PURPOSES : Due to the frequent occurrence of accidents on icy roads during nighttime, it would be advantageous to notify road managers and drivers about the most perilous areas. This would allow road managers to treat the icy roads with de-icing chemicals and enable drivers to be better prepared for potential hazards. Essential information about pavement temperature is required to identify icy spots on the road. METHODS : With the goal of estimating nighttime pavement temperature on the National Highways in Korea using atmospheric data, the current study investigated a widely recognized forecasting method known as deep neural network (DNN). To achieve this objective, the input data for the models were gathered from the weather agency's website. The dataset comprised of relative humidity, air temperature, dew point temperature, as well as the differences in air temperature and humidity between two consecutive days. RESULTS : In order to assess the effectiveness of the built DNN model, a comparison was made using baseline pavement temperature data gathered through an infrared-based pavement temperature sensor installed in a highway patrol car. The results indicated that the DNN model achieved a mean absolute error (MAE) of 0.42 and a root mean square error (RMSE) of 0.62. In comparison, a conventional regression model yielded an MAE of 2.07 and an RMSE of 2.64. Thus, the DNN model demonstrated superior performance in comparison to the conventional regression model. CONCLUSIONS : Considering the increasing focus on preventive maintenance, these newly developed prediction models can be implemented proactively as a preventive measure against icing. This proactive approach has the potential to significantly improve traffic safety on winter roads.

By providing an environment where energetic particles and micrometeorites can not penetrate, caves on Mars may serve as a human shelter in future Mars explorations. More than 1,000 cave entrance candidates have been detected; however, their physical characteristics that can be utilized in detecting more candidates have not been explored in detail. In this paper, we explore the nighttime temperature of 100 cave entrance candidates and their surrounding areas to investigate 1) the nighttime temperature tendencies relative to their surrounding areas and 2) the extent of these temperature differences. We find that 79% of the cave entrance candidates exhibit higher temperatures than the surrounding areas, and 59% show a temperature difference over 20K, suggesting that the cave entrances may generally show higher temperatures than the surrounding areas during the nighttime.

In this study, we investigated the effects of Upo-swamp upon local thermal environment with nighttime cooling rate. To do this, we set up the AWS(Automatic Weather observation System) over the central part of Upo-swamp on the early October 2007. We conducted the study by comparing the AWS data with another weather data observed by several meteorological observations of the Korea Meteorological Administration located at the vicinity of Upo-swamp for one year. The air temperature of Upo-swamp was higher than that of the surrounding in cold-climate season. But it was opposite in warm-climate season. We confirmed that Upo-swamp roles to mitigate the daily and annual air temperature ranges. And the daily air temperature variation of Upo-swamp lagged behind the land one. This phenomenon represent that the heat reservoir capacity of Upo-swamp is much larger than that of the ground.