and enhances the accuracy and reliability of AOD estimations, offering valuable insights into aerosol research and its implications for climate modeling and air quality assessment. The findings underscore the viability of using BAOD as a surrogate for direct AOD₅₀₀ measurements, presenting a promising avenue for more accessible and accurate aerosol monitoring practices, crucial for improving our understanding of aerosol dynamics and their environmental impacts.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.599-605
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• 2019

Rice yield (kg 10a^-1) in South Korea was estimated by meteorological variables that are influential factors in crop growth. This study investigated the possibility of anticipating the rice yield variability using a simple but an efficient statistical method, a multiple linear regression analysis, on the basis of the annual variation of meteorological variables. Due to heterogeneous environmental conditions by region, the yearly rice yield was assessed and validated for each province in South Korea. The monthly mean meteorological data for the period 1986-2018 (33 years) from 61 weather stations provided by Korean Meteorological Administration was used as the independent variable in the regression analysis. An 11-fold (leave-three-out) cross-validation was performed to check the accuracy of this method estimating rice yield at each province. This result demonstrated that temporal variation of rice yield by province in South Korea can be properly estimated using such concise procedure in terms of correlation coefficient (0.7, not significant). Furthermore, the estimated rice yield well captured spatial features of observation with mean bias of 0.7 kg 10a^-1 (0.15%). This method may offer useful information on rice yield by province in advance as long as accurate agro-meteorological forecasts are timely obtained from climate models.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.177-182
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• 2024

A consumer's decisions are made by affection of product. Affection has types: evaluation, mood, emotion and sensibility that means unconscious changes. Previous researches have clarified weather factors affect to sensibility that means weather factors may have causal effects to consumer's decision making. This research utilize weather information from KMA(Korea Meteorological Administration) and SNS geographical information and text to make weather sensibility model, and clarify the model shows significant change to online shop customer's purchase behavior(purchase frequency) by merging customer's address information and geometric information of the model for apply weather model. As a result, a model utilize daily precipitation, sunshine hours, average ground temperature, and average relative humidity makes significant result to e-commerce purchase behavior frequency.