• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.391-396
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• 2005

한국 연안 전 해역에 걸쳐서 매년 적조생물이 발생 한다. (1995년부터). 남해 중부 및 동부해역은 상습발생 지역이다 (7, 8월). 기상인자(기온, 수온, 강수량, 일사량, 일조시수, 바람)는 적조형성에 기여하며, 특히 수온 (기온)은 적조발생의 제한 인자로 작용한다. 수온 15$^{\circ}$C가 되는 일을 기점으로 적조발생에 소요되는 시간은 78${\sim}$104일 정도 걸리며, 누적일조시수, 누적수온, 누적강우량의 비교로부터 적조발생 해역을 구분할 수 있다. 즉, 남해중부 및 남해동부 해역은 고밀도 적조발생 해역이며 남해서부 해역, 동해남부 해역은 저밀도 적조발생 해역이다. 동해남부 해역을 제외한 나머지 해역은 수온 24.5${\sim}$25.5$^{\circ}$C의 범위에서 1000mg/l 이상의 밀도를 보이는데, 적조생물이 발생하면 대체로 수산피해를 가져다주는 적조경보의 범주에 든다. 위성원격탐사 기술로부터 우리나라 연근해 적조발생 해역 해황특성과 적조분포 상호간의 관계성으로부터 적조의 머무름과 이동은 냉수대의 발달 및 소멸 그리고 북상난류의 흐름과 밀접한 관계가 있음을 알 수 있었다. 또한 식물성플랑크톤의 농도의 변화를 이용하여 적조분포 해역의 감지가 위성위격탐사 기술로 가능하였다. GIS 기술을 통한 적조정보관리시스템의 구축으로 적조정보를 통한 공간분석이 가능하게 되었다.

• 한국기후변화학회지
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• 제5권1호
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• pp.47-59
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• 2014

본 연구에서는 주요 양념 채소류인 고추, 마늘, 양파의 생산량을 결정하는 재배면적 및 병해 발생양상의 변화를 기상요인과의 회귀분석을 통해 재배적지 추정 및 생산량 예측의 정확성을 기하는데 연구의 목적을 두었다. 고추에 있어 기온 및 강수량의 기상요인과 주요 병해인 역병과 탄저병의 발병율을 분석한 결과, 고추 정식기인 5월의 평균 기온이 $18.3^{\circ}C$ 이상이고, 7월의 평균 강수량이 532 mm 이상인 두 가지 조건을 만족했을 때 발병율이 50%를 넘었다. 고추 CMV와 BBWV2의 경우는 8월의 기상요인과 깊은 관련을 가지고 있는데, 기상요인별 발병율은 서로 반대되는 경향을 보였다. 마늘과 양파의 주요 병해인 흑색썩음균핵병의 발병율과 기상요인과의 관계를 보면, 마늘에서는 강수량보다는 기온이 더 높은 관계를 보였다. 구비대기인 4~5월의 평균기온이 $15.0{\sim}15.9^{\circ}C$ 사이에서 발병율이 가장 높았다. 양파에서는 11~1월의 평균기온이 $4.0^{\circ}C$ 이상이고, 3월 평균 강수량이 40 mm 이상일 경우, 흑색썩음균핵병 발병율이 증가하는 경향을 보였다. 주요 양념채소의 주요 병해 발생과 기상요인의 관련성을 회귀분석한 결과, 고추와 양파의 경우 재배적지가 중부지방으로 점차 확대될 것으로 추정된다.

• 농촌계획
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• 제27권2호
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• pp.69-76
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• 2021

This study focuses on the relationship between ginseng production per area and meteorological factors. Four areas of major ginseng production are considered in the study. Chungcheongnam-do and Gyengsangbuk-do are selected as the original major production places and Gyeonggi-do and Kangwon-do are selected as the new major places. The meteorological factors applied for study are the average temperature, accumulated precipitation, and integrated sunshine hours. With the data collected across four areas, we used a panel data analysis. From the results of Hausman test, the fixed effects model allowing to control individual area effect is preferable to the random effects model. Based on the results of the fixed effects model, the accumulated precipitation statistically and significantly affect the decreases in ginseng production. Changes in the average temperature negatively affect ginseng production, but the value is not statistically significant. The integrated sunshine positively affect ginseng production, but the value is not statistically significant.

• International Journal of Contents
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• 제18권1호
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• pp.17-26
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• 2022

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.

• Journal of Forest and Environmental Science
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• 제39권4호
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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.

• 한국환경과학회지
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• 제25권6호
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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.

• 한국농림기상학회지
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• 제23권4호
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• pp.295-305
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• 2021

본 연구에서는 서리발생과 관련된 기상요인을 선정하여 랜덤포레스트(RF)를 이용한 서리발생 유무 분류모형을 구축하였고, 이와 더불어 기상인자의 중요도와 데이터 세트를 구성하는 방법들을 비교하는 실험을 수행하였다. 그 결과, 서리발생에 대한 분류 모형을 구축할 경우에 데이터 세트의 양이 많더라도 모형 구축을 위해 학습하기 위한 데이터 세트에서 특정 값이 월등히 많은 불균형은 모형의 예측력에 좋지 못한 영향을 미치는 것으로 분석되었다. 또한, 이번 연구에서 수집된 25지역의 서리발생과 관련된 기상요인에 대해 지역별로 그룹화하여 중요도가 높은 기상요인을 반영한 모형 구축하는 것보다 하나의 통합된 모형을 구축하는 것이 더 효율적인 것으로 나타났다. 이번 연구를 통해 분석된 결과와 서리예측을 위한 기상요인에 대한 추가분석 연구를 수행한다면 정확도 높은 서리발생 예측모형을 구축할 수 있을 것이라 예상한다.

• 산업기술연구
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• 제35권
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• pp.23-30
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• 2015

In recent, the various methods to predict the hydrological impacts due to climate change have been developed and applied. Especially, the variability of the meteorological factors such as rainfall, temperature, and evaporation can impact on the ecosystem in a basin. The variability caused by climate change on the meteorological factors can be divided by a gradual and abrupt change. Therefore, in this study, the gradual change is detected by simple linear regression and Mann-Kendall trend test. Also, the abrupt change is detected by Bayesian change point analysis. Finally, the result using these methods can identify the meteorological threats in the Cheongmicheon basin.

• Journal of Korea Artificial Intelligence Association
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• 제2권2호
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• pp.9-14
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• 2024

Urban air quality significantly impacts life and health, with fine dust (PM10) and ultrafine dust (PM2.5) posing serious health risks. This study investigates the seasonal variations in fine dust concentrations based on meteorological data from 2022 and 2023, including temperature, humidity, and precipitation. A random forest regression model was utilized to analyze the relationship between fine dust levels and meteorological factors. The results revealed that fine dust concentrations were highest during spring and winter, while summer exhibited the lowest levels. This seasonal pattern is attributed to increased precipitation and higher temperatures, which help reduce airborne particulate matter. The findings underscore the predictive potential of meteorological data in estimating fine dust concentrations. This research provides a foundation for improving urban air quality management and developing public health strategies to mitigate the adverse effects of air pollution.

• 전기학회논문지
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• 제56권1호
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• pp.28-34
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• 2007

Recently, with the rapid growth of industry, environmental condition became worse. In addition to outdoor insulators in seashore are polluted due to salty wind. Also this pollution causes the flashover and failure of electric equipments. Especially the salt contaminant is one of the most representative pollutants, and known as the main source of the accident by contamination. As well known, the pollution has a close relation with meteorological factors such as wind velocity, wind direction, temperature, relative humidity, precipitation and so on. In this paper we have statistically analyzed the correlation between the pollution and the meteorological factors. The multiple regression analysis was used for the statistical analysis; daily measured equivalent salt deposit density(dependent variable) and the weather condition data(independent variable) were used. Also we have developed an expert program to predict the pollution deposit. A new prediction system using this program called SPPP(salt pollution prediction program) has been used to model accurately the relationship between ESDD with the meteorological factors.