• 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 association of regional geographers
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• v.23 no.1
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• pp.151-167
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• 2017

The main objective of this study was to investigate the climatic impact of $PM_{10}$ concentration on the temperature change pattern in Busan Metropolitan City(BMC), Korea during 2001~2015. Mean $PM_{10}$ concentration of BMC has gradually declined over the past 15 years. While the highest $PM_{10}$ concentration was observed in spring followed by winter, summer, and fall on average, the seasonal variations of $PM_{10}$ concentration differed from place to place within the city. Frequency analysis showed that the most frequently observed $PM_{10}$ concentration ranged from $20{\mu}g/m^3$ to $60{\mu}g/m^3$, which accounted for 64.6% of all daily observations. Overall, the west-high and east-low pattern of $PM_{10}$ concentration was relatively strong during the winter when the effect of yellow-dust events on the air quality was weak. Comparative analyses between $PM_{10}$ concentration and monthly temperature slope derived from generalized temperature curves indicated that the decreasing trend of $PM_{10}$ concentration was associated with increases of annual temperature range, and $PM_{10}$ concentration had a negative relationship with the temperature slope of warming months. Overall, $PM_{10}$ concentration had a weak correlation with the annual mean temperature, but it had a significant, positive correlation with the winter season, which had a dominant influence on the annual mean temperature. In terms of energy budget, it has been known that the change in $PM_{10}$ concentration contributes to the warming or cooling effect by affecting the radiative forcing due to the reflection and absorption of radiant energy. The correlation between $PM_{10}$ concentration and temperature changes in the study area was not seasonally and spatially consistent, and its significance was statistically limited partly due to the number of observations and the lack of potential socioeconomic factors relevant to urban air quality.