• Journal of the Korean earth science society
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• v.41 no.6
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• pp.575-587
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• 2020

Meteorological phenomena are observed by the Korea Meteorological Administration in a variety of ways (e.g., surface, upper-air, marine, ocean, and aviation). However, there are limits to the meteorological observation of the planetary boundary layer (PBL) that greatly affects human life. In particular, observations using a sonde or aircraft require significant observational costs in economic terms. Therefore, the goal of this study was to measure and analyze the meteorological factors of the vertical distribution of the see-land breeze among local meteorological phenomena using meteorological drones. To investigate the spatial distribution of the see-land breeze, a same integrated meteorological sensor was mounted on each drone at three different points (seaside, bottom of mountain, and mountainside), including the Boseong tall tower (BTT) at the Boseong Standard Weather Observatory (BSWO) in the Boseong region. Vertical profile observations for air temperature, relative humidity, wind direction, wind speed, and air pressure were conducted up to 400 m every 30 minutes from 1100 LST to 1800 LST on August 4, 2018. The spatial characteristics of meteorological phenomena for temperature, relative humidity, and atmospheric pressure were not shown at the four points. Strong winds (~8 m s-1) were observed from the midpoint (~100 m) at strong solar radiation hour, and in the afternoon the wind direction changed from the upper layer at the inland area to the west wind. It is expected that the analysis results of the lower atmospheric layer observed using the meteorological drone may help to improve the weather forecast more accurately.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.235-245
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• 2023

Forest fuel moisture content is a crucial factor influencing the combustion rate and fuel consumption during forest fires, significantly impacting the occurrence and spread of wildfires. In this study, meteorological data were gathered using a meteorological measuring device (HOBO data logger) installed in the south and north slopes of Kangwon National University Forest, as well as on bare land outside the forest, from November 1, 2021, to October 31, 2022. The objective was to analyze the relationship between meteorological data and fuel moisture content. Fuel moisture content from the ground cover on the south and north slopes was collected. Fallen leaves on the ground were utilized, with a focus on broad-leaved trees (Prunus serrulata, Quercus dentata, Quercus mongolica, and Castanea crenata) and coniferous trees (Pinus densiflora and Pinus koraiensis), categorized by species. Additionally, correlation analysis with fuel moisture content was conducted using temperature (average, maximum, and minimum), humidity (average, minimum), illuminance (average, maximum, and minimum), and wind speed (average, maximum, and minimum) data collected by meteorological measuring devices in the study area. The results indicated a significant correlation between meteorological factors such as temperature, humidity, illuminance, and wind speed, and the moisture content of fuels. Notably, exceptions were observed for the moisture content of the on the north slope and that of the ground cover of Prunus serrulata and Castanea crenata.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1261-1263
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• 2003

Based on twelve observational stations low meteorological visibility (LMV) data during November 2002 to April 2003, the spatial distribution of LMV over Hongkong area (113.8$^{\circ}$ E-114.4$^{\circ}$ E, 22.1$^{\circ}$ N-22.4$^{\circ}$ N) is studied, using a PCA method. Optical spectrum of NOAA-16 associated with LMV shows that the significant effect factors correlated with LMV in the leading mode are the difference or rate between the visible and near-IR channels and single visible channel. A successful retrieval of LMV is done and a regression equation with a multiple correlation coefficient of 0.67 is obtained.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• Journal of Environmental Health Sciences
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• v.42 no.1
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• pp.41-52
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• 2016

Objectives: Scrub typhus is one of the most prevalent vector-borne diseases. It is caused by Orientia tsutsugamushi, which is transmitted when people are bitten by infected chigger mites. This study aims at quantifying the association between the incidence of scrub typhus and meteorological factors in Jeollabuk-do Province over the period 2001-2015. Methods: Reported cases of scrub typhus were collected from the website of the Disease Web Statistical System supported by the Korea Centers for Disease Control and Prevention (KCDC). Simultaneous meteorological data, including temperature, rainfall, relative humidity, and sunshine duration were collected from the website of the National Climate Data Service System by the Korea Meteorological Administration. Correlation and regression analyses were applied to identify the association between the incidence of scrub typhus and meteorological factors. Results: The general epidemiological characteristics of scrub typhus in Jeollabuk-do Province were similar to those nationwide for sex, age, and geographical distribution. However, the annual incidence rate (i.e., cases per 100,000) of scrub typhus in Jeollabuk-do Province was approximately four times higher than all Korea's 0.9. The number of total cases was the highest proportion at 13.3% in Jeonbuk compared to other regions in Korea. The results of correlation analysis showed that there were significant correlations between annual cases of scrub typhus and monthly data for meteorological factors such as temperature and relative humidity in late spring and summer, especially in the case of temperature in May and June. The results of regression analysis showed that determining factors in the regression equation explaining the incidence of scrub typhus reached 46.2% and 43.5% in May and June. Using the regression equation, each 1oC rise in the monthly mean temperature in May or June may lead to an increase of 38 patients with scrub typhus compared to the annual mean of incidence cases in Jeollabuk-do Province. Conclusion: The result of our novel attempts provided rational evidence that meteorological factors are associated with the occurrence of scrub typhus in Jeollabuk-do. It should therefore be necessary to observe the trends and predict patterns of scrub typhus transmission in relation to global-scale climate change. Also, action is urgently needed in all areas, especially critical regions, toward taking steps to come up with preventive measures against scrub typhus transmission.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.839-851
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• 2016

The effect of weather on disease was investigated based on results reported in academic papers. Weather-sensitive disease was selected by analyzing the frequency distributions of diseases and correlations between diseases and meteorological factors (e.g., temperature, humidity, pressure, and wind speed). Correlations between disease and meteorological factors were most frequently reported for myocardial infarction (MI) (28%) followed by chronic ischemic heart disease (CHR) (12%), stroke (STR) (10%), and angina pectoris (ANG) (5%). These four diseases had significant correlations with temperature (meaningful correlation for MI and negative correlations for CHR, STR, and ANG). Selecting MI, as a representative weather-sensitive disease, and summarizing the quantitative correlations with meteorological factors revealed that, daily hospital admissions for MI increased approximately 1.7%-2.2% with each $1^{\circ}C$ decrease in physiologically equivalent temperature. On the days when MI occurred in three or more patients larger daily temperature ranges ($2.3^{\circ}C$ increase) were reported compared with the days when MI occurred in fewer than three patients. In addition, variations in pressure (10 mbar, 1016 mbar standard) and relative humidity (10%) contributed to an 11%-12% increase in deaths from MI and an approximately 10% increase in the incidence of MI, respectively.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.44-53
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• 1999

The statistical characteristics of the factors related to the daily rainfall prediction model are analyzed . Records of daily precipitation, mean air temperature, relative humidity , dew-point temperature and air pressure from 1973∼1998 at 8 meteorological sttions in south-western part of Korea were used. 1. Serial correlatino of daily precipitaiton was significant with the lag less than 1 day. But , that of other variables were large enough until 10 day lag. 2. Crosscorrelation of air temperature, relative humidity , dew-point temperature showed similar distribution wiht the basin contrours and the others were different. 3. There were significant correlation between the meteorological variables and precipitation preceded more than 2 days. 4. Daily preciption of each station were treated as a truncated continuous random variable and the annual periodic components, mean and standard deviation were estimated for each day. 5. All of the results could be considered to select the input variables of regression model or neural network model for the prediction of daily precipitation and to construct the stochastic model of daily precipitation.

• Journal of Environmental Health Sciences
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• v.46 no.3
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• pp.353-358
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• 2020

Objectives: An outbreak of pathogenic Escherichia coli food poisoning in Korea was first reported in 1998. They have continued to occure since then. This study was performed to describe the long-term trend in pathogenic E. coli food poisoning occurrences in Korea and examine the relation with climate factors. Methods: Official Korean statistics on food poisoning outbreaks and meteorological data for the period 2002-2017 were used. Pearson's correlation analysis was employed to establish the relationship between outbreaks of pathogenic E. coli food poisoning and meteorological factors. The influence of meteorological factors upon the outbreaks was analyzed by regression analysis. Results: During the study period, pathogenic E. coli food poisoning ranked second for the number of outbreaks (excluding unknowns) and first for the number of cases. Average temperature, the highest and lowest temperatures, precipitation, number of days with rainfall, and humidity all had a significant correlation with monthly number of outbreaks of pathogenic E. coli food poisoning (p<0.001). It was found that the lowest and highest temperatures and precipitation had a significant influence on the monthly number of outbreaks of food poisoning (p<0.001). These variables together explained 42.1% of the total variance, with the lowest temperature having the greatest explanatory power. Conclusion: These results show that food poisoning incidences may have been influenced by climate change, especially warming. The results also suggest that pathogenic E. coli infections are now an important public health issue in Korea since it is one of the countries where climate change is occurring rapidly.

• Journal of Climate Change Research
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• v.7 no.2
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• pp.193-203
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• 2016

The importance of forecasting agricultural production is receiving attention while climate change is accelerating. This study suggested three types of crop yield forecasting model for major vegetable crops by using downscaled meteorological information of main production area on farmland level, which identified as limitation from previous studies. First, this study conducted correlation analysis with seven types of farm level downscaled meteorological informations and reported crop yield of main production area. After, we selected three types of meteorological factors which showed the highest relation with each crop species and regions. Parameters were deducted from meterological factor with high correlation but crop species number was neglected. After, crop yield of each crops was estimated by using the three suggested types of models. Chinese cabbage showed high accuracy in overall, while the accuracy of daikon and onion was quiet revised by neglecting the outlier. Chili and garlic showed differences by region, but Kyungbuk chili and Chungnam, Kyungsang garlic appeared significant accuracy. We also selected key meteorological factor of each crops which has the highest relation with crop yield. If the factor had significant relation with the quantity, it explains better about the variations of key meteorological factor. This study will contribute to establishing the methodology of future studies by estimating the crop yield of different species by using farmland meterological information and relatively simplify multiple linear regression models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.1-14
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• 2021

Onions are a representative produce that requires supply-demand control measures due to large fluctuations in production and price by growing season. Accurate forecasts of crop production can improve the effectiveness of such measures. However, it is challenging to obtain accurate estimates of crop productivity for onions because they are mainly grown on the open fields. The objective of this study was to perform the empirical analysis of the relationship between factors for crop growth and meteorological conditions, which can support the development of models to predict crop growth and production. The growth survey data were collected from open fields. The survey data included the weight of above ground organs as well as that of the bulbs. The estimates of meteorological data were also compiled for the given fields. Correlation analysis between these factors was performed. The random forest was also used to compare the importance of the meteorological factors by the growth stage. Our results indicated that insolation in early March had a positive effect on the growth of the above-ground. There was a negative correlation between precipitation and the growth of the above-ground at the end of March although it has been suggested that drought can deter the growth of onion. The negative effects of precipitation and daylight hours on the growth of the above-ground and under-ground were significant during the harvest period. These meteorological factors identified by growth stage can be used to develop models for onion growth and production forecast.