PURPOSES: This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS: Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS: Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22nd December, 2020 and 29th January, and 25th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.

Land cover around weather station can affect various climatic elements such as temperature, precipitation, wind and moisture. This study tries to reveal patterns and causes in land cover changes around weather stations in South Korea and to classify types of the land cover changes. The agricultural land, forest and used area have occupied most areas around un-relocated 47 weather stations with enough weather observation durations for the past 20-30 years. Area of the agricultural land shows steady decrease with remarkable areal decrease in the wetland, while steady areal increase is found in the used area. Due to urban expansions and new town developments around the weather stations, areas of the urbanization type (used area+barren) and suburbanization type (agricultural land+forest+grass+wetland) show steady increase and decrease, respectively. On the other hand, accurate changes in land cover are not reflected in the water-front type (wetland+water) due to errors and limitations in land cover classification using satellite images. It is expected that the results in this study can contribute to reliability and homogeneity enhancement of weather data.

본 연구에서는 유사한 기상패턴이 유사한 재해피해를 가져오는가를 분석한다. 자연재해와 관련한 수많은 의사결정은 과연 자연재해를 가져올 가능성이 있는 다가오는 기상패턴이 과거 어떤 정도의 피해를 가져왔는가에 의존한다. 그리고 이러한 의사결정 방식은 기상패턴과 자연재해 피해 사이에 일정한 상관관계가 존재한다는 전제이 의존한다. 그러나 만약 기상패턴과 자연재해 사이에 뚜렷한 상관관계가 존재하지 않는다면 이러한 의사결정방식은 잘못된 것이다. 본 연구에서는 2000년 이후 기상청 AWS, ASOS 기상관측자료와 소방방재청 재해연보 자료를 이용하여 유사한 기상패턴이 유사한 자연재해 피해를 가져오는가를 분석하여 기상상황과 자연재해에 관한 기존의 전제를 정당성을 검토한다.

The characteristic features of surface ozone concentration and the forecasting procedure of high ozone days have been studied. The ozone concentration was continuously measured during 3 years (1997∼1999) at air quality monitoring stations in five major cities in Korea. The diurnal variation of surface ozone concentration on high ozone days is characterized by low ozone concentration at night. The ozone concentration increases continuously after sunrise, to reach a peak at 1500∼1600 LST. Thereafter it decreases steadily to a low concentration at sunset. The diurnal and annual maximum of the surface ozone concentration at Seoul were observed in May and June, respectively. The favorable synoptic condition for the high ozone day is divided into 4 different synoptic weather patterns: a high-pressure system from the Sea of Okhotsk, the Pacific subtropical high extending westward, a moving high-pressure system covering the Korean peninsula, and a synoptic system in front of a typhoon. Most of high ozone days occur under the high pressure system in Korea.

This study was carried out to investigate the characteristics of acidity in the precipitation and weather patterns that were influenced it at Kimhae area from March, 1992 to June, 1994. The range of pH value in the precipitation at Kimhae is 3.45 to 6.80 and the average is pH 4.62, and the major anion components associated with acidity in the precipitation are Cl-, SO42- , NO3-. These distributions are to be expected the influence of industrialization such as, urbanization and construction of industrial complex at Kimhae area and the long range transporting of air pollutants from China. The weather patterns governing the acid rain at Kimhae were classified broadly into four types(Cyclone(type I-a, type I-b), Migratory Anticyclone(type Ⅱ), Tropical Cyclone(type Ⅲ), Siberia High(type Ⅳ)) and weather pattern which had the most occurrence frequency of acid rain was type I-a and the average pH value of precipitation in this pattern was 4.45, and we are found that the source area of air mass which was accompanied with high acidic precipitation in Kimhae was the central China include with Peking through the analysis of surface weather maps, 850 hPa wind fields, and the streamline analyses.