Modeling Urban Temperature Using Measurements from Mobile and Stationary Monitoring Stations

Heat waves are occurring more frequently worldwide as global warming continues, and urban heat islands can threaten conventional life in cities. Measuring, analyzing, and simulating weather data at fine spatial and temporal scales are essential to prevent and reduce the damage caused by extreme heat waves. In urban environments, handling complex micrometeorological situations using current meteorological stations and global simulation models (e.g., weather research forecasting models) is challenging. In this study, the thermal environments of urban areas were measured using a mobile meteorological measurement platform. Both mobile and stationary datasets were incorporated into the meteorological modeling process to simulate the spatial and temporal distribution of temperature. Additionally, various mobile observation implementation scenarios for temperature modeling were examined. We compared simulation combinations with the temperature field generated from the total dataset to obtain a better sampling campaign and properly incorporate mobile data scenarios. When collecting mobile data, it is important to consider spatial features to improve the efficiency of sampling programs. This can substantially reduce the cost of mobile data collection, together with the sensor error bound.